Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparing Metrics for Robustness Against External Perturbations in Dynamic Trajectory Optimization

Dynamic trajectory optimization is a popular approach for generating optimal and dynamically consistent trajectories. In order to deal with model errors and perturbations, the trajectories are usually tracked with feedback controllers. Their robustness thus largely depends on the margins of stability and control authority the system retains. Manipulability ellipsoids and force polytopes are well-known tools for evaluating force and motion capabilities of robot manipulators. Increased control authority can be achieved by incorporating task constraints within those tools. However, they come with an increased computational cost. Additionally, their impact on resulting trajectory quality and control authority has not yet been benchmarked and compared. In this letter, we introduce a novel robustness metric, the residual force polytope, which takes the nominal torque required to maintain a posture into account. We further detail a benchmarking protocol including evaluation criteria and visualization tools to compare robustness metrics in dynamic trajectory optimization. To foster benchmarking and allow for reproducibility, we open source a flexible framework for dynamic trajectory optimization via direct transcription along with our benchmark protocols as supplementary materials. Finally, we include - to the best of our knowledge - the first holistic comparison between traditional energy minimization metrics, kinematic manipulability maximization, and force polytope methods.
Mixed pooling of seasonality in time series pallet forecasting

Multiple seasonal patterns play a key role in time series forecasting, especially for business time series where seasonal effects are often dramatic. Previous approaches including Fourier decomposition, exponential smoothing, and seasonal autoregressive integrated moving average (SARIMA) models do not reflect the distinct characteristics of each period in seasonal patterns, such as the unique behavior of specific days of the week in business data. We propose a multi-dimensional hierarchical model. Intermediate parameters for each seasonal period are first estimated, and a mixture of intermediate parameters is then taken, resulting in a model that successfully reflects the interactions between multiple seasonal patterns. Although this process reduces the data available for each parameter, a robust estimation can be obtained through a hierarchical Bayesian model implemented in Stan. Through this model, it becomes possible to consider both the characteristics of each seasonal period and the interactions among characteristics from multiple seasonal periods. Our new model achieved considerable improvements in prediction accuracy compared to previous models, including Fourier decomposition, which Prophet uses to model seasonality patterns. A comparison was performed on a real-world dataset of pallet transport from a national-scale logistic network.
Hadronic vacuum polarization contribution to the muon 
g−2
 with (
2+1
)-flavor lattice QCD on a larger than 
(10  fm)4
 lattice at the physical point

We study systematic uncertainties in the lattice QCD computation of hadronic vacuum polarization (HVP) contribution to the muon $g-2$. In this article we investigate three systematic effects; finite volume (FV) effect, cutoff effect, and integration scheme dependence. We evaluate the FV effect at the physical pion mass on two different volumes of (5.4 fm$)^4$ and (10.8 fm$)^4$ using the PACS10 configurations at the same cut-off scale. Our results indicate that the chiral perturbation theory (ChPT) possibly underestimates FV effects in a long distance region, where multi-hadron state contributions including two-pion state become prominent. For the cutoff effect, we compare two types of lattice vector operators, which are local and conserved (point-splitting) currents, by varying the cutoff scale on a more than (10 fm$)^4$ lattice at the physical point. The local-local current correlator shows smaller scaling violation than the local-conserved one both in light and strange quark channels. For the integration scheme dependence, we compare the results between the coordinate- and momentum-space integration schemes at the physical point on a (10.8 fm$)^4$ lattice. Our result for the HVP contribution to the muon $g-2$ is given by $a_\mu^{\rm hvp} = 737(9)(^{+13}_{-18})\times 10^{-10}$ in the continuum limit, where the first error is statistical and the second one is systematic. %This value is compared with the phenomenological estimate and recent lattice QCD results.
Learning Trajectory Dependencies for Human Motion Prediction

Human motion prediction, i.e., forecasting future body poses given observed pose sequence, has typically been tackled with recurrent neural networks (RNNs). However, as evidenced by prior work, the resulted RNN models suffer from prediction errors accumulation, leading to undesired discontinuities in motion prediction. In this paper, we propose a simple feed-forward deep network for motion prediction, which takes into account both temporal smoothness and spatial dependencies among human body joints. In this context, we then propose to encode temporal information by working in trajectory space, instead of the traditionally-used pose space. This alleviates us from manually defining the range of temporal dependencies (or temporal convolutional filter size, as done in previous work). Moreover, spatial dependency of human pose is encoded by treating a human pose as a generic graph (rather than a human skeletal kinematic tree) formed by links between every pair of body joints. Instead of using a pre-defined graph structure, we design a new graph convolutional network to learn graph connectivity automatically. This allows the network to capture long range dependencies beyond that of human kinematic tree. We evaluate our approach on several standard benchmark datasets for motion prediction, including Human3.6M, the CMU motion capture dataset and 3DPW. Our experiments clearly demonstrate that the proposed approach achieves state of the art performance, and is applicable to both angle-based and position-based pose representations. The code is available at https://github.com/wei-mao-2019/LearnTrajDep
Four-wave mixing of topological edge plasmons in graphene metasurfaces

It is demonstrated that net optical gain can be achieved via topologically protected four-wave mixing in a graphene metasurface. We study topologically protected four-wave mixing (FWM) interactions in a plasmonic metasurface consisting of a periodic array of nanoholes in a graphene sheet, which exhibits a wide topological bandgap at terahertz frequencies upon the breaking of time reversal symmetry by a static magnetic field. We demonstrate that due to the significant nonlinearity enhancement and large life time of graphene plasmons in specific configurations, a net gain of FWM interaction of plasmonic edge states located in the topological bandgap can be achieved with a pump power of less than 10 nW. In particular, we find that the effective nonlinear edge-waveguide coefficient is about γ ≃ 1.1 × 1013 W−1 m−1, i.e., more than 10 orders of magnitude larger than that of commonly used, highly nonlinear silicon photonic nanowires. These findings could pave a new way for developing ultralow-power-consumption, highly integrated, and robust active photonic systems at deep-subwavelength scale for applications in quantum communications and information processing.
Implications of dissipativity, inverse optimal control, and stability margins for nonlinear stochastic feedback regulators

In this paper, we derive stability margins for optimal and inverse optimal stochastic feedback regulators. Specifically, gain, sector, and disk margin guarantees are obtained for nonlinear stochastic dynamical systems controlled by nonlinear optimal and inverse optimal Hamilton‐Jacobi‐Bellman controllers that minimize a nonlinear‐nonquadratic performance criterion with cross‐weighting terms. Furthermore, using the newly developed notion of stochastic dissipativity, we derive a return difference inequality to provide connections between stochastic dissipativity and optimality of nonlinear controllers for stochastic dynamical systems. In particular, using extended Kalman‐Yakubovich‐Popov conditions characterizing stochastic dissipativity, we show that our optimal feedback control law satisfies a return difference inequality predicated on the infinitesimal generator of a controlled Markov diffusion process if and only if the controller is stochastically dissipative with respect to a specific quadratic supply rate.
Computing polynomial greatest common divisors using sparse interpolation

Computing polynomial greatest common divisors (GCD) plays an important role in Computer Algebra systems because the GCD operation is the bottleneck of many basic applications. For example, to simplify a rational function one divides the numerator and denominator by their GCD. In 1988 Ben-Or and Tiwari introduced the first deterministic polynomial interpolation algorithm which accounts for sparsity. The number of evaluation points needed by the Ben-Or/Tiwari algorithm is linear in the number of non-zero terms in the target polynomial, and moreover, all variables can be interpolated simultaneously hence parallelizing the algorithm is easier. In this thesis, we present modular multivariate polynomial GCD algorithms based on Ben-Or/Tiwari sparse interpolation. They compute the GCD modulo one or more primes. We apply a Kronecker substitution to reduce the number of variables and we modify the Ben-Or/Tiwari evaluation point sequence so that we can use primes of acceptable size (machine primes) as well as gain randomness on the choice of evaluation points to avoid several known issues in polynomial GCD algorithms. Based on several assumptions, we first present a simplified algorithm for GCD computation in Z[x1, . , xn] from which we derive some theoretical bounds and convince the reader why it works. Then we present a practical version of the algorithm where those assumptions are dropped. This leads to a more complicated algorithm but it can be shown that it always terminates and it computes the GCD efficiently. In the 1980s, subsequent research in polynomial GCD algorithm mainly focused on polynomials over number fields. In this thesis, we also present a GCD algorithm for multivariate polynomials in Q(α)[x1, . , xn] where α is an algebraic number. With a prime modulus p, all operations are performed in the finite ring Zp(α) where inversions may fail due to zero divisors. We manage to get all necessary bounds to support the correctness of our algorithm.
Engineering Wake Induction Model For Axisymmetric Multi-Kite Systems

Multi-kite airborne wind energy systems (MAWES) fly two or more kites in a high-altitude orbit that allows a main tether to unroll a generator. An engineering wake induction model could simplify the (axisymmetric) aerodynamics of such an unsteady actuator-annulus with variable thrust-coefficient, flight-radius, and downwind-direction apparent velocity, in comparison to high-fidelity wake models. To the authors’ knowledge, existing engineering models do not predict the influence of variable flight radius. The goal of this paper is to synthesize a MAWES engineering model, a closed-form function approximating the axial induction factor ã, specifically at the kite mid-span, using linearized wake parameters, as available from a dynamics solver. A vortex tube model is used to construct a plausible approximation of the induction, which is then heavily simplified for computation tractability while retaining important flow physics.
Joint Estimation of Channel and I/Q Imbalance in Massive MIMO: A Two-Timescale Optimization Approach

Although there has been a wide investigation on channel estimation in frequency-division duplex (FDD) massive multiple-input–multiple-output (MIMO) systems, the effect of imperfect radio frequency (RF) chains have been largely ignored. In this paper, we consider a downlink massive MIMO system with in-phase/quadrature imbalance (IQI) at the base station (BS). Focusing on the joint estimation for channel and IQI, we model the joint estimation problem as a two-timescale non-convex optimization based on maximum a posteriori (MAP) estimate, where the IQI parameter is treated as the long-term variable and the sparse channel vector is short-term. We propose a batch algorithm and a two-timescale online joint sparse estimation (TOJSE) algorithm to solve the problem. The proposed batch algorithm utilizes all the previously received signals to update the current long-term variable, which can achieve better performance but with increasing computational complexity over time. In contrast, the TOJSE algorithm solves the short-term problem related to the current system state and constructs a recursive convex approximation to update the long-term variable in each iteration. Thus, the memory requirements and computational complexity of the TOJSE are remarkably reduced. Moreover, for the low mobility regime, a dynamic TOJSE algorithm is further presented to exploit the temporal correlation of channel support. Finally, the simulations show that our proposed algorithms can achieve significant gain over various baselines.
Bundle Adjustment of a Time-Sequential Spectral Camera Using Polynomial Models

Lightweight hyperspectral cameras based on frame geometry have been used for several applications in unmanned aerial vehicles (UAVs). The camera used in this investigation is based on a tunable Fabry–Pérot interferometer (FPI) and works on the time-sequential principle for band acquisition. Due to this feature, when collecting images in movement, hypercubes are generated with unregistered bands, and consequently, the individual bands in each hypercube have different exterior orientation parameters (EOPs), which must be estimated by an image orientation procedure. The objective of this paper was to develop an approach for bundle block adjustment (BBA) using time-dependent polynomial models for simultaneous image orientation of all bands. The procedure consists of using a minimum number of bands to estimate the polynomial parameters. From the estimated polynomial parameters, the EOPs (position and attitude) of all bands can be determined. In tests with backprojecting ground points to interpolated bands, the average error was smaller than 1 pixel, which indicates excellent potential for orthomosaic generation. The polynomial technique was also compared to the conventional BBA. The discrepancies assessed at checkpoints indicated a similar error for both techniques, which were approximately less than the pixel size in planimetry and less than 2.8 times the pixel size in height. Therefore, the results show that the spectral band orientation can be performed with the proposed technique, assuming that the trajectory during the cube can be modeled with the polynomial model, which reduces the workload while achieving the same accuracy as conventional BBA for all bands.
Class Information-Based Band Selection for Hyperspectral Image Classification

This paper presents a class information (CI)-based band selection (BS) approach to hyperspectral image classification (HSIC). It introduces a new concept from an information theory point of view, CI which can be used to determine an appropriate weight imposed on each class of interest. Specifically, two types of criteria, intraclass information criterion (IC) and interclass IC are derived as CI probabilities to measure CI that can be used to determine the number of training samples required to be selected for each class. With such CI-calculated probabilities, another new concept called class self-information (CSI) is also defined for each class that can be further used to define the class entropy (CE) so that CSI and CE can be used to determine the number of bands required for BS, <inline-formula> <tex-math notation="LaTeX">$n_{\text {BS}}$ </tex-math></inline-formula>. In order to find desired <inline-formula> <tex-math notation="LaTeX">$n_{\text {BS}}$ </tex-math></inline-formula> bands, two types of BS methods based on CSI and CE are custom-designed, called single class signature-constrained BS (SCSC-BS) which utilizes the constrained energy minimization (CEM) to constrain each individual class signature to select bands for a particular class according to its CSI-determined <inline-formula> <tex-math notation="LaTeX">$n_{\text {BS}}$ </tex-math></inline-formula> and a multiple class signatures-constrained BS (MCSC-BS) which takes advantage of linearly constrained minimum variance (LCMV) to constrain all class signatures to select CE-determined <inline-formula> <tex-math notation="LaTeX">$n_{\text {BS}}$ </tex-math></inline-formula> bands for all classes. These SCSC-BS and MCSC-BS selected bands are then used to perform classification and evaluated by CI-weighted classification measures by real image experiments. The results show that HSIC using judiciously selected partial bands as well as CI-weighted measures can improve HSIC with using full bands.
Estimation of Rail Vertical Profile Using an H-Infinity Based Optimization With Learning

Railway track vertical alignment is an important indicator of serviceability condition and thus plays a critical role for maintenance planning. Estimating the rail profile through the vertical acceleration readings provides an efficient alternative to the current practice of optical methods using special vehicles. This paper proposes an algorithm to estimate the rail vertical profile using the vertical acceleration of the vehicle resulting from the train-track dynamic interaction. The algorithm is designed to approximate the inverse of the transfer function from the rail vertical roughness to the train’s measured acceleration. The approximation problem is formulated into an H-infinity optimal control design problem, which can be further transferred into a problem of convex optimization. The proposed algorithm possesses several advantages including easy design, little tuning effort, and low computational cost. In addition, to take into account the model uncertainty, an optimization-based learning framework is proposed to further enhance the performance of the proposed algorithm. The numerical study has been conducted comprehensively to validate the observer’s properties and effectiveness in reconstructing of the rail vertical roughness.
Energetic scales in a bluff body shear layer

A detailed experimental campaign into separated shear layers stemming from rectangular sections (having aspect ratios of 5 : 1, 3 : 1 and 1 : 1) was carried out at Reynolds numbers range between $1.34\times 10^{4}$ and $1.18\times 10^{5}$ based on the body thickness. Particle image velocimetry was used to locate the highest concentration of fluctuations in the velocity field and subsequent hot-wire measurements at those locations provided adequate spectral resolution to follow the evolution of various instabilities that are active within the separated shear layer. Similar to recent findings by this same group, the shear layer behaviour is observed to contain a combination of Reynolds invariant characteristics, including its time-averaged position, while other properties demonstrate clear Reynolds number dependency, including the spatial amplification of turbulent kinetic energy. Additional results here show that the ratio of side lengths of the body is a key parameter in revealing these effects. One reason for this is the level of coupling between modes of instability, which is evaluated using two-point correlation methods. These findings indicate that the separated shear layer on a bluff body is highly nonlinear. A specific set of scales responsible for these unique behaviours is identified and discussed, along with their relationship to other scales in the flow.
Progresses of the Dark Matter Particle Explorer experiment

The Dark Matter Particle Explorer (DAMPE) is a satellite-borne,calorimetric type, high-energy-resolution space cosmic ray and gamma-ray detector. It operates stably in space for more than three years since its launch in December, 2015. Precise measurements of the total electron plus positron spectrum and the proton spectrum in wide energy ranges have been obtained, which are expected to shed new light on our understanding of the cosmic ray origin and the nature of dark matter particles. The current status of the mission and its latest physical results will be presented.
Flow patterns and heat transfer correlations in partially active cavity filled with water at its density maximum

Abstract The work presents a numerical study of natural convection of water at its density maximum in a partially active square cavity, nine relatives locations of the partially heated/cooled zone are investigated as well as the effect of different thermal boundary conditions represented as the inversion parameter Two behaviors of average Nusselt number has been detected, at Top–Middle, Top–Bottom, and Middle–Bottom configurations give rise to a maximum Conversely, at Middle–Top, Bottom–Top, and Bottom–Middle configurations that take precedence. A heat transfer correlation based on a geometric parameter describing the locations of the active zone and the density inversion parameter is proposed.
Additive consistency‐based priority‐generating method of q‐rung orthopair fuzzy preference relation

The q‐rung orthopair fuzzy set, whose membership function and nonmembership function belong to the interval [0,1], is more powerful than both intuitionistic fuzzy set and Pythagorean fuzzy set in expressing imprecise information of decision‐makers. The aim of this paper is to investigate a method to determine the priority weights from individual or group q‐rung orthopair fuzzy preference relations (q‐ROFPRs). To do so, firstly, a new definition of additively consistent q‐ROFPR is presented based on the preference relation of alternatives given by decision‐makers. Afterward, according to individual and group q‐ROFPRs, two kinds of goal programming models are proposed, respectively, to generate the q‐rung orthopair fuzzy priority weight vector of the given q‐ROFPR(s). Finally, two numerical examples are given to illustrate the effectiveness and superiority of the method proposed in this paper.
An Adaptive Dropout Deep Computation Model for Industrial IoT Big Data Learning With Crowdsourcing to Cloud Computing

Deep computation, as an advanced machine learning model, has achieved the state-of-the-art performance for feature learning on big data in industrial Internet of Things (IoT). However, the current deep computation model usually suffers from overfitting due to the lack of public available labeled training samples, limiting its performance for big data feature learning. Motivated by the idea of active learning, an adaptive dropout deep computation model (ADDCM) with crowdsourcing to cloud is proposed for industrial IoT big data feature learning in this paper. First, a distribution function is designed to set the dropout rate for each hidden layer to prevent overfitting for the deep computation model. Furthermore, the outsourcing selection algorithm based on the maximum entropy is employed to choose appropriate samples from the training set to crowdsource on the cloud platform. Finally, an improved supervised learning from multiple experts scheme is presented to aggregate answers given by human workers and to update the parameters of the ADDCM simultaneously. Extensive experiments are conducted to evaluate the performance of the presented model by comparing with the dropout deep computation model and other state-of-the-art crowdsourcing algorithms. The results demonstrate that the proposed model can prevent overfitting effectively and aggregate the labeled samples to train the parameters of the deep computation model with crowdsouring for industrial IoT big data feature learning.