Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of Turbulent Proton and Electron Heating Rates via Landau Damping Constrained by Parker Solar Probe Observations

The heating of ions and electrons due to turbulent dissipation plays a crucial role in the thermodynamics of the solar wind and other plasma environments. Using magnetic field and thermal plasma observations from the first two perihelia of the Parker Solar Probe, we model the relative heating rates as a function of the radial distance, magnetic spectra, and plasma conditions, enabling us to better characterize the thermodynamics of the inner heliosphere. We employ the Howes et al. steady-state cascade model, which considers the behavior of turbulent, low-frequency, wavevector-anisotropic, critically balanced Alfvénic fluctuations that dissipate via Landau damping to determine proton-to-electron heating rates Q p /Q e . We distinguish ion cyclotron frequency circularly polarized waves from low-frequency turbulence and constrain the cascade model using spectra constructed from the latter. We find that the model accurately describes the observed energy spectrum from over 39.4% of the intervals from Encounters 1 and 2, indicating the possibility for Landau damping to heat the young solar wind. The ability of the model to describe the observed turbulent spectra increases with the ratio of thermal-to-magnetic pressure, β p , indicating that the model contains the necessary physics at higher β p . We estimate high magnitudes for the Kolmogorov constant which is inversely proportional to the nonlinear energy cascade rate. We verify the expected strong dependency of Q p /Q e on β p and the consistency of the critical balance assumption.
Context-specific kernel-based hidden Markov model for time series analysis

Traditional hidden Markov models have been a useful tool to understand and model stochastic dynamic data; in the case of non-Gaussian data, models such as mixture of Gaussian hidden Markov models can be used. However, these suffer from the computation of precision matrices and have a lot of unnecessary parameters. As a consequence, such models often perform better when it is assumed that all variables are independent, a hypothesis that may be unrealistic. Hidden Markov models based on kernel density estimation are also capable of modeling non-Gaussian data, but they assume independence between variables. In this article, we introduce a new hidden Markov model based on kernel density estimation, which is capable of capturing kernel dependencies using context-specific Bayesian networks. The proposed model is described, together with a learning algorithm based on the expectation-maximization algorithm. Additionally, the model is compared to related HMMs on synthetic and real data. From the results, the benefits in likelihood and classification accuracy from the proposed model are quantified and analyzed.
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation

Sparse Gaussian Processes are a key component of high-throughput Bayesian Optimisation (BO) loops; however, we show that existing methods for allocating their inducing points severely hamper optimisation performance. By exploiting the quality-diversity decomposition of Determinantal Point Processes, we propose the first inducing point allocation strategy designed specifically for use in BO. Unlike existing methods which seek only to reduce global uncertainty in the objective function, our approach provides the local high-fidelity modelling of promising regions required for precise optimisation. More generally, we demonstrate that our proposed framework provides a flexible way to allocate modelling capacity in sparse models and so is suitable broad range of downstream sequential decision making tasks.
From Inclusive Language to Gender-Neutral Machine Translation

Gender inclusivity in language has become a central topic of debate and research. Its application in the cross-lingual contexts of human and machine translation (MT), however, remains largely unexplored. Here, we discuss Gender-Neutral Translation (GNT) as a form of gender in-clusivity in translation and advocate for its adoption for MT models, which have been found to perpetuate gender bias and discrimination. To this aim, we review a selection of relevant institutional guidelines for Gender-Inclusive Language (GIL) to collect and systematize useful strategies of gender neutralization. Then, we discuss GNT and its scenarios of use, devising a list of desiderata. Finally, we identify the main technical challenges to the implementation of GNT in MT. Throughout these contributions we focus on translation from English into Italian, as representative of salient linguistic transfer problems, due to the different rules for gender marking in their grammar.
Correction of high-order phase variation effects in dynamic field monitoring

Purpose: Field monitoring measures field perturbations, which can be accounted for during image reconstructions. In certain field monitoring environments, significant phase deviations can arise far from isocenter due to the finite extent of the gradient and/or main magnet. This can degrade the accuracy of field dynamics when field probes are placed near or outside the diameter spherical volume of the gradient coils and/or main magnet, leading to corrupted image quality. The objective of this work was to develop a correction algorithm that reduces errors from highly nonlinear phase variations at distant field probes in field dynamic fits. Methods: The algorithm is split into three components. Component one fits phase coefficients one spatial order at a time, while the second implements a weighted least squares solution based on probe distance. After initial fitting, component three calculates phase residuals and removes the phase for distant probes before re-fitting. Two healthy volunteers were scanned on a head-only 7T MRI using diffusion-weighted single-shot spiral and EPI sequences and field monitoring was performed. Images were reconstructed with and without phase coefficient correction and compared qualitatively. Results: The algorithm was able to correct corrupted field dynamics, resulting in image quality improvements. Significant artefact reduction was observed when correcting higher order fits, especially for diffusion weighted images. Stepwise fitting provided the most correction benefit, which was marginally improved when adding weighted least squares and phase residual corrections. Conclusion: The proposed algorithm can mitigate effects of phase errors in field monitoring, providing improved reliability of field dynamic characterization.
Stability Estimates for Some Parabolic Inverse Problems With the Final Overdetermination via a New Carleman Estimate

This paper is about Holder and Lipschitz stability estimates and uniqueness theorems for some coefficient inverse problems and associated inverse source problems for a general linear parabolic equation of the second order with variable coefficients. The data for the inverse problem are given at the final moment of time {t=T}. In addition, both Dirichlet and Neumann boundary conditions are given either on a part or on the entire lateral boundary. Thus, if these boundary conditions are given only at a part of the boundary, then even if the target coefficient is known, still the forward problem is not a classical initial boundary value problem.
Breaking the f+1 Barrier: Executing Payment Transactions in Parallel with Less than f+1 Validations

We consider the problem of supporting payment transactions in an asynchronous system in which up to $f$ validators are subject to Byzantine failures under the control of an adaptive adversary. It was shown that this problem can be solved without consensus by using byzantine quorum systems (requiring at least $2f+1$ validations per transaction in asynchronous systems). We show that it is possible to validate transactions in parallel with less than $f$ validations per transaction if each transaction spends no more that a small fraction of a balance. Our solution relies on a novel quorum system that we introduce in this paper and that we call $(k_1,k_2)$-quorum systems. In the presence of a non-adaptive adversary, these systems can be used to allow up to $k_1$ transactions to be validated concurrently and asynchronously but prevent more than $k_2$ transactions from being validated. If the adversary is adaptive, these systems can be used to allow $k_1$ transaction to be validated and prevent more than $k'_2>k_2$ transactions from being validated, the difference $k'_2-k_2$ being dependent on the quorum system's {\em validation slack}, which we define in this paper. Using $(k_1,k_2)$-quorum systems, a payer can execute multiple partial spending transactions to spend a portion of its initial balance with less than full quorum validation (less than $f$ validations per transaction) then reclaim any remaining funds using one fully validated transaction, which we call a {\em settlement} transaction.
Towards Modular Machine Learning Solution Development: Benefits and Trade-offs

Machine learning technologies have demonstrated immense capabilities in various domains. They play a key role in the success of modern businesses. However, adoption of machine learning technologies has a lot of untouched potential. Cost of developing custom machine learning solutions that solve unique business problems is a major inhibitor to far-reaching adoption of machine learning technologies. We recognize that the monolithic nature prevalent in today’s machine learning applications stands in the way of efficient and cost effective customized machine learning solution development. In this work we explore the benefits of modular machine learning solutions and discuss how modular machine learning solutions can overcome some of the major solution engineering limitations of monolithic machine learning solutions. We analyze the trade-offs between modular and monolithic machine learning solutions through three deep learning problems; one text based and the two image based. Our experimental results show that modular machine learning solutions have a promising potential to reap the solution engineering advantages of modularity while gaining performance and data advantages in a way the monolithic machine learning solutions do not permit.
Self-Supervised Learning for Enhancing Angular Resolution in Automotive MIMO Radars

A novel framework to enhance the angular resolution of automotive radars is proposed. An approach to enlarge the antenna aperture using artificial neural networks is developed using a self-supervised learning scheme. Data from a high angular resolution radar, i.e., a radar with a large antenna aperture, is used to train a deep neural network to extrapolate the antenna element's response. Afterward, the trained network is used to enhance the angular resolution of compact, low-cost radars. One million scenarios are simulated in a Monte-Carlo fashion, varying the number of targets, their Radar Cross Section (RCS), and location to evaluate the method's performance. Finally, the method is tested in real automotive data collected outdoors with a commercial radar system. A significant increase in the ability to resolve targets is demonstrated, which can translate to more accurate and faster responses from the planning and decision-making system of the vehicle.
Breaking the Boundaries of Knowledge Space: Analyzing the Knowledge Spanning on the Q&A Website through Word Embeddings

The challenge of raising a creative question exists in recombining different categories of knowledge. However, the impact of recombination remains controversial. Drawing on the theories of knowledge recombination and category spanning, we propose that both the distance of knowledge spanning and the hierarchy of knowledge shape the appeal of questions. Using word embedding models and the data collected from a large online knowledge market (N = 463,545), we find that the impact of knowledge spanning on the appeal of questions is parabolic: the appeal of questions increases up to a threshold, after which point the positive effect reverses. However, the nonlinear influence of knowledge spanning is contingent upon the hierarchy of knowledge. The theoretical and practical implications of these findings for future research on knowledge recombination are discussed. We fill the research gap by conceptualizing question asking as knowledge spanning and highlighting the theoretical underpinnings of the knowledge hierarchy.
Learning Effective Strategies for Moving Target Defense with Switching Costs

Moving Target Defense (MTD) has emerged as a key technique in various security applications as it takes away the attacker's ability to perform reconnaissance for exploiting a system's vulnerabilities. However, most of the existing research in the field assumes unrealistic access to information about the attacker's motivations and/or actions when developing MTD strategies. Many of the existing approaches also assume complete knowledge regarding the vulnerabilities of a system and how each of these vulnerabilities can be exploited by an attacker. In this work, we aim to create algorithms that generate effective Moving Target Defense strategies that do not rely on prior knowledge about the attackers. Our work assumes that the only way the defender receives information about its own reward is via interaction with the attacker in a repeated game setting. Depending on the amount of information that can be obtained from the interactions, we devise two different algorithms using multi-armed bandit formulation to identify efficient strategies. We then evaluate our algorithms using data mined from the National Vulnerability Database to showcase that they match the performance of the state-of-the-art techniques, despite using a lot less amount of information.
Deep learning-based method for segmenting epithelial layer of tubules in histopathological images of testicular tissue

Abstract. Purpose There is growing concern that male reproduction is affected by environmental chemicals. One way to determine the adverse effect of environmental pollutants is to use wild animals as monitors and evaluate testicular toxicity using histopathology. We propose an automated method to process histology images of testicular tissue. Approach Testicular tissue consists of seminiferous tubules. Segmenting the epithelial layer of the seminiferous tubule is a prerequisite for developing automated methods to detect abnormalities in tissue. We suggest an encoder–decoder fully connected convolutional neural network model to segment the epithelial layer of the seminiferous tubules in histological images. The ResNet-34 is used in the feature encoder module, and the squeeze and excitation attention block is integrated into the encoding module improving the segmentation and localization of epithelium. Results We applied the proposed method for the two-class problem, where the epithelial layer of the tubule is the target class. The F-score and Intersection over Union of the proposed method are 0.85 and 0.92. Although the proposed method is trained on a limited training set, it performs well on an independent dataset and outperforms other state-of-the-art methods. Conclusion The pretrained ResNet-34 in the encoder and attention block suggested in the decoder result in better segmentation and generalization. The proposed method can be applied to testicular tissue images from any mammalian species and can be used as the first part of a fully automated testicular tissue processing pipeline. The dataset and codes are publicly available on GitHub.
Geostatistical analysis of binomial data: generalised linear or transformed Gaussian modelling?

When fitting a binomial geostatistical model to data obtained by spatially discrete sampling, techniques such as Markov chain Monte Carlo, which are computationally intensive and require careful tuning to each application, need to be employed. As a result, this approach is often infeasible when computational resources are scarce or statistical expertise is unavailable. A practical solution to this problem is to transform the binomial samples such that the conditional distribution of the transformed data can be assumed to be approximately Gaussian. Likelihood‐based inference can then be performed analytically, whereas Bayesian inference requires only routine Monte Carlo simulation. In this paper, the predictive performance of the binomial model‐fitting method is compared with that of the approximate transformed Gaussian method. A simulation experiment is undertaken in which data are simulated from a binomial geostatistical model, and both methods are then used to make predictive inference. Predictions are assessed using the empirical root mean square prediction error and empirical coverage probabilities of nominal prediction intervals obtained for the approximate method. As expected, the comparative performance of the approximate method deteriorates as the denominator decreases and, to a lesser extent, as the overall proportion of successes in the simulated data deviates from 0.5. Our results provide guidance on when the approximate method can safely be used. We demonstrate the two methods on village‐level prevalence data pertaining to the tropical eye disease Loa loa. Copyright © 2013 John Wiley & Sons, Ltd.
MLinter: Learning Coding Practices from Examples—Dream or Reality?

Coding practices are increasingly used by software companies. Their use promotes consistency, readability, and maintainability, which contribute to software quality. Coding practices were initially enforced by general-purpose linters, but companies now tend to design and adopt their own company-specific practices. However, these company-specific practices are often not automated, making it challenging to ensure they are shared and used by developers. Converting these practices into linter rules is a complex task that requires extensive static analysis and language engineering expertise.In this paper, we seek to answer the following question: can coding practices be learned automatically from examples manually tagged by developers? We conduct a feasibility study using CodeBERT, a state-of-the-art machine learning approach, to learn linter rules. Our results show that, although the resulting classifiers reach high precision and recall scores when evaluated on balanced synthetic datasets, their application on real-world, unbalanced codebases, while maintaining excellent recall, suffers from a severe drop in precision that hinders their usability.
An Energy-aware and Fault-tolerant Deep Reinforcement Learning based approach for Multi-agent Patrolling Problems

Autonomous vehicles are suited for continuous area patrolling problems. However, finding an optimal patrolling strategy can be challenging for many reasons. Firstly, patrolling environments are often complex and can include unknown environmental factors, such as wind or landscape. Secondly, autonomous vehicles can have failures or hardware constraints, such as limited battery life. Importantly, patrolling large areas often requires multiple agents that need to collectively coordinate their actions. In this work, we consider these limitations and propose an approach based on model-free, deep multi-agent reinforcement learning. In this approach, the agents are trained to patrol an environment with various unknown dynamics and factors. They can automatically recharge themselves to support continuous collective patrolling. A distributed homogeneous multi-agent architecture is proposed, where all patrolling agents execute identical policies locally based on their local observations and shared location information. This architecture provides a patrolling system that can tolerate agent failures and allow supplementary agents to be added to replace failed agents or to increase the overall patrol performance. The solution is validated through simulation experiments from multiple perspectives, including the overall patrol performance, the efficiency of battery recharging strategies, the overall fault tolerance, and the ability to cooperate with supplementary agents.
The relationship between psycho-environmental characteristics and wellbeing in non-spending players of certain mobile games

Revenue generation in modern digital games is often dependent on in-game continuous player spending. This brings concerns that games may be including features which drive player spending in potentially harmful ways. Moreover, it is unknown what types of individuals may be vulnerable to these design-driven harms. We used player-donated, objective data of playtime and in-game spending from a sample of 295 players of games previously identified as ‘designed to drive spending’. We combined this with psycho-environmental characteristics and wellbeing measures administered to the players. Quantile regression analyses did not show an interaction between player characteristics and playtime/spend as predictors of wellbeing outcomes; nor did we find a difference in wellbeing between players of these games and games with alternative monetization models. This is discussed in light of a low proportion of spenders in the sample, which may affect results pertaining to the moderating role of spend. However, it suggests that while design features in games aiming to drive player spend may be unethical and problematic, they may not necessarily cause harm to the normative player.
Theoretical and experimental study of the short and long range sensing using gold nanostructures

We study theoretically and experimentally the variation in localized surface plasmon resonance (LSPR) structure (λmax=660 nm) as a function of dielectric coating thickness. The influence of the morphology and interparticle distance on the LSPR spectra of a glass/Au NSs interface with a constant thickness of diamond (NCD) overcoating was investigated through the calculation of theoretical transmission spectra. Ajusting the theoretical curve to experimental LSPR spectra allowed fixing the geometry of the plasmonic interface and permitted to evaluate the change in the wavelength at maximum absorption (λmax) as a function of the thickness of the NCD overlayers. The theoretical data were compared with experimental ones obtained on glass/AuNSs/ NCD surface.
Should we abandon optimization theory? The need for bounded rationality

This paper makes explicit the rhetoric of optimization. Various arguments are examined, in order to determine whether we should retain optimization theory or assume bounded rationality. Empirical evidence confounds optimization theory; in the face of experimental studies, an empirical dilemma emerges, according to which we should discard either the theory of expected utility or the criterion of empirical refutation. Methodological criticisms attack optimization theory's epistemological status; together, they give rise to a methodological trilemma, according to which optimization theory is indeterminate, unfalsifiable or tautological. Methodological defences seem to protect optimization theory against criticism; but a more careful examination shows that either they involve logical defects or they rest upon a conceptual gap. Theoretical difficulties plague optimization theory; though various extensions have been proposed, optimization theory entails a theoretical dilemma, according to which one must choose between unrealistic environment and unrealistic abilities. Should we abandon optimization theory? The leading question will be answered in the affirmative: optimization theory should be abandoned and bounded rationality should be assumed.