Miracle-dz/UniFAR-FTUs huggingface dataset:
Structure and dynamics of Fe90Si3O7 liquids close to Earth's liquid core conditions

Using an artificial neural-network machine learning interatomic potential, we have performed molecular dynamics simulations to study the structure and dynamics of Fe90Si3O7 liquid close to the Earth's liquid core conditions. The simulation results reveal that the short-range structural order (SRO) in the Fe90Si3O7 liquid is very strong. About 80% of the atoms are arranged in crystalline-like SRO motifs. In particular, ~70% of Fe-centered clusters can be classified as either hexagonal-close-pack (HCP/HCP-like) or icosahedral (ICO/ICO-like) SRO motifs. The SRO clusters centered on Fe, Si, or O atoms are strongly intermixed and homogenously distributed throughout the liquid. The atomic structure of the liquid and the fractions of dominant SRO clusters are not sensitive to pressure/temperature used in the simulations except that the SRO of the O-centered clusters is enhanced close to inner core pressures. The O diffusion coefficient is about 2-3 times larger than the Fe and Si ions and increases more rapidly in the deeper core regions.
A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism

Autism spectrum disorder (autism) is a neurodevelopmental delay that affects at least 1 in 44 children. Like many neurological disorder phenotypes, the diagnostic features are observable, can be tracked over time, and can be managed or even eliminated through proper therapy and treatments. However, there are major bottlenecks in the diagnostic, therapeutic, and longitudinal tracking pipelines for autism and related neurodevelopmental delays, creating an opportunity for novel data science solutions to augment and transform existing workflows and provide increased access to services for affected families. Several efforts previously conducted by a multitude of research labs have spawned great progress toward improved digital diagnostics and digital therapies for children with autism. We review the literature on digital health methods for autism behavior quantification and beneficial therapies using data science. We describe both case-control studies and classification systems for digital phenotyping. We then discuss digital diagnostics and therapeutics that integrate machine learning models of autism-related behaviors, including the factors that must be addressed for translational use. Finally, we describe ongoing challenges and potential opportunities for the field of autism data science. Given the heterogeneous nature of autism and the complexities of the relevant behaviors, this review contains insights that are relevant to neurological behavior analysis and digital psychiatry more broadly. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 6 is August 2023. Please see http://www.annualreviews. org/page/journal/pubdates for revised estimates.
Exact coefficients of finite-size corrections in the Ising model with Brascamp–Kunz boundary conditions and their relationships for strip and cylindrical geometries

We derive exact finite-size corrections for the free energy F of the Ising model on the square lattice with Brascamp–Kunz boundary conditions. We calculate ratios rp(ρ) of pth coefficients of F for the infinitely long cylinder ( ) and the infinitely long Brascamp–Kunz strip ( ) at varying values of the aspect ratio . Like previous studies have shown for the two-dimensional dimer model, the limiting values p→∞ of rp(ρ) exhibit abrupt anomalous behavior at certain values of ρ. These critical values of ρ and the limiting values of the finite-size-expansion-coefficient ratios differ, however, between the two models.
Wind Turbine Gearbox Fault Detection Based on Sparse Filtering and Graph Neural Networks

The wind energy industry has been experiencing tremendous growth and confronting the failures of wind turbine components. Wind turbine gearbox malfunctions are particularly prevalent and lead to the most prolonged downtime and highest cost. This paper presents a data-driven gearbox fault detection algorithm base on high frequency vibration data using graph neural network (GNN) models and sparse filtering (SF). The approach can take advantage of the comprehensive data sources and the complicated sensing networks. The GNN models, including basic graph neural networks, gated graph neural networks, and gated graph sequential neural networks, are used to detect gearbox condition from knowledge-based graphs formed using wind turbine information. Sparse filtering is used as an unsupervised feature learning method to accelerate the training of the GNN models. The effectiveness of the proposed method was verified on practical experimental data.
On the jets ejected after the inertial collapse of cavities

Motivated by the results in Gordillo and Blanco-Rodriguez, 'Bubble bursting jets are driven by the purely inertial collapse of gas cavities', \emph{Phys. Rev. Lett. , Submitted} (2023) \cite{PRL2023}, where it is found that bubble bursting jets are driven by a purely inertial mechanism, here we present a study on the dynamics of the jets produced by the collapse of gas cavities of generic shape when the implosion is forced by a far field boundary condition expressing that the flow rate per unit length, $q_\infty$, remains constant in time. Making use of theory and of numerical simulations, we first analyze the case of a conical bubble with a half-opening angle $\beta$ when the value of $q_\infty$ is fixed to a constant, finding that this type of jets converge towards a purely inertial $\beta$-dependent self-similar solution of the equations in which the jet width and velocity are respectively given, in the limit $\beta\ll 1$, by $r_{jet}\approx 2.25\tan\beta\sqrt{q_\infty\tau}$ and $v_{jet}\approx 3 q_\infty/(2\tan\beta\sqrt{q_\infty\tau})$ respectively, with $\tau$ indicating the dimensionless time after the jet is ejected. For the case of parabolic cavities with a dimensionless radius of curvature at the plane of symmetry $r_c$ our theory predicts that $r_{jet}\propto \left(2 r_c\right)^{-1/2}\left(q_\infty \tau\right)^{3/4}$ and $v_{jet}\propto q_\infty \left(2r_c\right)^{1/2} \left(q_\infty\tau\right)^{-3/4}$, a result which is also in good agreement with numerical simulations. The present results might find applications in the description of the very fast jets, with velocities reaching up to $1000$ m s$^{-1}$, produced after a bubble cavitates very close to a wall and in the quantification of the so-called bazooka effect.
Homological representations of low genus mapping class groups

Given a finite group $G$ acting on a surface $S$, the centralizer of G in the mapping class group $\textrm{Mod}(S)$ has a natural representation given by its action on the homology $H_1(S; \mathbb{Q})$. We consider the question of whether this representation has arithmetic image. Several authors have given positive and negative answers to this question. We give a complete answer when S has genus at most 3.
Zigzag Edge States in Graphene in the Presence of In‐Plane Electric Field

Herein, the edge states in a finite‐width graphene ribbon and a semi‐infinite geometry subject to a perpendicular magnetic field and an in‐plane electric field, applied perpendicular to a zigzag edge, are explored. To accomplish this, a combination of analytic and numerical methods within the framework of low‐energy effective theory is employed. Both the gapless and gapped Dirac fermions in graphene are considered. It is found that a surface mode localized at the zigzag edge remains dispersionless even in the presence of electric field. This is shown analytically by employing Darwin's expansion of the parabolic cylinder functions of large order and argument.
Multiparameter Radar Study of a Microburst: Comparison with Model Results

Abstract Radar observations and model results are used to investigate the microphysical evolution of an isolated, intense storm observed on 20 July during the Microburst and Severe Thunderstorm (MIST) experiment. The storm grew to a height of 14 km and upon collapsing, produced heavy rain, pea-sized hail, and a microburst at the surface. The storm was observed by three Doppler radars and one of the radars was equipped to collect differential reflectivity (ZDR) and dual-frequency measurements. The radar observations indicate that the initial precipitation development was by collision-coalescence. Later, as the storm intensified, accretional growth became dominant leading to rapid precipitation development. Radar-derived rainfall rates peaked around 150 to 190 mm h−1. The microburst developed as the precipitation core descended to the surface and was likely initiated by a combination of mass loading and cooling due to melting. Each morning during the experiment, a two-dimensional, time-dependent cloud model.
Energy-Flexible Network (EF-Net) for Dual-Energy CT Image Reconstruction

In dual-energy computed tomography (DECT), the X-ray tube energy pair often changes depending on the target organ or patient obesity. In practice, it makes difficult to apply deep learning (DL) based algorithms for image reconstruction since most of the existing DL-based algorithms are trained to be used for dedicated X-ray tube energies. In this paper, we propose 1) an energy flexibility training (EFT) method, which makes a network applicable for data measured at various X-ray tube energies between two trained energies, and 2) an effective dual-domain convolutional neural network for image reconstruction. The proposed network is derived from the regularized version of the primal-dual hybrid gradient algorithm, so its architecture has an unfolded iterative dual-domain structure. For validation, we generated datasets from a lab-made polychromatic X-ray simulator. The proposed method showed promising results not only at the trained X-ray tube energies but also at untrained X-ray tube energies outperforming an iterative algorithm and other DL-based algorithms.
The analysis of indexed astronomical time series. II: The O-C (observed-calculated) technique reconsidered

Circumstances under which application of the O-C technique leads to spurious conclusions are described. For data in which measurements of many individual cycle lengths are available, use of the CUSUM method is a good alternative. The contentions of the paper are illustrated by application to observations of RY Sgr:in contrast to several previous authors, it is concluded that the mean pulsation period of the star is not changing
Tangent, cotangent, normal and conormal bundles are almost never instanton bundles

In this very short note we give an elementary characteristic free proof of the result claimed in the title (see Theorem 1.2 for a more precise formulation), generalizing a recent result proved for Ulrich bundles over the complex field by V. Benedetti, P. Montero, Y. Prieto Monta\~nez, S. Troncoso. Moreover, we also give a similar result about the twists of the cotangent bundle and make some comments about the possibility to obtain an analogous result for twists of the tangent bundle.
Distribution of roundoff noise in binary floating-point addition

A method for deriving the distribution of relative round off error in addition of two binary floating-point numbers is introduced. The distribution is needed in the calculation of variance of the relative roundoff error in floating-point addition. The resulting distribution depends on the statistics of the addends and the rounding strategy chosen. As an example, the distribution is derived for the addition of two uncorrelated numbers from Gaussian distribution. The result was verified by simulation. <<ETX>>
Safe Importance Sampling in Model Predictive Path Integral Control

We introduce the notion of importance sampling under embedded barrier state control, titled Safety Controlled Model Predictive Path Integral Control (SC-MPPI). For robotic systems operating in an environment with multiple constraints, hard constraints are often encoded utilizing penalty functions when performing optimization. Alternative schemes utilizing optimization-based techniques, such as Control Barrier Functions, can be used as a safety filter to ensure the system does not violate the given hard constraints. In contrast, this work leverages the principle of a safety filter but applies it during forward sampling for Model Predictive Path Integral Control. The resulting set of forward samples can remain safe within the domain of the safety controller, increasing sample efficiency and allowing for improved exploration of the state space. We derive this controller through information theoretic principles analogous to Information Theoretic MPPI. We empirically demonstrate both superior sample efficiency, exploration, and system performance of SC-MPPI when compared to Model-Predictive Path Integral Control (MPPI) and Differential Dynamic Programming (DDP) optimizing the barrier state.
Environment Transformer and Policy Optimization for Model-Based Offline Reinforcement Learning

Interacting with the actual environment to acquire data is often costly and time-consuming in robotic tasks. Model-based offline reinforcement learning (RL) provides a feasible solution. On the one hand, it eliminates the requirements of interaction with the actual environment. On the other hand, it learns the transition dynamics and reward function from the offline datasets and generates simulated rollouts to accelerate training. Previous model-based offline RL methods adopt probabilistic ensemble neural networks (NN) to model aleatoric uncertainty and epistemic uncertainty. However, this results in an exponential increase in training time and computing resource requirements. Furthermore, these methods are easily disturbed by the accumulative errors of the environment dynamics models when simulating long-term rollouts. To solve the above problems, we propose an uncertainty-aware sequence modeling architecture called Environment Transformer. It models the probability distribution of the environment dynamics and reward function to capture aleatoric uncertainty and treats epistemic uncertainty as a learnable noise parameter. Benefiting from the accurate modeling of the transition dynamics and reward function, Environment Transformer can be combined with arbitrary planning, dynamics programming, or policy optimization algorithms for offline RL. In this case, we perform Conservative Q-Learning (CQL) to learn a conservative Q-function. Through simulation experiments, we demonstrate that our method achieves or exceeds state-of-the-art performance in widely studied offline RL benchmarks. Moreover, we show that Environment Transformer's simulated rollout quality, sample efficiency, and long-term rollout simulation capability are superior to those of previous model-based offline RL methods.
Revisiting scalar field cosmology with $\Lambda(t)$ in $f(R,T)$ gravity

Two scalar field cosmological models in $f(R,T)$ gravity with cosmic transit and varying cosmological constant have been investigated. The time-dependent cosmological constant $\Lambda(t)$ is positive and tends to a very small value at the present epoch. The scalar field pressure $p_{\phi}$ shows a sign flipping for normal scalar field. For the phantom field, the scalar potential $V(\phi)$ is negative and the energy density $\rho_{\phi}=E_k+V$ takes negative values when the equation of state parameter $\omega_{\phi}<-1$. While the weak energy condition WEC implies that the total energy density $\rho=\sum_i\rho_i\geq 0$, we still can have a negative $\rho$ term as long as the total $\rho$ is positive. In the current work we argue that the WEC, $\rho=\sum_i \rho_i \geq 0$ and $p_i+\rho_i \geq 0$, is not violated. For a scalar field $\phi$, The condition $\rho_{\phi}+p_{\phi}=\rho_{\phi} (1+\omega_{\phi})=2E_k\geq 0$ allows for $\rho_{\phi}<0$ if $\omega_{\phi}<-1$. The cosmology in both models has been probed using a given function $a(t)$ deduced from the desired cosmic behavior which is the opposite of the conventional viewpoint.
Parma: Confidential Containers via Attested Execution Policies

Container-based technologies empower cloud tenants to develop highly portable software and deploy services in the cloud at a rapid pace. Cloud privacy, meanwhile, is important as a large number of container deployments operate on privacy-sensitive data, but challenging due to the increasing frequency and sophistication of attacks. State-of-the-art confidential container-based designs leverage process-based trusted execution environments (TEEs), but face security and compatibility issues that limits their practical deployment. We propose Parma, an architecture that provides lift-and-shift deployment of unmodified containers while providing strong security protection against a powerful attacker who controls the untrusted host and hypervisor. Parma leverages VM-level isolation to execute a container group within a unique VM-based TEE. Besides container integrity and user data confidentiality and integrity, Parma also offers container attestation and execution integrity based on an attested execution policy. Parma execution policies provide an inductive proof over all future states of the container group. This proof, which is established during initialization, forms a root of trust that can be used for secure operations within the container group without requiring any modifications of the containerized workflow itself (aside from the inclusion of the execution policy.) We evaluate Parma on AMD SEV-SNP processors by running a diverse set of workloads demonstrating that workflows exhibit 0-26% additional overhead in performance over running outside the enclave, with a mean 13% overhead on SPEC2017, while requiring no modifications to their program code. Adding execution policies introduces less than 1% additional overhead. Furthermore, we have deployed Parma as the underlying technology driving Confidential Containers on Azure Container Instances.
Expectiles In Risk Averse Stochastic Programming and Dynamic Optimization

This paper features expectiles in dynamic and stochastic optimization. Expectiles are a family of risk functionals characterized as minimizers of optimization problems. For this reason, they enjoy various unique stability properties, which can be exploited in risk averse management, in stochastic optimization and in optimal control. The paper provides tight relates of expectiles to other risk functionals and addresses their properties in regression. Further, we extend expectiles to a dynamic framework. As such, they allow incorporating a risk averse aspect in continuous-time dynamic optimization and a risk averse variant of the Hamilton-Jacobi-Bellman equations.
Simultaneous Recursive Identification of Parameters and Switching Manifolds Identification of Discrete-Time Switched Linear Systems

A novel procedure for the online identification of a class of discrete-time switched linear systems, which simultaneously estimates the parameters and switching manifolds of the systems, is proposed in this paper. Firstly, to estimate the parameters of the subsystems, a discrete-time concurrent learning-based recursive parameter estimator is designed to guarantee the exponential convergence of the estimation errors to zero. Secondly, as an assistant procedure of the identification framework, an online switching detection method is proposed by making use of the history stacks produced by the concurrent learning estimators. Thirdly, techniques of incremental support vector machine are applied to develop the recursive algorithm to estimate the system switching manifolds, with its stability proven by a Lynapunov-based method. At the end of the paper, the stability and precision of the proposed identification methods are confirmed by the numerical simulation of a 2-order switched linear system. Compared to the traditional offline identification methods, the proposed online identification framework possesses superior efficiency with respect to large amounts of data, while the limitations and outlook of this framework are also discussed within the conclusion.
External Camera-Based Mobile Robot Pose Estimation for Collaborative Perception with Smart Edge Sensors

We present an approach for estimating a mobile robot's pose w.r. t. the allocentric coordinates of a network of static cameras using multi-view RGB images. The images are processed online, locally on smart edge sensors by deep neural networks to detect the robot and estimate 2D keypoints defined at distinctive positions of the 3D robot model. Robot keypoint detections are synchronized and fused on a central backend, where the robot's pose is estimated via multi-view minimization of reprojection errors. Through the pose estimation from external cameras, the robot's localization can be initialized in an allocentric map from a completely unknown state (kidnapped robot problem) and robustly tracked over time. We conduct a series of experiments evaluating the accuracy and robustness of the camera-based pose estimation compared to the robot's internal navigation stack, showing that our camera-based method achieves pose errors below 3 cm and 1° and does not drift over time, as the robot is localized allocentrically. With the robot's pose precisely estimated, its observations can be fused into the allocentric scene model. We show a real-world application, where observations from mobile robot and static smart edge sensors are fused to collaboratively build a 3D semantic map of a ~240 m2 indoor environment.
Variational Inference for Neyman-Scott Processes

Neyman-Scott processes (NSPs) have been applied across a range of fields to model points or temporal events with a hierarchy of clusters. Markov chain Monte Carlo (MCMC) is typically used for posterior sampling in the model. However, MCMC's mixing time can cause the resulting inference to be slow, and thereby slow down model learning and prediction. We develop the first variational inference (VI) algorithm for NSPs, and give two examples of suitable variational posterior point process distributions. Our method minimizes the inclusive Kullback-Leibler (KL) divergence for VI to obtain the variational parameters. We generate samples from the approximate posterior point processes much faster than MCMC, as we can directly estimate the approximate posterior point processes without any MCMC steps or gradient descent. We include synthetic and real-world data experiments that demonstrate our VI algorithm achieves better prediction performance than MCMC when computational time is limited.
Predicted Embedding Power Regression for Large-Scale Out-of-Distribution Detection

Out-of-distribution (OOD) inputs can compromise the performance and safety of real world machine learning systems. While many methods exist for OOD detection and work well on small scale datasets with lower resolution and few classes, few methods have been developed for large-scale OOD detection. Existing large-scale methods generally depend on maximum classification probability, such as the state-of-the-art grouped softmax method. In this work, we develop a novel approach that calculates the probability of the predicted class label based on label distributions learned during the training process. Our method performs better than current state-of-the-art methods with only a negligible increase in compute cost. We evaluate our method against contemporary methods across $14$ datasets and achieve a statistically significant improvement with respect to AUROC (84.2 vs 82.4) and AUPR (96.2 vs 93.7).