Search

Statically typed languages offer numerous benefits to developers, such as improved code quality and reduced runtime errors, but they also require the overhead of manual type annotations. To mitigate this burden, language designers have started incorporating support for type inference, where the compiler infers the type of a variable based on its declaration/usage context. As a result, type annotations are optional in certain contexts, and developers are empowered to use type inference in these situations. However, the usage patterns of type annotations in languages that support type inference are unclear. These patterns can help provide evidence for further research in program comprehension, in language design, and for education. We conduct a large-scale empirical study using Boa, a tool for mining software repositories, to investigate when and where developers use type inference in 498,963 Kotlin projects. We choose Kotlin because it is the default language for Android development, one of the largest software marketplaces. Additionally, Kotlin has supported declaration-site optional type annotations from its initial release. Our findings reveal that type inference is frequently employed for local variables and variables initialized with method calls declared outside the file are more likely to use type inference. These results have significant implications for language designers, providing valuable insight into where to allow type inference and how to optimize type inference algorithms for maximum efficiency, ultimately improving the development experience for developers.

Symbolic Regression remains an NP-Hard problem, with extensive research focusing on AI models for this task. Transformer models have shown promise in Symbolic Regression, but performance suffers with smaller datasets. We propose applying k-fold cross-validation to a transformer-based symbolic regression model trained on a significantly reduced dataset (15,000 data points, down from 500,000). This technique partitions the training data into multiple subsets (folds), iteratively training on some while validating on others. Our aim is to provide an estimate of model generalization and mitigate overfitting issues associated with smaller datasets. Results show that this process improves the model's output consistency and generalization by a relative improvement in validation loss of 53.31%. Potentially enabling more efficient and accessible symbolic regression in resource-constrained environments.

We explore how light-driven optical phonons can be used to drive magnetic exchange interactions into interesting physical regimes by developing a general theory of spin-phonon pumping in magnetic insulators with non-equilibrium optical phonon distributions, focusing on the diabatic regime where phonon frequencies are much larger than the magnetic interactions. We present several applications of spin-phonon pumping two-dimensional nearest-neighbor Heisenberg, XYZ and Kitaev models to examine what kind of further neighbor interactions and chiral fields can be generated, and how anisotropic couplings can be enhanced, showing that experimentally accessible non-equilibrium phonon distributions can generically drive significant frustration and realize a variety of spin liquid regimes. This effect is described for both direct and superexchange mechanisms, and we derive simple geometric rules for which phonon modes are "spin-phonon'' active and for which magnetic interactions. Spin-phonon pumping provides an intriguing possibility for preferentially pumping specific magnetic interaction terms. In addition to generating further neighbor interactions, such pumping can lead to increased magnetic anisotropy for initially weakly anisotropic models, and selectively pumping the Kitaev-Heisenberg model can suppress undesirable Heisenberg terms while enhancing Kitaev interactions., Comment: 16 pages, 10 figures

Social conventions are the foundation for social and economic life. As legions of AI agents increasingly interact with each other and with humans, their ability to form shared conventions will determine how effectively they will coordinate behaviors, integrate into society and influence it. Here, we investigate the dynamics of conventions within populations of Large Language Model (LLM) agents using simulated interactions. First, we show that globally accepted social conventions can spontaneously arise from local interactions between communicating LLMs. Second, we demonstrate how strong collective biases can emerge during this process, even when individual agents appear to be unbiased. Third, we examine how minority groups of committed LLMs can drive social change by establishing new social conventions. We show that once these minority groups reach a critical size, they can consistently overturn established behaviors. In all cases, contrasting the experimental results with predictions from a minimal multi-agent model allows us to isolate the specific role of LLM agents. Our results clarify how AI systems can autonomously develop norms without explicit programming and have implications for designing AI systems that align with human values and societal goals.

Imitation learning (IL) has shown great potential in various applications, such as robot control. However, traditional IL methods are usually designed to learn only one specific type of behavior since demonstrations typically correspond to a single expert. In this work, we introduce the first generic framework for Quality Diversity Imitation Learning (QD-IL), which enables the agent to learn a broad range of skills from limited demonstrations. Our framework integrates the principles of quality diversity with adversarial imitation learning (AIL) methods, and can potentially improve any inverse reinforcement learning (IRL) method. Empirically, our framework significantly improves the QD performance of GAIL and VAIL on the challenging continuous control tasks derived from Mujoco environments. Moreover, our method even achieves 2x expert performance in the most challenging Humanoid environment., Comment: 22 pages, conference paper

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.

Reinforcement Learning (RL) has made promising progress in planning and decision-making for Autonomous Vehicles (AVs) in simple driving scenarios. However, existing RL algorithms for AVs fail to learn critical driving skills in complex urban scenarios. First, urban driving scenarios require AVs to handle multiple driving tasks of which conventional RL algorithms are incapable. Second, the presence of other vehicles in urban scenarios results in a dynamically changing environment, which challenges RL algorithms to plan the action and trajectory of the AV. In this work, we propose an action and trajectory planner using Hierarchical Reinforcement Learning (atHRL) method, which models the agent behavior in a hierarchical model by using the perception of the lidar and birdeye view. The proposed atHRL method learns to make decisions about the agent's future trajectory and computes target waypoints under continuous settings based on a hierarchical DDPG algorithm. The waypoints planned by the atHRL model are then sent to a low-level controller to generate the steering and throttle commands required for the vehicle maneuver. We empirically verify the efficacy of atHRL through extensive experiments in complex urban driving scenarios that compose multiple tasks with the presence of other vehicles in the CARLA simulator. The experimental results suggest a significant performance improvement compared to the state-of-the-art RL methods., Comment: ICML Workshop on New Frontiers in Learning, Control, and Dynamical Systems

The hidden order phase of URu$_2$Si$_2$ has eluded identification for over thirty-five years. A compelling proposal that explains the Ising heavy-fermion nature of the material is \emph{hastatic order}: a symmetry breaking heavy-Fermi liquid arising from a spinorial microscopic hybridization. The original hastatic proposal cannot microscopically model the pressure induced antiferromagnetic phase; and while it predicts a spinorial order parameter, it does not provide any detectable signatures of the spinorial nature. Here, we present a more realistic microscopic model of hastatic order in URu$_2$Si$_2$ based on two conducting electron bands and explore its phase diagram in detail. Our model non-trivially preserves the Ising heavy-fermion physics of the original, while allowing us to tune between the antiferromagnet and hidden order using pressure analogues and magnetic field. Our model is also consistent with recent phenomenological predictions of not one, but two vector order parameters associated with the spinorial order that provide the first microscopic predictions for detecting the spinorial nature., Comment: 30 pages, 20 figures

This paper presents a new solution to address the challenge of increasing memory usage in high-performance computing simulations of Lattice-Bolzmann or Finite-Volume schemes.Our approach utilises a lossy compression scheme based on the Discrete Wavelet Transform (DWT) to achieve high compression ratios while preserving the accuracy of the simulation.Our evaluation on two different FV/LBM schemes demonstrates that the approach can reduce memory usage by several orders of magnitude.

Federated Reinforcement Learning (FedRL) encourages distributed agents to learn collectively from each other's experience to improve their performance without exchanging their raw trajectories. The existing work on FedRL assumes that all participating agents are homogeneous, which requires all agents to share the same policy parameterization (e.g., network architectures and training configurations). However, in real-world applications, agents are often in disagreement about the architecture and the parameters, possibly also because of disparate computational budgets. Because homogeneity is not given in practice, we introduce the problem setting of Federated Reinforcement Learning with Heterogeneous And bLack-box agEnts (FedRL-HALE). We present the unique challenges this new setting poses and propose the Federated Heterogeneous Q-Learning (FedHQL) algorithm that principally addresses these challenges. We empirically demonstrate the efficacy of FedHQL in boosting the sample efficiency of heterogeneous agents with distinct policy parameterization using standard RL tasks., Comment: Preprint. Under review

Many software engineering research papers rely on time-based data (e.g., commit timestamps, issue report creation/update/close dates, release dates). Like most real-world data however, time-based data is often dirty. To date, there are no studies that quantify how frequently such data is used by the software engineering research community, or investigate sources of and quantify how often such data is dirty. Depending on the research task and method used, including such dirty data could affect the research results. This paper presents an extended survey of papers that utilize time-based data, published in the Mining Software Repositories (MSR) conference series. Out of the 754 technical track and data papers published in MSR 2004--2021, we saw at least 290 (38%) papers utilized time-based data. We also observed that most time-based data used in research papers comes in the form of Git commits, often from GitHub. Based on those results, we then used the Boa and Software Heritage infrastructures to help identify and quantify several sources of dirty Git timestamp data. Finally we provide guidelines/best practices for researchers utilizing time-based data from Git repositories., Comment: Accepted for publication in Empirical Software Engineering (EMSE). arXiv admin note: substantial text overlap with arXiv:2103.11339

Major Depressive Disorder (MDD) is a severe illness that affects millions of people, and it is critical to diagnose this disorder as early as possible. Detecting depression from voice signals can be of great help to physicians and can be done without any invasive procedure. Since relevant labelled data are scarce, we propose a modified Instance Discriminative Learning (IDL) method, an unsupervised pre-training technique, to extract augment-invariant and instance-spread-out embeddings. In terms of learning augment-invariant embeddings, various data augmentation methods for speech are investigated, and time-masking yields the best performance. To learn instance-spread-out embeddings, we explore methods for sampling instances for a training batch (distinct speaker-based and random sampling). It is found that the distinct speaker-based sampling provides better performance than the random one, and we hypothesize that this result is because relevant speaker information is preserved in the embedding. Additionally, we propose a novel sampling strategy, Pseudo Instance-based Sampling (PIS), based on clustering algorithms, to enhance spread-out characteristics of the embeddings. Experiments are conducted with DepAudioNet on DAIC-WOZ (English) and CONVERGE (Mandarin) datasets, and statistically significant improvements, with p-value 0.0015 and 0.05, respectively, are observed using PIS in the detection of MDD relative to the baseline without pre-training.

Preserving a patient's identity is a challenge for automatic, speech-based diagnosis of mental health disorders. In this paper, we address this issue by proposing adversarial disentanglement of depression characteristics and speaker identity. The model used for depression classification is trained in a speaker-identity-invariant manner by minimizing depression prediction loss and maximizing speaker prediction loss during training. The effectiveness of the proposed method is demonstrated on two datasets - DAIC-WOZ (English) and CONVERGE (Mandarin), with three feature sets (Mel-spectrograms, raw-audio signals, and the last-hidden-state of Wav2vec2.0), using a modified DepAudioNet model. With adversarial training, depression classification improves for every feature when compared to the baseline. Wav2vec2.0 features with adversarial learning resulted in the best performance (F1-score of 69.2% for DAIC-WOZ and 91.5% for CONVERGE). Analysis of the class-separability measure (J-ratio) of the hidden states of the DepAudioNet model shows that when adversarial learning is applied, the backend model loses some speaker-discriminability while it improves depression-discriminability. These results indicate that there are some components of speaker identity that may not be useful for depression detection and minimizing their effects provides a more accurate diagnosis of the underlying disorder and can safeguard a speaker's identity., Comment: Accepted to Interspeech 2022

Recent works on neural contextual bandits have achieved compelling performances due to their ability to leverage the strong representation power of neural networks (NNs) for reward prediction. Many applications of contextual bandits involve multiple agents who collaborate without sharing raw observations, thus giving rise to the setting of federated contextual bandits. Existing works on federated contextual bandits rely on linear or kernelized bandits, which may fall short when modeling complex real-world reward functions. So, this paper introduces the federated neural-upper confidence bound (FN-UCB) algorithm. To better exploit the federated setting, FN-UCB adopts a weighted combination of two UCBs: $\text{UCB}^{a}$ allows every agent to additionally use the observations from the other agents to accelerate exploration (without sharing raw observations), while $\text{UCB}^{b}$ uses an NN with aggregated parameters for reward prediction in a similar way to federated averaging for supervised learning. Notably, the weight between the two UCBs required by our theoretical analysis is amenable to an interesting interpretation, which emphasizes $\text{UCB}^{a}$ initially for accelerated exploration and relies more on $\text{UCB}^{b}$ later after enough observations have been collected to train the NNs for accurate reward prediction (i.e., reliable exploitation). We prove sub-linear upper bounds on both the cumulative regret and the number of communication rounds of FN-UCB, and empirically demonstrate its competitive performance., Comment: ICLR 2023. Code: https://github.com/daizhongxiang/Federated-Neural-Bandits

In an effort to effectively model observed patterns in the spatial configuration of individuals of multiple species in nature, we introduce the saturated pairwise interaction Gibbs point process. Its main strength lies in its ability to model both attraction and repulsion within and between species, over different scales. As such, it is particularly well-suited to the study of associations in complex ecosystems. Based on the existing literature, we provide an easy to implement fitting procedure as well as a technique to make inference for the model parameters. We also prove that under certain hypotheses the point process is locally stable, which allows us to use the well-known `coupling from the past' algorithm to draw samples from the model. Different numerical experiments show the robustness of the model. We study three different ecological datasets, demonstrating in each one that our model helps disentangle competing ecological effects on species' distribution., Comment: 36 pages, 14 figures

The two-channel Kondo lattice likely hosts a rich array of phases, including hastatic order, a channel symmetry breaking heavy Fermi liquid. We revisit its one-dimensional phase diagram using density matrix renormalization group and, in contrast to previous work find algebraic hastatic orders generically for stronger couplings. These are heavy Tomonaga-Luttinger liquids with nonanalyticities at Fermi vectors captured by hastatic density waves. We also find a predicted additional non-local order parameter due to interference between hastatic spinors, not present at large-N; and residual repulsive interactions at strong coupling suggesting non-Fermi-liquid physics in higher dimensions., Comment: 6+7 pages, 4+5 figures

In this paper, a data augmentation method is proposed for depression detection from speech signals. Samples for data augmentation were created by changing the frame-width and the frame-shift parameters during the feature extraction process. Unlike other data augmentation methods (such as VTLP, pitch perturbation, or speed perturbation), the proposed method does not explicitly change acoustic parameters but rather the time-frequency resolution of frame-level features. The proposed method was evaluated using two different datasets, models, and input acoustic features. For the DAIC-WOZ (English) dataset when using the DepAudioNet model and mel-Spectrograms as input, the proposed method resulted in an improvement of 5.97% (validation) and 25.13% (test) when compared to the baseline. The improvements for the CONVERGE (Mandarin) dataset when using the x-vector embeddings with CNN as the backend and MFCCs as input features were 9.32% (validation) and 12.99% (test). Baseline systems do not incorporate any data augmentation. Further, the proposed method outperformed commonly used data-augmentation methods such as noise augmentation, VTLP, Speed, and Pitch Perturbation. All improvements were statistically significant., Comment: Accepted to ICASSP 2022. copyright 2022 IEEE. Personal use of this material is permitted

This paper develops a spatial input-output approach to investigate the dynamics of a turbulent boundary layer subject to a localized single frequency excitation. This method uses one-way spatial integration to reformulate the problem in terms of spatial evolution equations. The technique is used to examine the effect of localized periodic actuation at a given temporal frequency, based on an experimental set-up in which an active large-scale is introduced into the outer layer of a turbulent boundary layer. First, the large-scale structures associated with the phase-locked modal velocity field obtained from spatial input-output analysis are shown to closely match those computed based on hot-wire measurements. The approach is then used to further investigate the response of the boundary layer to the synthetically generated large-scale. A quadrant trajectory analysis indicates that the spatial input-output response produces shear stress distributions consistent with those in canonical wall-bounded turbulent flows in terms of both the order and types of events observed. The expected correspondence between the dominance of different quadrant behavior and actuation frequency is also observed. These results highlight the promise of a spatial input-output framework for analyzing the formation and streamwise evolution of structures in actuated wall-bounded turbulent flows., Comment: 34 pages, 12 figures, preliminary work presented in AIAA Aviation 2021 with paper no. 2021-2873

Floquet engineering is a powerful technique using periodic potentials, typically laser light, to drive materials into regimes inaccessible in equilibrium. Here, we show that Kondo models can be driven to multi-channel degenerate points, even when the starting model is single-channel. These emergent channels are differentiated by symmetry, and their strength and number can be controlled by changing the light polarization, frequency and amplitude. Unpolarized light, constructed by polarization averaging, is particularly useful to induce three and four channel degeneracies. Multi-channel Kondo models host a wide variety of exotic phenomena, including non-Abelian anyons in impurity models and composite pair superconductivity in lattice models. We demonstrate our findings on both a simple square lattice toy model and a more realistic spin-orbit coupled model for $J=5/2$ Ce ions in a tetragonal environment, as relevant for the Ce 115 materials, and show that the transition temperature for composite pair superconductivity can be dynamically enhanced., Comment: 19 pages including Appendices, 10 figures

The growing literature of Federated Learning (FL) has recently inspired Federated Reinforcement Learning (FRL) to encourage multiple agents to federatively build a better decision-making policy without sharing raw trajectories. Despite its promising applications, existing works on FRL fail to I) provide theoretical analysis on its convergence, and II) account for random system failures and adversarial attacks. Towards this end, we propose the first FRL framework the convergence of which is guaranteed and tolerant to less than half of the participating agents being random system failures or adversarial attackers. We prove that the sample efficiency of the proposed framework is guaranteed to improve with the number of agents and is able to account for such potential failures or attacks. All theoretical results are empirically verified on various RL benchmark tasks., Comment: Published at NeurIPS 2021. Extended version with proofs and additional experimental details and results. New version changes: reduced file size of figures; added a diagram illustrating the problem setting; added link to code on GitHub; modified proof for Theorem 6 (highlighted in red)

Non-abelian anyons are highly desired for topological quantum computation purposes, with Majorana fermions providing a promising route, particularly zero modes with non-trivial mutual statistics. Yet realizing Majorana zero modes in matter is a challenge, with various proposals in chiral superconductors, nanowires, and spin liquids, but no clear experimental examples. Heavy fermion materials have long been known to host Majorana fermions at two-channel Kondo impurity sites, however, these impurities cannot be moved adiabatically and generically occur in metals, where the absence of a gap removes the topological protection. Here, we consider an ordered lattice of these two-channel Kondo impurities, which at quarter-filling form a Kondo insulator. We show that topological defects in this state will host Majorana zero modes, or possibly more complicated parafermions. These states are protected by the insulating gap and may be adiabatically braided, providing the novel possibility of realizing topological quantum computation in heavy fermion materials.

Many software engineering research papers rely on time-based data (e.g., commit timestamps, issue report creation/update/close dates, release dates). Like most real-world data however, time-based data is often dirty. To date, there are no studies that quantify how frequently such data is used by the software engineering research community, or investigate sources of and quantify how often such data is dirty. Depending on the research task and method used, including such dirty data could affect the research results. This paper presents the first survey of papers that utilize time-based data, published in the Mining Software Repositories (MSR) conference series. Out of the 690 technical track and data papers published in MSR 2004--2020, we saw at least 35% of papers utilized time-based data. We then used the Boa and Software Heritage infrastructures to help identify and quantify several sources of dirty commit timestamp data. Finally we provide guidelines/best practices for researchers utilizing time-based data from Git repositories., Comment: Accepted to the 18th International Conference on Mining Software Repositories (MSR 2021)

The hidden order in URu$_2$Si$_2$ remains a compelling mystery after more than thirty years, with the order parameter still unidentified. One intriguing proposal for the phase has been hastatic order: a symmetry breaking heavy Fermi liquid with a spinorial hybridization that breaks both single and double time-reversal symmetry. Hastatic order is the first spinorial, rather than vectorial order in materials, but previous work has not yet found direct consequences of the spinorial nature. In this paper, we revisit the hastatic proposal within Landau-Ginzburg theory. Rather than a single spinorial order parameter breaking double-time-reversal symmetry, we find two gauge invariant vectorial orders: the expected composite order with on-site moments, and a new quantity capturing symmetries broken solely by the spinorial nature. We address the effect of fluctuations and disorder on the tetragonal symmetry breaking, explaining the absence of in-plane moments in URu$_2$Si$_2$ and predicting a new transition in transverse field., Comment: 17 pages, 5 figures

Search clarification has recently attracted much attention due to its applications in search engines. It has also been recognized as a major component in conversational information seeking systems. Despite its importance, the research community still feels the lack of a large-scale data for studying different aspects of search clarification. In this paper, we introduce MIMICS, a collection of search clarification datasets for real web search queries sampled from the Bing query logs. Each clarification in MIMICS is generated by a Bing production algorithm and consists of a clarifying question and up to five candidate answers. MIMICS contains three datasets: (1) MIMICS-Click includes over 400k unique queries, their associated clarification panes, and the corresponding aggregated user interaction signals (i.e., clicks). (2) MIMICS-ClickExplore is an exploration data that includes aggregated user interaction signals for over 60k unique queries, each with multiple clarification panes. (3) MIMICS-Manual includes over 2k unique real search queries. Each query-clarification pair in this dataset has been manually labeled by at least three trained annotators. It contains graded quality labels for the clarifying question, the candidate answer set, and the landing result page for each candidate answer. MIMICS is publicly available for research purposes, thus enables researchers to study a number of tasks related to search clarification, including clarification generation and selection, user engagement prediction for clarification, click models for clarification, and analyzing user interactions with search clarification.

Recursive preferences, of the sort developed by Epstein and Zin (1989), play an integral role in modern macroeconomics and asset pricing theory. Unfortunately, it is non-trivial to establish the unique existence of a solution to recursive utility models. We show that the tightest known existence and uniqueness conditions can be extended to (i) Schorfheide, Song and Yaron (2018) recursive utilities and (ii) recursive utilities with `narrow framing'. Further, we sharpen the solution space of Borovicka and Stachurski (2019) from $L_1$ to $L_p$ so that the results apply to a broader class of modern asset pricing models. For example, using $L_2$ Hilbert space theory, we find the class of parameters which generate a unique $L_2$ solution to the Bansal and Yaron (2004) and Schorfheide, Song and Yaron (2018) models.

Magnetic exchange couplings can be tuned by coupling to periodic light, where the frequency and amplitude are typically varied: a process known as Floquet engineering. The polarization of the light is also important, and in this paper, we show how different polarizations, including several types of unpolarized light, can tune the exchange couplings in distinct ways. Using unpolarized light, for example, it is possible to tune the material without breaking either time-reversal or any lattice symmetries. To illustrate these effects generically, we consider single-band Hubbard models at half-filling on the honeycomb, square and triangular lattices. We derive the effective Heisenberg spin models to fourth order in perturbation theory for arbitrary fixed polarizations, and several types of unpolarized light that preserve time-reversal and lattice symmetries. Coupling these models to periodic light tunes first, second and third neighbor exchange couplings, as well as ring exchange terms on the square and triangular lattices. Circularly polarized light induces chiral fields for the honeycomb and triangular lattices, which favors non-coplanar magnetism and potential chiral spin liquids. We discuss how to maximize the enhancement of the couplings without inducing heating., Comment: 25 pages, 21 figures

We use a projective symmetry group analysis to determine all symmetric spin liquids on the stuffed honeycomb lattice Heisenberg model. This lattice interpolates between honeycomb, triangular and dice lattices, always preserving hexagonal symmetry, and it already has one spin liquid candidate, TbInO$_3$, albeit with strong spin-orbit coupling not considered here. In addition to the stuffed honeycomb lattice itself, we gain valuable insight into potential spin liquids on the honeycomb and triangular lattices, as well as how they might be connected. For example, the sublattice pairing state proposed on the honeycomb lattice connects to the uniform spinon Fermi surface that may be relevant for the triangular lattice with ring exchange, while there are no spin liquids competitive on both the $J_1-J_2$ honeycomb and triangular lattice limits. In particular, we find three stuffed honeycomb descendants of the U(1) Dirac spin liquid widely believed to be found on the $J_1-J_2$ triangular lattice. We also discuss how spin liquids near the honeycomb limit can potentially explain the physics of LiZn$_2$Mo$_3$O$_8$., Comment: 14 pages, 11 figures

Floquet engineering is a powerful tool that drives materials with periodic light. Traditionally, the light is monochromatic, with amplitude, frequency, and polarization varied. We introduce Floquet engineering via unpolarized light built from quasi-monochromatic light, and show how it can modify strongly correlated systems, while preserving the original symmetries. Different types of unpolarized light can realize different strongly correlated phases As an example, we treat insulating magnetic materials on a triangular lattice and show how unpolarized light can induce a Dirac spin liquid., Comment: 5 + 6 pages, 3 + 2 figures

We currently observe a disconcerting phenomenon in machine learning studies in psychiatry: While we would expect larger samples to yield better results due to the availability of more data, larger machine learning studies consistently show much weaker performance than the numerous small-scale studies. Here, we systematically investigated this effect focusing on one of the most heavily studied questions in the field, namely the classification of patients suffering from major depressive disorder (MDD) and healthy control (HC) based on neuroimaging data. Drawing upon structural magnetic resonance imaging (MRI) data from a balanced sample of $N = 1,868$ MDD patients and HC from our recent international Predictive Analytics Competition (PAC), we first trained and tested a classification model on the full dataset which yielded an accuracy of $61\,\%$. Next, we mimicked the process by which researchers would draw samples of various sizes ($N = 4$ to $N = 150$) from the population and showed a strong risk of misestimation. Specifically, for small sample sizes ($N = 20$), we observe accuracies of up to $95\,\%$. For medium sample sizes ($N = 100$) accuracies up to $75\,\%$ were found. Importantly, further investigation showed that sufficiently large test sets effectively protect against performance misestimation whereas larger datasets per se do not. While these results question the validity of a substantial part of the current literature, we outline the relatively low-cost remedy of larger test sets, which is readily available in most cases.

Lesotho and Wales have undergone significant curriculum changes recently, and both advocate the desire for their learners to be 'active citizens' and to acquire core life skills that allow them to be 'creative contributors' to society. The Connecting Classrooms Through Global Learning (CCGL) cluster lead schools in this research have been working in partnership for 13 years. They believe that their collaborative projects are essential elements of their curricula that provide significant global learning for all involved. This study centres upon the experiences and impact of global partnerships on the learners and asks: (1) How have pupils in both countries engaged in the partnership?; (2) What was their experience of it?; and (3) How has the partnership impacted on their learning? By providing opportunities to investigate and analyse a snapshot of the pupil's perceptions and experiences, their knowledge and assumptions, and what types of skills they display as a result, we can begin to document the effectiveness of such projects and partnerships, and the impact on learning in general. This is an important addition to global learning and international partnerships research in Wales and Lesotho, as at the time of writing no other such paper has ever been written. As Welsh schools begin to deliver the new New Curriculum for Wales (2022), the findings from this study will support schools to prioritise and further embed their partnerships in practice. [This paper was published by Connecting Classrooms through Global Learning in collaboration with the Development Education Research Centre (DERC). The Connecting Classrooms through Global Learning (CCGL) programme is funded by the British Council and UK aid.]

Transgender individuals (TIs) show brain structural alterations that differ from their biological sex as well as their perceived gender. To substantiate evidence that the brain structure of TIs differs from male and female, we use a combined multivariate and univariate approach. Gray matter segments resulting from voxel-based morphometry preprocessing of $N = 1753$ cisgender (CG) healthy participants were used to train ($N=1402$) and validate (20 % hold-out; $N = 351$) a support-vector machine classifying the biological sex. As a second validation, we classified $N = 1104$ patients with depression. A third validation was performed using the matched CG sample of the transgender women (TWs) application-sample. Subsequently, the classifier was applied to $N = 26$ TWs. Finally, we compared brain volumes of CG-men, women and TW-pre/post treatment (cross-sex hormone treatment) in a univariate analysis controlling for sexual orientation, age and total brain volume. The application of our biological sex classifier to the transgender sample resulted in a significantly lower true positive rate (TPR) (TPR-male = 56.0 %). The TPR did not differ between CG-individuals with (TPR-male = 86.9 %) and without depression (TPR-male = 88.5 %). The univariate analysis of the transgender application-sample revealed that TW-pre/post treatment show brain structural differences from CG-women and CG-men in the putamen and insula, as well as the whole-brain analysis. Our results support the hypothesis that brain structure in TW differs from brain structure of their biological sex (male) as well as their perceived gender (female). This finding substantiates evidence that TIs show specific brain structural alterations leading to a different pattern of brain structure than CG-individuals., Comment: Content adapted to the publication at Neuropsychopharmacology

In order to satisfy safety conditions, an agent may be constrained from acting freely. A safe controller can be designed a priori if an environment is well understood, but not when learning is employed. In particular, reinforcement learned (RL) controllers require exploration, which can be hazardous in safety critical situations. We study the benefits of giving structure to the constraints of a constrained Markov decision process by specifying them in formal languages as a step towards using safety methods from software engineering and controller synthesis. We instantiate these constraints as finite automata to efficiently recognise constraint violations. Constraint states are then used to augment the underlying MDP state and to learn a dense cost function, easing the problem of quickly learning joint MDP/constraint dynamics. We empirically evaluate the effect of these methods on training a variety of RL algorithms over several constraints specified in Safety Gym, MuJoCo, and Atari environments., Comment: NeurIPS 2019 Workshop on Safety and Robustness in Decision Making

In recent years, a number of functional inequalities have been derived for Poisson random measures, with a wide range of applications. In this paper, we prove that such inequalities can be extended to the setting of marked temporal point processes, under mild assumptions on their Papangelou conditional intensity. First, we derive a Poincar\'e inequality. Second, we prove two transportation cost inequalities. The first one refers to functionals of marked point processes with a Papangelou conditional intensity and is new even in the setting of Poisson random measures. The second one refers to the law of marked temporal point processes with a Papangelou conditional intensity, and extends a related inequality which is known to hold on a general Poisson space. Finally, we provide a variational representation of the Laplace transform of functionals of marked point processes with a Papangelou conditional intensity. The proofs make use of an extension of the Clark-Ocone formula to marked temporal point processes. Our results are shown to apply to classes of renewal, nonlinear Hawkes and Cox point processes.