Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved assessment of the statistical stability of turbulent flows using extended Orr–Sommerfeld stability analysis

Abstract The concept of statistical stability is central to Malkus's 1956 attempt to predict the mean profile in shear flow turbulence. Here we discuss how his original attempt to assess this – an Orr–Sommerfeld (OS) analysis on the mean profile – can be improved by considering a cumulant expansion of the Navier–Stokes equations. Focusing on the simplest non-trivial closure (commonly referred to as CE2) that corresponds to the quasilinearized Navier–Stokes equations, we develop an extended OS analysis that also incorporates information about the fluctuation field. A more practical version of this – minimally extended OS analysis – is identified and tested on a number of statistically steady and, therefore, statistically stable turbulent channel flows. Beyond the concept of statistical stability, this extended stability analysis should also improve the popular approach of mean flow linear analysis in time-dependent shear flows by including more information about the underlying flow in its predictions as well as for other flows with additional physics such as convection.
Control of a Soft Robotic Arm Using a Piecewise Universal Joint Model

The 'infinite' passive degrees of freedom of soft robotic arms render their control especially challenging. In this paper, we leverage a previously developed model, which drawing equivalence of the soft arm to a series of universal joints, to design two closed-loop controllers: a configuration space controller for trajectory tracking and a task space controller for position control of the end effector. Extensive experiments and simulations on a four-segment soft arm attest to substantial improvement in terms of: a) superior tracking accuracy of the configuration space controller and b) reduced settling time and steady-state error of the task space controller. The task space controller is also verified to be effective in the presence of interactions between the soft arm and the environment.
Robotic Laser Orientation Planning with a 3D Data-driven Method

This paper focuses on a research problem of robotic controlled laser orientation to minimize errant overcutting of healthy tissue during the course of pathological tissue resection. Laser scalpels have been widely used in surgery to remove pathological tissue targets such as tumors or other lesions. However, different laser orientations can create various tissue ablation cavities, and incorrect incident angles can cause over-irradiation of healthy tissue that should not be ablated. This work aims to formulate an optimization problem to find the optimal laser orientation in order to minimize the possibility of excessive laser-induced tissue ablation. We first develop a 3D data-driven geometric model to predict the shape of the tissue cavity after a single laser ablation. Modelling the “target” and “non-target” tissue region by an obstacle boundary, the determination of an optimal orientation is converted to a collision-minimization problem. The goal of this optimization formulation is maintaining the ablated contour distance from the obstacle boundary, which is solved by Projected gradient descent. Simulation experiments were conducted and the results validated the proposed method with conditions of various obstacle shapes and different initial incident angles.
Task assignment for suppliers' participation in collaborative product development

The Particle Swarm Optimization(PSO) algorithm was used to deal with task assignment problem in multi-suppliers' participation in collaborative product development.Based on the Dependence Structure Matrix(DSM),the total time model for collaborative produce development was constructed,the PSO algorithm to realize task optimization assignment was designed,and through simulation analysis,practicality of the algorithm was verified.By algorithm design,assignment optimization of subtasks cross organizations was realized,so as to ensure manufacturers to finish product development in due date and respond to rapid changes of markets.
Linear Variational State Space Filtering

We introduce Variational State-Space Filters (VSSF), a new method for unsupervised learning, identification, and filtering of latent Markov state space models from raw pixels. We present a theoretically sound framework for latent state space inference under heterogeneous sensor configurations. The resulting model can integrate an arbitrary subset of the sensor measurements used during training, enabling the learning of semi-supervised state representations, thus enforcing that certain components of the learned latent state space to agree with interpretable measurements. From this framework we derive L-VSSF, an explicit instantiation of this model with linear latent dynamics and Gaussian distribution parameterizations. We experimentally demonstrate L-VSSF's ability to filter in latent space beyond the sequence length of the training dataset across several different test environments.
DenseTact: Optical Tactile Sensor for Dense Shape Reconstruction

Increasing the performance of tactile sensing in robots enables versatile, in-hand manipulation. Vision-based tactile sensors have been widely used as rich tactile feedback has been shown to be correlated with increased performance in manipulation tasks. Existing tactile sensor solutions with high resolution have limitations that include low accuracy, expensive components, or lack of scalability. In this paper, an inexpensive, scalable, and compact tactile sensor with high-resolution surface deformation modeling for surface reconstruction of the 3D sensor surface is presented. By observing the contact surface with a fisheye camera, it is shown that the surface deformation can be estimated in real-time (1.8 ms) using deep convolutional neural networks. This sensor in its design and sensing abilities represents a significant step toward better object in-hand localization, classification, and surface estimation all enabled by calibrated, high-resolution shape reconstruction.
Synthesizing Tensor Transformations for Visual Self-attention

Self-attention shows outstanding competence in capturing long-range relationships while enhancing performance on vision tasks, such as image classification and image captioning. However, the self-attention module highly relies on the dot product multiplication and dimension alignment among query-key-value features, which cause two problems: (1) The dot product multiplication results in exhaustive and redundant computation. (2) Due to the visual feature map often appearing as a multi-dimensional tensor, reshaping the scale of the tensor feature to adapt to the dimension alignment might destroy the internal structure of the tensor feature map. To address these problems, this paper proposes a self-attention plug-in module with its variants, namely, Synthesizing Tensor Transformations (STT), for directly processing image tensor features. Without computing the dot-product multiplication among query-key-value, the basic STT is composed of the tensor transformation to learn the synthetic attention weight from visual information. The effectiveness of STT series is validated on the image classification and image caption. Experiments show that the proposed STT achieves competitive performance while keeping robustness compared to self-attention based above vision tasks.
Making Music Using Two Quantum Algorithms

This document explores how to make music using quantum computing algorithms. The text is an unedited pre-publication chapter which will appear in the book"Quantum Computer Music", Miranda, E. R. (Editor). This chapters provides the background and specific details of a collaboration formed in 2021 between the Quantum Engineering Technology Labs - a quantum computing and technology research group at the University of Bristol - and music artist, producer and audio engineer Simon Small. The goal of the collaboration was to explore how the data and concepts used in the research at the university could be `sonified' to create sounds or even make music.
Driving Conflict Resolution of Autonomous Vehicles at Unsignalized Intersections: A Differential Game Approach

Considering personalized driving preferences, a new decision-making framework is developed using a differential game approach to resolve the driving conflicts of autonomous vehicles (AVs) at unsignalized intersections. To realize human-like driving and personalized decision-making, driving aggressiveness is first defined for AVs. To improve driving safety, a Gaussian potential field model is built for collision risk assessment. Besides, in the proposed decision-making framework, the collision risk assessment model is further used to reduce the computational complexity based on an event-triggered mechanism. In the construction of payoff function, both driving safety and passing efficiency are comprehensively considered, and the driving aggressiveness is also reflected. Two kinds of equilibrium solution to the differential game, i.e., the Nash equilibrium and Stackelberg equilibrium, are discussed and solved. Finally, the proposed decision-making algorithm is tested through a hardware-in-the-loop testing platform, and its feasibility, effectiveness, and real-time implementation performance are validated.
Solvability of Multistage Pseudomonotone Stochastic Variational Inequalities

This paper focuses on the solvability of multistage pseudomonotone stochastic variational inequalities (SVIs). On one hand, some known solvability results of pseudomonotone deterministic variational inequalities cannot be directly extended to pseudomonotone SVIs, so we construct the isomorphism between both and then establish theoretical results on the existence, convexity, boundedness and compactness of the solution set for pseudomonotone SVIs via such an isomorphism. On the other hand, the progressive hedging algorithm (PHA) is an important and effective method for solving monotone SVIs, but it cannot be directly used to solve nonmonotone SVIs. We propose some sufficient conditions on the elicitability of pseudomonotone SVIs, which opens the door for applying Rockafellar’s elicited PHA to solve pseudomonotone SVIs. Numerical results on solving a pseudomonotone two-stage stochastic market optimization problem and randomly generated two-stage pseudomonotone linear complementarity problems are presented to show the efficiency of the elicited PHA for solving pseudomonotone SVIs.
Nonlinear lumped-parameter models for blood flow simulations in networks of vessels

To address the issue of computational efficiency related to the modelling of blood flow in complex networks, we derive a family of nonlinear lumped-parameter models for blood flow in compliant vessels departing from a well-established one-dimensional model. These 0D models must preserve important nonlinear properties of the original 1D model: the nonlinearity of the pressure-area relation and the pressure-dependent parameters characterizing the 0D models, the resistance R and the inductance L , defined in terms of a time-dependent cross-sectional area subject to pressure changes. We introduce suitable coupling conditions to join 0D vessels through 0D junctions and construct 0D networks preserving the original 1D network topology. The newly derived nonlinear 0D models are then applied to several arterial networks and the predicted results are compared against (i) the reference 1D results, to validate the models and assess their ability to reproduce good approximations of pressure and flow waveforms in all vessels at a much lower computational cost, measured in terms of CPU time, and (ii) the linear 0D results, to evaluate the improvement gained by including certain nonlinearities in the 0D models, in terms of agreement with the 1D results.
Opinion dynamics in social networks: From models to data

Opinions are an integral part of how we perceive the world and each other. They shape collective action, playing a role in democratic processes, the evolution of norms, and cultural change. For decades, researchers in the social and natural sciences have tried to describe how shifting individual perspectives and social exchange lead to archetypal states of public opinion like consensus and polarization. Here we review some of the many contributions to the field, focusing both on idealized models of opinion dynamics, and attempts at validating them with observational data and controlled sociological experiments. By further closing the gap between models and data, these efforts may help us understand how to face current challenges that require the agreement of large groups of people in complex scenarios, such as economic inequality, climate change, and the ongoing fracture of the sociopolitical landscape.
Diffusion-mediated surface reactions, Brownian functionals and the Feynman-Kac formula

Many processes in cell biology involve diffusion in a domain Ω that contains a target U whose boundary ∂U is a chemically reactive surface. Such a target could represent a single reactive molecule, an intracellular compartment or a whole cell. Recently, a probabilistic framework for studying diffusion-mediated surface reactions has been developed that considers the joint probability density or propagator for the particle position and the so-called boundary local time. The latter characterizes the amount of time that a Brownian particle spends in the neighborhood of a point on a totally reflecting boundary. The effects of surface reactions are then incorporated via an appropriate stopping condition for the boundary local time. In this paper we generalize the theory of diffusionmediated surface reactions to cases where the whole interior target domain U acts as a partial absorber rather than the target boundary ∂U . Now the particle can freely enter and exit U , and is only able to react (be absorbed) within U . The appropriate Brownian functional is then the occupation time (accumulated time that the particle spends within U) rather than the boundary local time. We show that both cases can be considered within a unified framework by using a FeynmanKac formula to derive a boundary value problem (BVP) for the propagator of the corresponding Brownian functional, and introducing an associated stopping condition. We illustrate the theory by calculating the mean first passage time (MFPT) for a spherical target U located at the center of a spherical domain Ω. This is achieved by solving the propagator BVP directly, rather than using spectral methods. We find that if the first moment of the stopping time density is infinite, then the MFPT is also infinite, that is, the spherical target is not sufficiently absorbing. Diffusion-mediated surface reactions 2
CAD Based Design Optimization of Four-bar Mechanisms: a coronaventilator case study

Design optimization of mechanisms is a promising research area as it results in more energy-efficient machines without compromising performance. However, machine builders do not actually use the design methods described in the literature as these algorithms require too much theoretical analysis. Moreover, the design synthesis approaches in the literature predominantly utilize heuristic optimizers leading to suboptimal local minima. This research introduces a convenient optimization workflow allowing wide industrial adoption, while guaranteeing to reveal the global optimum. To guarantee that we find the global optimum, a mathematical expression of the constraints describing the feasible region of possible designs is of great importance. Therefore, kinematic analysis of the point-to-point (PTP) planar four-bar mechanism is discussed to obtain the static and dynamic constraints. Within the feasible region, objective value samples are generated through CAD multi-body software. These motion simulations determine the required torque to fulfill the movement for a certain combination of design parameters. Sparse interpo∗Corresponding author email adresses: Abdelmajid.Benyahya@uantwerpen.be (Abdelmajid Ben yahya), Nick.VanOosterwyck@uantwerpen.be (Nick Van Oosterwyck), Ferre.Knaepkens@uantwerpen.be (Ferre Knaepkens), Simon.Houwen@UGent.be (Simon Houwen), Stijn.Herregodts@UGent.be (Stijn Herregodts), Jan.Herregodts@UGent.be (Jan Herregodts), Bart.Vanwalleghem@UGent.be (Bart Vanwalleghem), Annie.Cuyt@uantwerpen.be (Annie Cuyt), Stijn.Derammelaere@uantwerpen.be (Stijn Derammelaere) 1 ar X iv :2 20 1. 01 59 0v 1 [ ee ss . S Y ] 5 J an 2 02 2 lation techniques allow minimizing the required amount of samples and thus CAD simulations. Moreover, this interpolation of simulation results enables the representation of the objective in a mathematical description without in-depth analytical design analysis by the machine designer. Subsequently, the mathematical expression of the objective allows global optimizers to find a global optimal design within the feasible design space. In a case study of a coronaventilator mechanism with three design parameters (DP’s), 1870 CAD motion simulations from which only 618 are used to build a model allowed to reduce the RMS torque of the mechanism by 67%.
Extending the limit of molecular dynamics with ab initio accuracy to 10 billion atoms

High-performance computing, together with a neural network model trained from data generated with first-principles methods, has greatly boosted applications of ab initio molecular dynamics in terms of spatial and temporal scales on modern supercomputers. Previous state-of-the-art can achieve 1 -- 2 nanoseconds molecular dynamics simulation per day for 100-million atoms on the entire Summit supercomputer. In this paper, we have significantly reduced the memory footprint and computational time by a comprehensive approach with both algorithmic and system innovations. The neural network model is compressed by model tabulation, kernel fusion, and redundancy removal. Then optimizations such as acceleration of customized kernel, tabulation of activation function, MPI+OpenMP parallelization are implemented on GPU and ARM architectures. Testing results of the copper system show that the optimized code can scale up to the entire machine of both Fugaku and Summit, and the corresponding system size can be extended by a factor of 134 to an unprecedented 17 billion atoms. The strong scaling of a 13.5-million atom copper system shows that the time-to-solution can be 7 times faster, reaching 11.2 nanoseconds per day. This work opens the door for unprecedentedly large-scale molecular dynamics simulations based on ab initio accuracy and can be potentially utilized in studying more realistic applications such as mechanical properties of metals, semiconductor devices, batteries, etc. The optimization techniques detailed in this paper also provide insight for relevant high-performance computing applications.
Unlocking the Secrets of Software Configuration Landscapes-Ruggedness, Accessibility, Escapability, and Transferability

Modern software systems are often highly configurable to tailor varied requirements from diverse stakeholders. Understanding the mapping between configurations and the desired performance attributes plays a fundamental role in advancing the controllability and tuning of the underlying system, yet has long been a dark hole of knowledge due to their black-box nature and the enormous combinatorial configuration space. In this paper, using $86$M evaluated configurations from three real-world systems on $32$ running workloads, we conducted one of its kind fitness landscape analysis (FLA) for configurable software systems. With comprehensive FLA methods, we for the first time show that: $i)$ the software configuration landscapes are fairly rugged, with numerous scattered local optima; $ii)$ nevertheless, the top local optima are highly accessible, featuring significantly larger basins of attraction; $iii)$ most inferior local optima are escapable with simple perturbations; $iv)$ landscapes of the same system with different workloads share structural similarities, which can be exploited to expedite heuristic search. Our results also provide valuable insights on the design of tailored meta-heuristics for configuration tuning; our FLA framework along with the collected data, build solid foundation for future research in this direction.
Improving Spiking Neural Network Accuracy Using Time-based Neurons

Due to the fundamental limit to reducing power consumption of running deep learning models on von-Neumann architecture, research on neuromorphic computing systems based on low-power spiking neural networks using analog neurons is in the spotlight. In order to integrate a large number of neurons, neurons need to be designed to occupy a small area, but as technology scales down, analog neurons are difficult to scale, and they suffer from reduced voltage headroom/dynamic range and circuit nonlinearities. In light of this, this paper first models the nonlinear behavior of existing current-mirror-based voltage-domain neurons designed in a 28nm process, and show SNN inference accuracy can be degraded by the effect of neuron’s nonlinearity. Then, to mitigate this problem, we propose a novel neuron, which processes incoming spikes in the time domain and greatly improves the linearity, thereby improving the inference accuracy compared to the existing voltage-domain neuron. Tested on the MNIST dataset, the inference error rate of the proposed neuron differs by less than 0.1% from that of the ideal neuron.
Quantum thermodynamic devices: From theoretical proposals to experimental reality

Thermodynamics originated in the need to understand novel technologies developed by the Industrial Revolution. However, over the centuries, the description of engines, refrigerators, thermal accelerators, and heaters has become so abstract that a direct application of the universal statements to real-life devices is everything but straight forward. The recent, rapid development of quantum thermodynamics has taken a similar trajectory, and, e.g., “quantum engines” have become a widely studied concept in theoretical research. However, if the newly unveiled laws of nature are to be useful, we need to write the dictionary that allows us to translate abstract statements of theoretical quantum thermodynamics to physical platforms and working mediums of experimentally realistic scenarios. To assist in this endeavor, this review is dedicated to provide an overview over the proposed and realized quantum thermodynamic devices and to highlight the commonalities and differences of the various physical situations.