Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Bias Meets Data Bias: The Impacts of Labeling and Measurement Errors on Fairness Criteria

Although many fairness criteria have been proposed to ensure that machine learning algorithms do not exhibit or amplify our existing social biases, these algorithms are trained on datasets that can themselves be statistically biased. In this paper, we investigate the robustness of existing (demographic) fairness criteria when the algorithm is trained on biased data. We consider two forms of dataset bias: errors by prior decision makers in the labeling process, and errors in the measurement of the features of disadvantaged individuals. We analytically show that some constraints (such as Demographic Parity) can remain robust when facing certain statistical biases, while others (such as Equalized Odds) are significantly violated if trained on biased data. We provide numerical experiments based on three real-world datasets (the FICO, Adult, and German credit score datasets) supporting our analytical findings. While fairness criteria are primarily chosen under normative considerations in practice, our results show that naively applying a fairness constraint can lead to not only a loss in utility for the decision maker, but more severe unfairness when data bias exists. Thus, understanding how fairness criteria react to different forms of data bias presents a critical guideline for choosing among existing fairness criteria, or for proposing new criteria, when available datasets may be biased.
Asymptotic Properties for Bayesian Neural Network in Besov Space

Neural networks have shown great predictive power when dealing with various unstructured data such as images and natural languages. The Bayesian neural network captures the uncertainty of prediction by putting a prior distribution for the parameter of the model and computing the posterior distribution. In this paper, we show that the Bayesian neural network using spike-and-slab prior has consistency with nearly minimax convergence rate when the true regression function is in the Besov space. Even when the smoothness of the regression function is unknown the same posterior convergence rate holds and thus the spike-and-slab prior is adaptive to the smoothness of the regression function. We also consider the shrinkage prior, which is more feasible than other priors, and show that it has the same convergence rate. In other words, we propose a practical Bayesian neural network with guaranteed asymptotic properties.
Technical Debts and Faults in Open-source Quantum Software Systems: An Empirical Study

Quantum computing is a rapidly growing field attracting the interest of both researchers and software developers. Supported by its numerous open-source tools, developers can now build, test, or run their quantum algorithms. Although the maintenance practices for traditional software systems have been extensively studied, the maintenance of quantum software is still a new field of study but a critical part to ensure the quality of a whole quantum computing system. In this work, we set out to investigate the distribution and evolution of technical debts in quantum software and their relationship with fault occurrences. Understanding these problems could guide future quantum development and provide maintenance recommendations for the key areas where quantum software developers and researchers should pay more attention. In this paper, we empirically studied 118 open-source quantum projects, which were selected from GitHub. The projects are categorized into 10 categories. We found that the studied quantum software suffers from the issues of code convention violation, error-handling, and code design. We also observed a statistically significant correlation between code design, redundant code or code convention, and the occurrences of faults in quantum software.
Low frequency tail of gravitational wave spectra from hydromagnetic turbulence

Hydrodynamic and magnetohydrodynamic (MHD) turbulence in the early Universe can drive gravitational waves (GWs) and imprint their spectrum onto that of GWs, which might still be observable today. We study the production of the GW background from freely decaying MHD turbulence from helical and nonhelical initial magnetic ﬁelds. To understand the produced GW spectra, we develop a simple model on the basis of the evolution of the magnetic stress tensor. We ﬁnd that the GW spectra obtained in this model reproduce those obtained in numerical simulations if we consider the detailed time evolution of the low frequency tail of the stress spectrum from numerical simulations. We also show that the shapes of the produced GW frequency spectra are diﬀerent for helical and nonhelical cases for the same initial magnetic energy spectra. Such diﬀerences can help distinguish helical and nonhelical initial magnetic ﬁelds from a polarized background of GWs – especially when the expected circular polarization cannot be detected directly.
The Reactive Synthesis Competition (SYNTCOMP): 2018-2021

We report on the last four editions of the reactive synthesis competition (SYNTCOMP 2018–2021). We brieﬂy describe the evaluation scheme and the experimental setup of SYNTCOMP. Then, we introduce new benchmark classes that have been added to the SYNTCOMP library and give an overview of the participants of SYNTCOMP. Finally, we present and analyze the results of our experimental evaluations, including a ranking of tools with respect to quantity and quality of solutions. veriﬁcation, zero-sum games
Deepfake Caricatures: Amplifying attention to artifacts increases deepfake detection by humans and machines

Deepfakes pose a serious threat to digital well-being by fueling misinformation. As deepfakes get harder to recognize with the naked eye, human users become increasingly reliant on deepfake detection models to decide if a video is real or fake. Currently, models yield a prediction for a video's authenticity, but do not integrate a method for alerting a human user. We introduce a framework for amplifying artifacts in deepfake videos to make them more detectable by people. We propose a novel, semi-supervised Artifact Attention module, which is trained on human responses to create attention maps that highlight video artifacts. These maps make two contributions. First, they improve the performance of our deepfake detection classifier. Second, they allow us to generate novel"Deepfake Caricatures": transformations of the deepfake that exacerbate artifacts to improve human detection. In a user study, we demonstrate that Caricatures greatly increase human detection, across video presentation times and user engagement levels. Overall, we demonstrate the success of a human-centered approach to designing deepfake mitigation methods.
Defining the Underlying-Event Activity in the Presence of Heavy-Flavour Processes in Proton-Proton Collisions at LHC Energies

We present a systematic analysis of heavy-flavour production in the underlying event in connection to a leading hard process in pp collisions at s=13 TeV, using the PYTHIA 8 Monte Carlo event generator. We compare results from events selected by triggering on the leading hadron, as well as those triggered with reconstructed jets. We show that the kinematics of heavy-flavour fragmentation complicates the characterisation of the underlying event, and the usual method which uses the leading charged final-state hadron as a trigger may wash away the connection between the leading process and the heavy-flavour particle created in association with that. Events triggered with light or heavy-flavour jets, however, retain this connection and bring more direct information on the underlying heavy-flavour production process, but may also import unwanted sensitivity to gluon radiation. The methods outlined in the current work provide means to verify model calculations for light and heavy-flavour production in the jet and the underlying event in great details.
PiDRAM: An FPGA-based Framework for End-to-end Evaluation of Processing-in-DRAM Techniques

DRAM-based main memory is used in nearly all computing systems as a major component. Modern memory-intensive workloads have increasing memory bandwidth, latency, and capacity requirements. However, DRAM vendors often prioritize memory capacity scaling over latency and bandwidth [1]–[4]. As a result, main memory is an increasingly worsening bottleneck in computing systems [3,5–9].
Quantum Mechanics as a Theory of Incompatible Symmetries

It is increasingly becoming recognized that incompatible variables, which play an essential role in quantum mechanics (QM), are not in fact unique to QM. Here we add a new example, the “Arrow” system, to the growing list of classical systems that possess incompatible variables. We show how classical probability theory can be extended to include any system with incompatible variables in a general incompatible variables (GIV) theory. We then show how the QM theory of elementary systems emerges naturally from the GIV framework when the fundamental variables are taken to be the symmetries of the states of the system. This result follows primarily because in QM the symmetries of the Poincaré group play a double role, not only as the operators which transform the states under symmetry transformations but also as the fundamental variables of the system. The incompatibility of the QM variables is then seen to be just the incompatibility of the corresponding space-time symmetries. We also arrive at a clearer understanding of the Born Rule: although not primarily derived from symmetry – rather it is simply a free Pythagorean construction for accommodating basic features of classical probability theory in Hilbert spaces – it is Poincaré symmetry that allows the Born Rule to take on its familiar form in QM, in agreement with Gleason’s theorem. Finally, we show that any probabilistic system (classical or quantal) that possesses incompatible variables will show not only uncertainty, but also interference in its probability patterns. Thus the GIV framework provides the basis for a broader perspective from which to view QM: quantal systems are a subset of the set of all systems possessing incompatible variables (and hence showing uncertainty and interference), namely the subset in which the incompatible variables are incompatible symmetries.
Generalized Peas in a Pod: Extending Intra-system Mass Uniformity to Non-TTV Systems via the Gini Index

It has been demonstrated that planets belonging to the same close-in, compact multiple-planet system tend to exhibit a striking degree of uniformity in their sizes. A similar trend has also been found to hold for the masses of such planets, but considerations of such intra-system mass uniformity have generally been limited to statistical samples wherein a majority of systems have constituent planetary mass measurements obtained via analysis of transit timing variations (TTVs). Since systems with strong TTV signals typically lie in or near mean motion resonance, it remains to be seen whether intra-system mass uniformity is still readily emergent for nonresonant systems with non-TTV mass provenance. We thus present in this work a mass uniformity analysis of 17 non-TTV systems with masses measured via radial velocity measurements. Using the Gini index, a common statistic for economic inequality, as our primary metric for uniformity, we find that our sample of 17 non-TTV systems displays intra-system mass uniformity at a level of ∼2. 5σ confidence. We provide additional discussion of possible statistical and astrophysical underpinnings for this result. We also demonstrate the existence of a correlation (r = 0.25) between characteristic solid surface density (Σ0) of the minimum-mass extrasolar nebula and system mass Gini index, suggesting that more-massive disks may generally form systems with more-unequal planetary masses.
Vorticity convergence from Boltzmann to 2D incompressible Euler equations below Yudovich class

We obtain a microscopic description of the singularity through the so-called kinetic vorticity and understand its behavior in the vicinity of the macroscopic singularity. As a consequence of our new analysis, we settle affirmatively an open problem of the hydrodynamic limit toward Lagrangian solutions of the 2D incompressible Euler equation whose vorticity is unbounded (ω ∈ L for any fixed 1 ≤ p < ∞). Moreover, we prove the convergence of kinetic vorticities toward the vorticity of the Lagrangian solution of the Euler equation. In particular, we obtain the rate of convergence when the vorticity blows up moderately in L as p → ∞ (localized Yudovich class).
BRExIt: On Opponent Modelling in Expert Iteration

Finding a best response policy is a central objective in game theory and multi-agent learning, with modern population-based training approaches employing reinforcement learning algorithms as best-response oracles to improve play against candidate opponents (typically previously learnt policies). We propose Best Response Expert Iteration (BRExIt), which accelerates learning in games by incorporating opponent models into the state-of-the-art learning algorithm Expert Iteration (ExIt). BRExIt aims to (1) improve feature shaping in the apprentice, with a policy head predicting opponent policies as an auxiliary task, and (2) bias opponent moves in planning towards the given or learnt opponent model, to generate apprentice targets that better approximate a best response. In an empirical ablation on BRExIt's algorithmic variants against a set of fixed test agents, we provide statistical evidence that BRExIt learns better performing policies than ExIt. Code available at: https://github.com/Danielhp95/on-opponent-modelling-in-expert-iteration-code. Supplementary material available at https://arxiv.org/abs/2206.00113.
Supervised Denoising of Diffusion-Weighted Magnetic Resonance Images Using a Convolutional Neural Network and Transfer Learning

In this paper, we propose a method for denoising diffusion-weighted images (DWI) of the brain using a convolutional neural network trained on realistic, synthetic MR data. We compare our results to averaging of repeated scans, a widespread method used in clinics to improve signal-to-noise ratio of MR images. To obtain training data for transfer learning, we model, in a data-driven fashion, the effects of echo-planar imaging (EPI): Nyquist ghosting and ramp sampling. We introduce these effects to the digital phantom of brain anatomy (BrainWeb). Instead of simulating pseudo-random noise with a defined probability distribution, we perform noise scans with a brain-DWI-designed protocol to obtain realistic noise maps. We combine them with the simulated, noise-free EPI images. We also measure the Point Spread Function in a DW image of an AJR-approved geometrical phantom and inter-scan movement in a brain scan of a healthy volunteer. Their influence on image denoising and averaging of repeated images is investigated at different signal-to-noise ratio levels. Denoising performance is evaluated quantitatively using the simulated EPI images and qualitatively in real EPI DWI of the brain. We show that the application of our method allows for a significant reduction in scan time by lowering the number of repeated scans. Visual comparisons made in the acquired brain images indicate that the denoised single-repetition images are less noisy than multi-repetition averaged images. We also analyse the convolutional neural network denoiser and point out the challenges accompanying this denoising method.
Evaluating Gaussian Grasp Maps for Generative Grasping Models

Generalising robotic grasping to previously unseen objects is a key task in general robotic manipulation. The current method for training many antipodal generative grasping models rely on a binary ground truth grasp map generated from the centre thirds of correctly labelled grasp rectangles. However, these binary maps do not accurately reflect the positions in which a robotic arm can correctly grasp a given object. We propose a continuous Gaussian representation of annotated grasps to generate ground truth training data which achieves a higher success rate on a simulated robotic grasping benchmark. Three modern generative grasping networks are trained with either binary or Gaussian grasp maps, along with recent advancements from the robotic grasping literature, such as discretisation of grasp angles into bins and an attentional loss function. Despite negligible difference according to the standard rectangle metric, Gaussian maps better reproduce the training data and therefore improve success rates when tested on the same simulated robot arm by avoiding collisions with the object: achieving 87.94% accuracy. Furthermore, the best performing model is shown to operate with a high success rate when transferred to a real robotic arm, at high inference speeds, without the need for transfer learning. The system is then shown to be capable of performing grasps on an antagonistic physical object dataset benchmark.
Human Wellbeing and Machine Learning

There is a vast literature on the determinants of subjective wellbeing. International organisations and statistical offices are now collecting such survey data at scale. However, standard regression models explain surprisingly little of the variation in wellbeing, limiting our ability to predict it. In response, we here assess the potential of Machine Learning (ML) to help us better understand wellbeing. We analyse wellbeing data on over a million respondents from Germany, the UK, and the United States. In terms of predictive power, our ML approaches do perform better than traditional models. Although the size of the improvement is small in absolute terms, it turns out to be substantial when compared to that of key variables like health. We moreover find that drastically expanding the set of explanatory variables doubles the predictive power of both OLS and the ML approaches on unseen data. The variables identified as important by our ML algorithms - $i.e.$ material conditions, health, and meaningful social relations - are similar to those that have already been identified in the literature. In that sense, our data-driven ML results validate the findings from conventional approaches.
Accelerated first-order methods for a class of semidefinite programs

This paper introduces a new storage-optimal first-order method (FOM), CertSDP, for solving a special class of semidefinite programs (SDPs) to high accuracy. The class of SDPs that we consider, the exact QMP-like SDPs, is characterized by low-rank solutions, a priori knowledge of the restriction of the SDP solution to a small subspace, and standard regularity assumptions such as strict complementarity. Crucially, we show how to use a certificate of strict complementarity to construct a low-dimensional strongly convex minimax problem whose optimizer coincides with a factorization of the SDP optimizer. From an algorithmic standpoint, we show how to construct the necessary certificate and how to solve the minimax problem efficiently. We accompany our theoretical results with preliminary numerical experiments suggesting that CertSDP significantly outperforms current state-of-the-art methods on large sparse exact QMP-like SDPs.
The Hipster Paradox in Electronic Dance Music: How Musicians Trade Mainstream Success off against Alternative Status

The hipster paradox in Electronic Dance Music is the phenomenon that commercial success is collectively considered illegitimate while serious and aspiring professional musicians strive for it. We study this behavioral dilemma using digital traces of performing live and releasing music as they are stored in the Resident Advisor, Juno Download, and Discogs databases from 2001-2018. We construct network snapshots following a formal sociological approach based on bipartite networks, and we use network positions to explain success in regression models of artistic careers. We find evidence for a structural trade-off among autonomy and success. Musicians in EDM embed into exclusive performance-based communities for autonomy but, in earlier career stages, seek the mainstream for commercial success. Our approach highlights how Computational Social Science can benefit from a close connection of data analysis and theory.
Are there any pristine comets? Constraints from pebble structure

We show that if comets (or any small icy planetesimals such as Kuiper belt objects) are composed of pebble piles, their internal radiogenic as well as geochemical heating results in considerably different evolutionary outcomes compared to similar past studies. We utilize a 1D thermo-physical evolution code, modified to include state-of-the-art empirical measurements of pebble thermal conductivity and compression, the latter obtained through a new laboratory experiment presented here for the first time. Results indicate that due to the low pebble thermal conductivity, the peak temperatures attained during evolution are much higher than in any previous study given the same formation time. Assuming meteoritic radiogenic abundances, we find that only extremely small, sub-kilometre comets have the potential to retain the primordial, uniform and thermally unprocessed composition from which they formed. Comets with radii in excess of about 20 km are typically swept by rapid and energetically powerful aqueous hydration reactions. Across the full range of comet sizes and formation times, evolutions result in the processing and differentiation of various volatile species, and a radially heterogeneous nucleus stucture. Our computations however also indicate that the assumed fraction of radionuclides is a pivotal free parameter, because isotopic analyses of the only available cometary samples suggest that no 26Al was ever present in comet 81P/Wild 2. We show that if comets formed early in the protoplanetary disc (within 1-3 Myr), the radionuclide abundances indeed must be much smaller than those typically assumed based on meteoritic samples. We discuss the importance of our findings for the formation, present-day attributes and future research of comets.
Regularized by Physics: Graph Neural Network Parametrized Potentials for the Description of Intermolecular Interactions

Simulations of molecular systems using electronic structure methods are still not feasible for many systems of biological importance. As a result, empirical methods such as force fields (FF) have become an established tool for the simulation of large and complex molecular systems. The parametrization of FF is, however, time-consuming and has traditionally been based on experimental data. Recent years have therefore seen increasing efforts to automatize FF parametrization or to replace FF with machine-learning (ML) based potentials. Here, we propose an alternative strategy to parametrize FF, which makes use of ML and gradient-descent based optimization while retaining a functional form founded in physics. Using a predefined functional form is shown to enable interpretability, robustness, and efficient simulations of large systems over long time scales. To demonstrate the strength of the proposed method, a fixed-charge and a polarizable model are trained on ab initio potential-energy surfaces. Given only information about the constituting elements, the molecular topology, and reference potential energies, the models successfully learn to assign atom types and corresponding FF parameters from scratch. The resulting models and parameters are validated on a wide range of experimentally and computationally derived properties of systems including dimers, pure liquids, and molecular crystals.