Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular Dynamics Simulation of Stationary and Rotating Nanotube in Uniform Liquid Argon Flow

In this paper molecular dynamics (MD) simulation is used to investigate the liquid argon flow past a stationary and rotating carbon nanotube. The main purpose of this work is to estimate flow forces on the nanotube and compare them with classical continuum results. The simulation is 3D and consists of 33,700 liquid argon atoms as fluid and 240 atoms of carbon as the nanotube. The single walled nanotube is simulated as a rigid cylinder of fixed carbon atoms. For simulation of rotating carbon nanotube, carbon atoms are rotated around center axes of the nanotube in each times step according to the desired angular velocity. Both argon-argon and carbon-argon interactions are modeled by Lennard-Jones potential function. Periodic boundary condition is used for the whole system. Flow is driven by rescaling velocities at the inlet each 50 time steps. The results show that the rotation of nanotube causes a reduction in drag force, up to rotation rate of 3.0 where the drag force is about 78% of the stationary one. Above the rotation rate of 3.0 drag coefficient is almost constant. Lift coefficient of stationary nanotube is negligible in comparison with drag coefficient and the rotation of nanotube has a little influence on the lift coefficient.
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations

The evaluation of explanation methods is a research topic that has not yet been explored deeply, however, since explainability is supposed to strengthen trust in artificial intelligence, it is necessary to systematically review and compare explanation methods in order to confirm their correctness. Until now, no tool with focus on XAI evaluation exists that exhaustively and speedily allows researchers to evaluate the performance of explanations of neural network predictions. To increase transparency and reproducibility in the field, we therefore built Quantus -- a comprehensive, evaluation toolkit in Python that includes a growing, well-organised collection of evaluation metrics and tutorials for evaluating explainable methods. The toolkit has been thoroughly tested and is available under an open-source license on PyPi (or on https://github.com/understandable-machine-intelligence-lab/Quantus/).
Individual-Level Inverse Reinforcement Learning for Mean Field Games

The recent mean field game (MFG) formalism has enabled the application of inverse reinforcement learning (IRL) methods in large-scale multi-agent systems, with the goal of inferring reward signals that can explain demonstrated behaviours of large populations. The existing IRL methods for MFGs are built upon reducing an MFG to a Markov decision process (MDP) defined on the collective behaviours and average rewards of the population. However, this paper reveals that the reduction from MFG to MDP holds only for the fully cooperative setting. This limitation invalidates existing IRL methods on MFGs with non-cooperative environments. To measure more general behaviours in large populations, we study the use of individual behaviours to infer ground-truth reward functions for MFGs. We propose Mean Field IRL (MFIRL), the first dedicated IRL framework for MFGs that can handle both cooperative and non-cooperative environments. Based on this theoretically justified framework, we develop a practical algorithm effective for MFGs with unknown dynamics. We evaluate MFIRL on both cooperative and mixed cooperative-competitive scenarios with many agents. Results demonstrate that MFIRL excels in reward recovery, sample efficiency and robustness in the face of changing dynamics.
A Survey on Programmatic Weak Supervision

Labeling training data has become one of the major roadblocks to using machine learning. Among various weak supervision paradigms, programmatic weak supervision (PWS) has achieved remarkable success in easing the manual labeling bottleneck by programmatically synthesizing training labels from multiple potentially noisy supervision sources. This paper presents a comprehensive survey of recent advances in PWS. In particular, we give a brief introduction of the PWS learning paradigm, and review representative approaches for each component within PWS's learning workflow. In addition, we discuss complementary learning paradigms for tackling limited labeled data scenarios and how these related approaches can be used in conjunction with PWS. Finally, we identify several critical challenges that remain under-explored in the area to hopefully inspire future research directions in the field.
Long-Time Convergence and Propagation of Chaos for Nonlinear MCMC

In this paper, we study the long-time convergence and uniform strong propagation of chaos for a class of nonlinear Markov chains introduced in Andrieu et al. (2011) for Markov chain Monte Carlo (MCMC). Our technique is quite simple, making use of recent contraction estimates for linear Markov kernels (Hairer and Mattingly, 2011) and basic techniques from Markov theory and analysis. Moreover, the same proof strategy applies to both the long-time convergence and propagation of chaos. We also show, via some experiments, that these nonlinear MCMC techniques are viable for use in real-world high-dimensional inference such as Bayesian neural networks.
Survey for Distant Solar Twins (SDST) – I. EPIC method for stellar parameter measurement

Solar twins are stars of key importance to the field of astronomy and offer a multitude of science applications. Only a small number (≲ 200) of solar twins are known today, all of which are relatively close to our Sun (≲ 800 pc). The goal of our Survey for Distant Solar Twins (SDST) is to identify many more solar twin and solar analogue stars out to much larger distances (∼4 kpc). In this paper, we present a new method to identify solar twins using relatively low S/N, medium resolving power (R ∼ 28 000) spectra that will be typical of such distant targets observed with HERMES on the 3.9 m Anglo-Australian Telescope (AAT). We developed a novel approach, namely EPIC, to measure stellar parameters which we use to identify stars similar to our Sun. EPIC determines the stellar atmospheric parameters (effective temperature Teff, surface gravity log g and metallicity [Fe/H]) using differential equivalent width (EW) measurements of selected spectroscopic absorption features and a simple model, trained on previously analysed spectra, that connects these EWs to the stellar parameters. The reference for the EW measurements is a high S/N solar spectrum which is used to minimize several systematic effects. EPIC is fast, optimised for Sun-like stars and yields stellar parameter measurements with small enough uncertainties to enable spectroscopic identification of solar twin and analogue stars up to ∼4 kpc away using AAT/HERMES, i.e. σ(Teff, log g, [Fe/H]) = (50 K, 0.08 dex, 0.03 dex) on average at S/N = 25.
Confident AI

In this paper, we propose"Confident AI"as a means to designing Artificial Intelligence (AI) and Machine Learning (ML) systems with both algorithm and user confidence in model predictions and reported results. The 4 basic tenets of Confident AI are Repeatability, Believability, Sufficiency, and Adaptability. Each of the tenets is used to explore fundamental issues in current AI/ML systems and together provide an overall approach to Confident AI.
Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learning

When people receive advice while making difficult decisions, they often make better decisions in the moment and also increase their knowledge in the process. However, such incidental learning can only occur when people cognitively engage with the information they receive and process this information thoughtfully. How do people process the information and advice they receive from AI, and do they engage with it deeply enough to enable learning? To answer these questions, we conducted three experiments in which individuals were asked to make nutritional decisions and received simulated AI recommendations and explanations. In the first experiment, we found that when people were presented with both a recommendation and an explanation before making their choice, they made better decisions than they did when they received no such help, but they did not learn. In the second experiment, participants first made their own choice, and only then saw a recommendation and an explanation from AI; this condition also resulted in improved decisions, but no learning. However, in our third experiment, participants were presented with just an AI explanation but no recommendation and had to arrive at their own decision. This condition led to both more accurate decisions and learning gains. We hypothesize that learning gains in this condition were due to deeper engagement with explanations needed to arrive at the decisions. This work provides some of the most direct evidence to date that it may not be sufficient to include explanations together with AI-generated recommendation to ensure that people engage carefully with the AI-provided information. This work also presents one technique that enables incidental learning and, by implication, can help people process AI recommendations and explanations more carefully.
Adaptive Control With Guaranteed Transient Behavior and Zero Steady-State Error for Systems With Time-Varying Parameters

It is nontrivial to achieve global zero-error regulation for uncertain nonlinear systems. The underlying problem becomes even more challenging if mismatched uncertainties and unknown time-varying control gain are involved, yet certain performance specifications are also pursued. In this work, we present an adaptive control method, which, without the persistent excitation (PE) condition, is able to ensure global zero-error regulation with guaranteed output performance for parametric strict-feedback systems involving fast time-varying parameters in the feedback path and input path. The development of our control scheme benefits from generalized $t$-dependent and $x$-dependent functions, a novel coordinate transformation and “congelation of variables” method. Both theoretical analysis and numerical simulation verify the effectiveness and benefits of the proposed method.
Predictive modeling of microbiological seawater quality classification in karst region using cascade model

In this paper, an in-depth analysis of Escherichia coli seawater measurements during the bathing season in the city of Rijeka, Croatia was conducted. Submerged sources of groundwater were observed at several measurement locations which could be the cause for increased E. coli values. This specificity of karst terrain is usually not considered during the monitoring process, thus a novel measurement methodology is proposed. A cascade machine learning model is used to predict coastal water quality based on meteorological data, which improves the level of accuracy due to data imbalance resulting from rare occurrences of measurements with reduced water quality. Currently, the cascade model is employed as a filter method, where measurements not classified as excellent quality need to be further analyzed. However, with improvements proposed in the paper, the cascade model could be ultimately used as a standalone method.
Policy mix and the impact on PV technologies and industry: The challenge - how to make policies quantifiable?

In the framework of the research project (Gretchen) financed by the German Federal Ministry of Education and Research, the impact of German policies on industry structure and technological change is investigated. As the quantitative impact analysis of German policies on technologies and structures is complex and requires a comprehensive approach, this paper focuses on a selected part of the analysis: It explores and discusses the possibilities of how to quantify and operationalize German policies addressing photovoltaic electricity generation. This is done for the case of PV. The impact analysis of the policies will be discussed in a separate paper.
Molecule Generation from Input-Attributions over Graph Convolutional Networks

It is well known that Drug Design is often a costly process both in terms of time and economic effort. While good Quantitative Structure-Activity Relationship models (QSAR) can help predicting molecular properties without the need to synthesize them, it is still required to come up with new molecules to be tested. This is mostly done in lack of tools to determine which modifications are more promising or which aspects of a molecule are more influential for the final activity/property. Here we present an automatic process which involves Graph Convolutional Network models and input-attribution methods to generate new molecules. We also explore the problems of over-optimization and applicability, recognizing them as two important aspects in the practical use of such automatic tools.
Fast algorithm for overcomplete order-3 tensor decomposition

We develop the first fast spectral algorithm to decompose a random third-order tensor over R of rank up to O(d/ polylog(d)). Our algorithm only involves simple linear algebra operations and can recover all components in time O(d) under the current matrix multiplication time. Prior to this work, comparable guarantees could only be achieved via sum-of-squares [Ma, Shi, Steurer 2016]. In contrast, fast algorithms [Hopkins, Schramm, Shi, Steurer 2016] could only decompose tensors of rank at most O(d/ polylog(d)). Our algorithmic result rests on two key ingredients. A clean lifting of the third-order tensor to a sixth-order tensor, which can be expressed in the language of tensor networks. A careful decomposition of the tensor network into a sequence of rectangular matrix multiplications, which allows us to have a fast implementation of the algorithm.
Robust Estimation of Discrete Distributions under Local Differential Privacy

Although robust learning and local differential privacy are both widely studied fields of research, combining the two settings is just starting to be explored. We consider the problem of estimating a discrete distribution in total variation from $n$ contaminated data batches under a local differential privacy constraint. A fraction $1-\epsilon$ of the batches contain $k$ i.i. d. samples drawn from a discrete distribution $p$ over $d$ elements. To protect the users' privacy, each of the samples is privatized using an $\alpha$-locally differentially private mechanism. The remaining $\epsilon n $ batches are an adversarial contamination. The minimax rate of estimation under contamination alone, with no privacy, is known to be $\epsilon/\sqrt{k}+\sqrt{d/kn}$, up to a $\sqrt{\log(1/\epsilon)}$ factor. Under the privacy constraint alone, the minimax rate of estimation is $\sqrt{d^2/\alpha^2 kn}$. We show that combining the two constraints leads to a minimax estimation rate of $\epsilon\sqrt{d/\alpha^2 k}+\sqrt{d^2/\alpha^2 kn}$ up to a $\sqrt{\log(1/\epsilon)}$ factor, larger than the sum of the two separate rates. We provide a polynomial-time algorithm achieving this bound, as well as a matching information theoretic lower bound.
Cosmic Kite: Auto-encoding the Cosmic Microwave Background

In this work we present the results of the study of the cosmic microwave background TT power spectrum through autoencoders in which the latent variables are the cosmological parameters. This method was trained and calibrated using a data-set composed by 80000 power spectra from random cosmologies computed numerically with the CAMB code. Due to the specific architecture of the auto-encoder, the encoder part is a model that estimates the maximum-likelihood parameters from a given power spectrum. On the other hand, the decoder part is a model that computes the power spectrum from the cosmological parameters and can be used as a forward model in a fully Bayesian analysis. We show that the encoder is able to estimate the true cosmological parameters with a precision varying from ≈ 0.004% to ≈ 0.2% (depending on the cosmological parameter), while the decoder computes the power spectra with a mean percentage error of ≈ 0.0018% for all the multipole range. We also demonstrate that the decoder recovers the expected trends when varying the cosmological parameters one by one, and that it does not introduce any significant bias on the estimation of cosmological parameters through a Bayesian analysis. These studies gave place to the Cosmic Kite python software that is publicly available and can be downloaded and installed from https://github.com/Martindelosrios/cosmic-kite. Although this algorithm does not improve the precision of the measurements compared with the traditional methods, it reduces significantly the computation time and represents the first attempt towards forcing the latent variables to have a physical interpretation.
Tensor Moments of Gaussian Mixture Models: Theory and Applications

Gaussian mixture models (GMMs) are fundamental tools in statistical and data sciences. We study the moments of multivariate Gaussians and GMMs. The $d$-th moment of an $n$-dimensional random variable is a symmetric $d$-way tensor of size $n^d$, so working with moments naively is assumed to be prohibitively expensive for $d>2$ and larger values of $n$. In this work, we develop theory and numerical methods for \emph{implicit computations} with moment tensors of GMMs, reducing the computational and storage costs to $\mathcal{O}(n^2)$ and $\mathcal{O}(n^3)$, respectively, for general covariance matrices, and to $\mathcal{O}(n)$ and $\mathcal{O}(n)$, respectively, for diagonal ones. We derive concise analytic expressions for the moments in terms of symmetrized tensor products, relying on the correspondence between symmetric tensors and homogeneous polynomials, and combinatorial identities involving Bell polynomials. The primary application of this theory is to estimating GMM parameters (means and covariances) from a set of observations, when formulated as a moment-matching optimization problem. If there is a known and common covariance matrix, we also show it is possible to debias the data observations, in which case the problem of estimating the unknown means reduces to symmetric CP tensor decomposition. Numerical results validate and illustrate the numerical efficiency of our approaches. This work potentially opens the door to the competitiveness of the method of moments as compared to expectation maximization methods for parameter estimation of GMMs.
The short ionizing photon mean free path at z=6 in Cosmic Dawn III, a new fully-coupled radiation-hydrodynamical simulation of the Epoch of Reionization

Recent determinations of the mean free path of ionising photons (mfp) in the intergalactic medium (IGM) at $\rm z=6$ are lower than many theoretical predictions. In order to gain insight, we investigate the evolution of the mfp in our new massive fully coupled radiation hydrodynamics cosmological simulation of reionization: Cosmic Dawn III (CoDa III). CoDa III’s scale ($\rm 94^3 \, cMpc^3$) and resolution ($\rm 8192^3$ grid) make it particularly suitable to study the IGM during reionization. The simulation was performed with RAMSES-CUDATON on Summit, and used 131072 processors coupled to 24576 GPUs, making it the largest reionization simulation, and largest ever RAMSES simulation. A superior agreement with global constraints on reionization is obtained in CoDa III over CoDa II, especially for the evolution of the neutral hydrogen fraction and the cosmic photo-ionization rate, thanks to an improved calibration, later end of reionization ($\rm z=5.6$), and higher spatial resolution. Analyzing the mfp, we find that CoDa III reproduces the most recent observations very well, from $\rm z=6$ to $\rm z=4.6$. We show that the distribution of the mfp in CoDa III is bimodal, with short (neutral) and long (ionized) mfp modes, due to the patchiness of reionization and the co-existence of neutral versus ionized regions during reionization. The neutral mode peaks at sub-kpc to kpc scales of mfp, while the ionized mode peak evolves from $\rm 0.1 Mpc/h$ at $\rm z=7$ to ∼10 Mpc/h at $\rm z=5.2$. Computing the mfp as the average of the ionized mode provides the best match to the recent observational determinations. The distribution reduces to a single neutral (ionized) mode at $\rm z>13$ ($\rm z<5$).
Meta-learning with GANs for anomaly detection, with deployment in high-speed rail inspection system

Anomaly detection has been an active research area with a wide range of potential applications. Key challenges for anomaly detection in the AI era with big data include lack of prior knowledge of potential anomaly types, highly complex and noisy background in input data, scarce abnormal samples, and imbalanced training dataset. In this work, we propose a meta-learning framework for anomaly detection to deal with these issues. Within this framework, we incorporate the idea of generative adversarial networks (GANs) with appropriate choices of loss functions including structural similarity index measure (SSIM). Experiments with limited labeled data for high-speed rail inspection demonstrate that our meta-learning framework is sharp and robust in identifying anomalies. Our framework has been deployed in five high-speed railways of China since 2021: it has reduced more than 99.7% workload and saved 96.7% inspection time.
Robust policy selection and harvest risk quantification for natural resources management under model uncertainty

In this work the problem of optimal harvesting policy selection for natural resources management under model uncertainty is investigated. Under the framework of the neoclassical growth model dynamics, the associated optimal control problem is investigated by introducing the concept of model uncertainty on the initial conditions of the operational procedure. At this stage, the notion of convex risk measures, and in particular the class of Fréchet risk measures, is employed in order to quantify the total operational and marginal risk, whereas simultaneously obtaining robust to model uncertainty harvesting strategies.
Describing image focused in cognitive and visual details for visually impaired people: An approach to generating inclusive paragraphs

Several services for people with visual disabilities have emerged recently due to achievements in Assistive Technologies and Artificial Intelligence areas. Despite the growth in assistive systems availability, there is a lack of services that support specific tasks, such as understanding the image context presented in online content, e.g., webinars. Image captioning techniques and their variants are limited as Assistive Technologies as they do not match the needs of visually impaired people when generating specific descriptions. We propose an approach for generating context of webinar images combining a dense captioning technique with a set of filters, to fit the captions in our domain, and a language model for the abstractive summary task. The results demonstrated that we can produce descriptions with higher interpretability and focused on the relevant information for that group of people by combining image analysis methods and neural language models.
Nonlinear biphasic mixture model: existence and uniqueness results

This paper is concerned with the development and analysis of a mathematical model that is motivated by interstitial hydrodynamics and tissue deformation mechanics (poro-elasto-hydrodynamics) within an in-vitro solid tumor. The classical mixture theory is adopted for mass and momentum balance equations for a two-phase system. A main contribution of this study, we treat the physiological transport parameter (i.e., hydraulic resistivity) as anisotropic and heterogeneous, thus the governing system is strongly coupled and nonlinear. We derived a weak formulation and then formulated the equivalent fixed-point problem. This enabled us to use the Galerkin method, and the classical results on monotone operators combined with the well-known Schauder and Banach fixed point theorems to prove the existence and uniqueness results.