Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital Twin Applications : A first systemization of their dimensions

The computer-based representation of "things" in the real world is at the heart of today’s virtual engineering practices. Digital Twin (DT) is a term that receives significant attention in academia and business within this domain. Despite its appealing metaphorical strength, people use it to describe quite different applications with specific conceptual backgrounds, goals and approaches. This paper aims to provide a first systematic classification about DT applications to support follow-up research. The first part of this paper focuses on three application cases described in the academic literature. It analyzes their conceptual background, the targeted problem and the implemented use case. The result of this analysis are seven dimensions that categorize the presented DT applications. They include distinctions of goals, focused users, life cycle phases, system levels, data sources, authenticity and data exchange levels.
Non-parametric Imitation Learning of Robot Motor Skills

Unstructured environments impose several challenges when robots are required to perform different tasks and adapt to unseen situations. In this context, a relevant problem arises: how can robots learn to perform various tasks and adapt to different conditions? A potential solution is to endow robots with learning capabilities. In this line, imitation learning emerges as an intuitive way to teach robots different motor skills. This learning approach typically mimics human demonstrations by extracting invariant motion patterns and subsequently applies these patterns to new situations. In this paper, we propose a novel kernel treatment of imitation learning, which endows the robot with imitative and adaptive capabilities. In particular, due to the kernel treatment, the proposed approach is capable of learning human skills associated with high-dimensional inputs. Furthermore, we study a new concept of correlation-adaptive imitation learning, which allows for the adaptation of correlations exhibited in high-dimensional demonstrated skills. Several toy examples and a collaborative task with a real robot are provided to verify the effectiveness of our approach.
Dynamic Stepping on Unknown Obstacles With Upper-Body Compliance and Angular Momentum Damping From the Reaction Null-Space

Contact destabilization after an impact that occurs at high-speed, e.g. when a robot steps on an obstacle of unknown height, can be tackled by injecting angular momentum damping for a short time interval immediately after the impact. This is done by making use of the motion from within the reaction null-space (RNS). The angular momentum damping results in an appropriate arm motion that stabilizes the contacts. An impact at high-speed occurs when the stepping time is very short. In this case, conventional controllers cannot handle the reaction stemming from the swing leg dynamics. A general whole-body controller is designed that makes use of the relative angular acceleration control component to inject the angular momentum damping. The proposed control method is robust; it can deal with obstacles of various height and inclination without altering the feedback gains. The controller is fast since iterative optimization is avoided. The performance is examined via a simulated dynamic stepping.
Designing Worm-inspired Neural Networks for Interpretable Robotic Control

In this paper, we design novel liquid time-constant recurrent neural networks for robotic control, inspired by the brain of the nematode, C. elegans. In the worm’s nervous system, neurons communicate through nonlinear time-varying synaptic links established amongst them by their particular wiring structure. This property enables neurons to express liquid time-constants dynamics and therefore allows the network to originate complex behaviors with a small number of neurons. We identify neuron-pair communication motifs as design operators and use them to configure compact neuronal network structures to govern sequential robotic tasks. The networks are systematically designed to map the environmental observations to motor actions, by their hierarchical topology from sensory neurons, through recurrently-wired interneurons, to motor neurons. The networks are then parametrized in a supervised-learning scheme by a search-based algorithm. We demonstrate that obtained networks realize interpretable dynamics. We evaluate their performance in controlling mobile and arm robots, and compare their attributes to other artificial neural network-based control agents. Finally, we experimentally show their superior resilience to environmental noise, compared to the existing machine learning-based methods.
Versatile Reactive Bipedal Locomotion Planning Through Hierarchical Optimization

When experiencing disturbances during locomotion, human beings use several strategies to maintain balance, e.g. changing posture, modulating step frequency and location. However, when it comes to the gait generation for humanoid robots, modifying step time or body posture in real time introduces nonlinearities in the walking dynamics, thus increases the complexity of the planning. In this paper, we propose a two-layer hierarchical optimization framework to address this issue and provide the humanoids with the abilities of step time and step location adjustment, Center of Mass (CoM) height variation and angular momentum adaptation. In the first layer, times and locations of consecutive two steps are modulated online based on the current CoM state using the Linear Inverted Pendulum Model. By introducing new optimization variables to substitute the hyperbolic functions of step time, the derivatives of the objective function and feasibility constraints are analytically derived, thus reduces the computational cost. Then, taking the generated horizontal CoM trajectory, step times and step locations as inputs, CoM height and angular momentum changes are optimized by the second layer nonlinear model predictive control. This whole procedure will be repeated until the termination condition is met. The improved recovery capability under external disturbances is validated in simulation studies.
Texting and Driving Recognition Exploiting Subsequent Turns Leveraging Smartphone Sensors

Texting while Driving has been reported as one of the major sources of inattention by car drivers, leading to an increased probability of severe road accidents. In fact, notifications, messages and other interactions with mobile devices may make the driver unaware of road and traffic events. To prevent or mitigate this issue, solutions have been proposed that either block the smartphone when inside the vehicle or recognize the activity to issue monetary fines at a later time. This paper proposes a classification framework capable to identify the location of a device within the vehicle using data from integrated sensors. This allow more selective countermeasures targeted specifically to mobile devices used by the driver, rather than by any person inside the vehicle. The framework extracts sensor data from the smartphone, computes ad-hoc features and feeds them to a neural network. Different from prior work, we demonstrate that accurate detection can be achieved even using only one device by combining subsequent turns of the vehicle.
Perceptual Image Fusion Technique of RGB and NIR Images

Image fusing techniques for visible-color (RGB) and near-infrared (NIR) images have been actively studied in last decades. Noting that the information obtained from the NIR images can improve the visibility of the RGB images, we propose an algorithm that fuses RGB image with NIR to improve the quality of RGB image based on perceptual information. We first decompose the RGB/NIR image into two parts, detail and base. Subsequently, we adjust each part by applying Just-noticeable difference (JND) profile computed by RGB image. The perceptual information quantified in terms of JND is utilized to preserve the original color characteristics by preventing saturation of image pixel values. Experimental results show that the proposed method provides better texture information and contrast adjustment than the conventional tone-mapped HDR images.
Merging Position and orientation Motion Primitives

In this paper, we focus on generating complex robotic trajectories by merging sequential motion primitives. A robotic trajectory is a time series of positions and orientations ending at a desired target. Hence, we first discuss the generation of converging pose trajectories via dynamical systems, providing a rigorous stability analysis. Then, we present approaches to merge motion primitives which represent both the position and the orientation part of the motion. Developed approaches preserve the shape of each learned movement and allow for continuous transitions among succeeding motion primitives. Presented methodologies are theoretically described and experimentally evaluated, showing that it is possible to generate a smooth pose trajectory out of multiple motion primitives.
Symmetry, Unimodality, and Lefschetz Properties for Graded Modules

If $\mbk$ is algebraically closed of characteristic zero and $R = \mbk[x,y, z]$, we first investigate the Weak Lefschetz Property for the finite length $R$-module $M$ that is the cokernel of a map $\vp: \bds_{j=1}^{n+2} R(-b_j)\to\bds_{i=1}^n R(-a_i)$. Before doing so, we spend significant time discuss the minimal free resolution of $M$, which we use to prove useful results on the symmetry and unimodality of the Hilbert function of $M$. Lastly, we define the\textit{non-Lefschetz locus} for finite length graded modules in arbitrary codimension, as well as proving several results in this direction. Moreover, we also discuss the connection with Artin level modules, the non-Lefschetz locus, and Lefschetz properties for graded modules of finite length in arbitrary codimension.
Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables

We consider the problem of learning causal models from observational data generated by linear non-Gaussian acyclic causal models with latent variables. Without considering the effect of latent variables, one usually infers wrong causal relationships among the observed variables. Under faithfulness assumption, we propose a method to check whether there exists a causal path between any two observed variables. From this information, we can obtain the causal order among them. The next question is then whether or not the causal effects can be uniquely identified as well. It can be shown that causal effects among observed variables cannot be identified uniquely even under the assumptions of faithfulness and non-Gaussianity of exogenous noises. However, we will propose an efficient method to identify the set of all possible causal effects that are compatible with the observational data. Furthermore, we present some structural conditions on the causal graph under which we can learn causal effects among observed variables uniquely. We also provide necessary and sufficient graphical conditions for unique identification of the number of variables in the system. Experiments on synthetic data and real-world data show the effectiveness of our proposed algorithm on learning causal models.
Influence of non-hydrodynamic forces on the elastic response of an ultra-thin soft coating under fluid-mediated dynamic loading

The force between two approaching solids in a liquid medium becomes increasingly large with decreasing separation, a phenomenon that prevents contact between the two solids. This growth in force occurs because of the intervening liquid, and, studies of such physical systems constitute the classical discipline of lubrication. Furthermore, when the solid(s) are soft, there are quantitative as well as qualitative alterations in the force interaction due to the solids' deformation. The underlying physics as well as resultant system behaviour are even more complex when forces of non-hydrodynamic origin come into play, two major classes of such forces being the DLVO (Derjaguin-Landau-Verwey-Overbeek) forces and the non-DLVO molecular forces. Studies assessing the coupling of these physical phenomenon are avenues of contemporary research. With this view, we perform an analytical study of fluid-mediated oscillatory motion of a rigid sphere over an ultra-thin soft coating, delineating the distinctive effects of solvation force as well as substrate compliance. Our key finding is the major augmentation in the force and substrate-deformation characteristics of the system due to solvation force when the confinement reduces to a few nanometers. Consideration of solvation force leads to upto four orders of magnitude and upto three orders of magnitude increment in force and substrate-deformation respectively. While higher softness leads to higher deformation (as expected), its effect on force and substrate-deformation characteristics exhibits a tendency towards amelioration of the increment due to solvation force.
Preflare Processes, Flux Rope Activation, Large-scale Eruption, and Associated X-class Flare from the Active Region NOAA 11875

We present a multiwavelength analysis of the eruption of a hot coronal channel associated with an X1.0 flare (SOL2013-10-28T02:03) from the active region NOAA 11875 by combining observations from Atmospheric Imaging Assembly/Solar Dynamics Observatory (SDO), Helioseismic and Magnetic Imager/SDO, Reuven Ramaty High Energy Solar Spectroscopic Imager, and Hiraiso Radio Spectrograph. EUV images at high coronal temperatures indicated the presence of a hot channel at the core of the active region from the early preflare phase evidencing the preexistence of a quasi-stable magnetic flux rope. The hot channel underwent an activation phase after a localized and prolonged preflare event occurring adjacent to one of its footpoints. Subsequently, the flux rope continued to rise slowly for ≈16 minutes during which soft X-ray flux gradually built-up characterizing a distinct precursor phase. The flux rope transitioned from the state of slow rise to the eruptive motion with the onset of the impulsive phase of the X1.0 flare. The eruptive expansion of the hot channel is accompanied by a series of type III radio bursts in association with the impulsive rise of strong hard X-ray nonthermal emissions that included explicit hard X-ray sources of energies up to ≈50 keV from the coronal loops and ≈100 keV from their footpoint locations. Our study contains evidence that preflare activity occurring within the spatial extent of a stable flux rope can destabilize it toward eruption. Moreover, sudden transition of the flux rope from the state of slow rise to fast acceleration precisely bifurcated the precursor and the impulsive phases of the flare, which points toward a feedback relationship between early coronal mass ejection dynamics and the strength of the large-scale magnetic reconnection.
AutoGAN: Neural Architecture Search for Generative Adversarial Networks

Neural architecture search (NAS) has witnessed prevailing success in image classification and (very recently) segmentation tasks. In this paper, we present the first preliminary study on introducing the NAS algorithm to generative adversarial networks (GANs), dubbed AutoGAN. The marriage of NAS and GANs faces its unique challenges. We define the search space for the generator architectural variations and use an RNN controller to guide the search, with parameter sharing and dynamic-resetting to accelerate the process. Inception score is adopted as the reward, and a multi-level search strategy is introduced to perform NAS in a progressive way. Experiments validate the effectiveness of AutoGAN on the task of unconditional image generation. Specifically, our discovered architectures achieve highly competitive performance compared to current state-of-the-art hand-crafted GANs, e.g., setting new state-of-the-art FID scores of 12.42 on CIFAR-10, and 31.01 on STL-10, respectively. We also conclude with a discussion of the current limitations and future potential of AutoGAN. The code is available at https://github.com/TAMU-VITA/AutoGAN
The assembly of freely moving rigid fibres measures the flow velocity gradient tensor

The motion of an assembly of rigid fibres is investigated for different classes of closed streamline flows, steady or time dependent, two-dimensional or three-dimensional. In our study, the dynamics of the fibre assembly is fully coupled to the flow field by means of a state of the art immersed boundary method. We show that, for sufficiently small Stokes times of the assembly, the whole flow gradient tensor can be accurately reconstructed by simply tracking the fibre assembly and measuring suitable fibre velocity differences evaluated at the fibre ends. Our results strongly suggest the possibility of using rigid fibres (or assemblies of them) to perform multi-point flow measures, either in laboratory or in field: future experiments are therefore mandatory to inquire the feasibility of a new ‘fibre tracking velocimetry’ technique.
Moments of Maximum: Segment of AR(1).

Let $X_{t}$ denote a stationary first-order autoregressive process. Consider five contiguous observations (in time $t$) of the series (e.g., $X_{1}, ., X_{5}$). Let $M$ denote the maximum of these. Let $\rho$ be the lag-one serial correlation, which satisfies $|\rho| < 1$. For what value of $\rho$ is $\mathbb{E}(M)$ maximized? How does $\mathbb{V}(M)$ behave for increasing $\rho$? Answers to these questions lie in Afonja (1972), suitably decoded.
Deviations from normal distributions in artificial and real time series: a false positive prescription

Time-series analysis allows for the determination of the Power Spectral Density (PSD) and Probability Density Function (PDF) for astrophysical sources. The former of these illustrates the distribution of power at various time-scales, typically taking a power-law form, while the latter characterizes the distribution of the underlying stochastic physical processes, with Gaussian and lognormal functional forms both physically motivated. In this paper, we use artificial time series generated using the prescription of Timmer & Koenig to investigate connections between the PDF and PSD. PDFs calculated for these artificial light curves are less likely to be well described by a Gaussian functional form for steep (Γ⪆1) PSD indices due to weak non-stationarity. Using the Fermi LAT monthly light curve of the blazar PKS2155-304 as an example, we prescribe and calculate a false positive rate that indicates how likely the PDF is to be attributed an incorrect functional form. Here, we generate large numbers of artificial light curves with intrinsically normally distributed PDFs and with statistical properties consistent with observations. These are used to evaluate the probabilities that either Gaussian or lognormal functional forms better describe the PDF. We use this prescription to show that PKS2155-304 requires a high prior probability of having a normally distributed PDF, $P(\rm {G})~$ ≥ 0.82, for the calculated PDF to prefer a Gaussian functional form over a lognormal. We present possible choices of prior and evaluate the probability that PKS2155-304 has a lognormally distributed PDF for each.
29P/Schwassmann–Wachmann 1, A Centaur in the Gateway to the Jupiter-family Comets

Jupiter-family comets (JFCs) are the evolutionary products of trans-Neptunian objects (TNOs) that evolve through the giant planet region as Centaurs and into the inner solar system. Through numerical orbital evolution calculations following a large number of TNO test particles that enter the Centaur population, we have identified a short-lived dynamical Gateway, a temporary low-eccentricity region exterior to Jupiter through which the majority of JFCs pass. We apply an observationally based size distribution function to the known Centaur population and obtain an estimated Gateway region population. We then apply an empirical fading law to the rate of incoming JFCs implied by the the Gateway region residence times. Our derived estimates are consistent with observed population numbers for the JFC and Gateway populations. Currently, the most notable occupant of the Gateway region is 29P/Schwassmann–Wachmann 1 (SW1), a highly active, regularly outbursting Centaur. SW1's present-day, very-low-eccentricity orbit was established after a 1975 Jupiter conjunction and will persist until a 2038 Jupiter conjunction doubles its eccentricity and pushes its semimajor axis out to its current aphelion. Subsequent evolution will likely drive SW1's orbit out of the Gateway region, perhaps becoming one of the largest JFCs in recorded history. The JFC Gateway region coincides with a heliocentric distance range where the activity of observed cometary bodies increases significantly. SW1's activity may be typical of the early evolutionary processing experienced by most JFCs. Thus, the Gateway region, and its most notable occupant SW1, are critical to both the dynamical and physical transition between Centaurs and JFCs.
Magnetic helicity in multiply connected domains

Magnetic helicity is a fundamental quantity of magnetohydrodynamics that carries topological information about the magnetic field. By ‘topological information’, we usually refer to the linkage of magnetic field lines. For domains that are not simply connected, however, helicity also depends on the topology of the domain. In this paper we expand the standard definition of magnetic helicity in simply connected domains to multiply connected domains in $\mathbb{R}^{3}$ of arbitrary topology. We also discuss how using the classic Biot–Savart operator simplifies the expression for helicity and how domain topology affects the physical interpretation of helicity.
GroundNet: Monocular Ground Plane Normal Estimation with Geometric Consistency

We focus on estimating the 3D orientation of the ground plane from a single image. We formulate the problem as an inter-mingled multi-task prediction problem by jointly optimizing for pixel-wise surface normal direction, ground plane segmentation, and depth estimates. Specifically, our proposed model, GroundNet, first estimates the depth and surface normal in two separate streams, from which two ground plane normals are then computed deterministically. To leverage the geometric correlation between depth and normal, we propose to add a consistency loss on top of the computed ground plane normals. In addition, a ground segmentation stream is used to isolate the ground regions so that we can selectively back-propagate parameter updates through only the ground regions in the image. Our method achieves the top-ranked performance on ground plane normal estimation and horizon line detection on the real-world outdoor datasets of ApolloScape and KITTI, improving the performance of previous art by up to 17.7% relatively.