Miracle-dz/UniFAR-FTUs huggingface dataset:
Haptic Teleoperation of High-dimensional Robotic Systems Using a Feedback MPC Framework

Model Predictive Control (MPC) schemes have proven their efficiency in controlling high degree-of-freedom (DoF) complex robotic systems. However, they come at a high computational cost and an update rate of about tens of hertz. This relatively slow update rate hinders the possibility of stable haptic teleoperation of such systems since the slow feedback loops can cause instabilities and loss of transparency to the operator. This work presents a novel framework for transparent teleoperation of MPC-controlled complex robotic systems. In particular, we employ a feedback MPC approach [1] and exploit its structure to account for the operator input at a fast rate which is independent of the update rate of the MPC loop itself. We demonstrate our framework on a mobile manipulator platform and show that it significantly improves haptic teleoperation's transparency and stability. We also highlight that the proposed feedback structure is constraint satisfactory and does not violate any constraints defined in the optimal control problem. To the best of our knowledge, this work is the first realization of the bilateral teleoperation of a legged manipulator using a whole-body MPC framework.
A Graph Theoretic Exploration of Coronary Vascular Trees

The aim of this study was to automate the generation of small coronary vascular networks from large point clouds that represent the coronary arterial network. Smaller networks that can be generated in a predictable manner can be used to assess the impact of network morphometry on, for example, blood flow in hemodynamic simulations. We develop a set of algorithms for generating coronary vascular networks from large point clouds. These algorithms sort the point cloud, simplify its network structure without information loss, and produce subgraphs based on given, physiologically meaningful parameters. The data were originally collected from optical fluorescence cryomicrotome images and processed before their use here.
Long-range multipartite quantum correlations and factorization in a one-dimensional spin-1/2 
XY
 chain

We study the properties of multipartite quantum correlation (MQC) in a one-dimensional spin-1/2 $XY$ chain, where the three-spin reduced states are focused on and the four introduced MQC measures are based on entanglement negativity and entanglement of formation. It is found that, even in the Ising case, the three-spin subsystems have the long-range MQCs and the tripartite quantum correlations beyond the nearest-neighbor three spins can detect the quantum phase transition and obey the finite-size scaling around the critical point. Furthermore, in the $XY$ model, we show that the two selected MQCs can indicate exactly the factorization point of the ground state for the anisotropic model in the thermodynamic and finite-size cases. Moreover, the spatial distribution of MQC based on entanglement negativity can attain to a much larger range by tuning the anisotropic parameter, and the newly defined MQC based on entanglement of formation can detect the bound entanglement in the three-spin subsystems when the entanglement negativity loses its efficacy.
A Transfer Learning-Based Approach to Marine Vessel Re-Identification

—Marine vessel re-identification technology is an important component of intelligent shipping systems and an important part of the visual perception tasks required for marine surveillance. However, unlike the situation on land, the maritime environment is complex and variable with fewer samples, and it is more difficult to perform vessel re-identification at sea. Therefore, this paper proposes a transfer dynamic alignment algorithm and simulates the swaying situation of vessels at sea, using a well-camouflaged and similar warship as the test target to improve the recognition difficulty and thus cope with the impact caused by complex sea conditions, and discusses the effect of different types of vessels as transfer objects. The experimental results show that the improved algorithm improves the mean average accuracy (mAP) by 10.2% and the first hit rate (Rank1) by 4.9% on average.
SHAP for additively modeled features in a boosted trees model

An important technique to explore a black-box machine learning (ML) model is called SHAP (SHapley Additive exPlanation). SHAP values decompose predictions into contributions of the features in a fair way. We will show that for a boosted trees model with some or all features being additively modeled, the SHAP dependence plot of such a feature corresponds to its partial dependence plot up to a vertical shift. We illustrate the result with XGBoost.
KG-NSF: Knowledge Graph Completion with a Negative-Sample-Free Approach

Knowledge Graph (KG) completion is an important task that greatly beneﬁts knowledge discovery in many ﬁelds (e.g. biomedical research). In recent years, learning KG embeddings to perform this task has received considerable attention. Despite the success of KG embedding methods, they predominantly use negative sampling, resulting in increased computational complexity as well as biased pre-dictions due to the closed world assumption. To overcome these limitations, we propose KG-NSF , a negative sampling-free framework for learning KG embeddings based on the cross-correlation matrices of embedding vectors. It is shown that the proposed method achieves comparable link prediction performance to negative sampling-based methods while converging much faster.
Towards solving the Fermi-Hubbard model via tailored quantum annealers

The Fermi-Hubbard model (FHM) on a two dimensional square lattice has long been an important testbed and target for simulating fermionic Hamiltonians on quantum hardware. We present an alternative for quantum simulation of FHMs based on an adiabatic protocol that could be an attractive target for next generations of quantum annealers. Our results rely on a recently introduced low-weight encoding that allows the FHM to be expressed in terms of Pauli operators with locality of at most three. We theoretically and numerically determine promising quantum annealing setups for both interacting 2D spinless and spinful systems, that enable to reach near the ground state solution with high fidelity for systems as big as $6\times 6$ (spinless) and $4\times 3$ (spinful). Moreover, we demonstrate the scaling properties of the minimal gap and analyze robustness of the protocol against control noise. Additionally, we identify and discuss basic experimental requirements to construct near term annealing hardware tailored to simulate these problems. Finally, we perform a detailed resource estimation for the introduced adiabatic protocol, and discuss pros and cons of this approach relative to gate-based approaches for near-term platforms.
End-to-end View Synthesis via NeRF Attention

In this paper, we present a simple seq2seq formulation for view synthesis where we take a set of ray points as input and output colors corresponding to the rays. Directly applying a standard transformer on this seq2seq formulation has two limitations. First, the standard attention cannot successfully fit the volumetric rendering procedure, and therefore high-frequency components are missing in the synthesized views. Second, applying global attention to all rays and pixels is extremely inefficient. Inspired by the neural radiance field (NeRF), we propose the NeRF attention (NeRFA) to address the above problems. On the one hand, NeRFA considers the volumetric rendering equation as a soft feature modulation procedure. In this way, the feature modulation enhances the transformers with the NeRF-like inductive bias. On the other hand, NeRFA performs multi-stage attention to reduce the computational overhead. Furthermore, the NeRFA model adopts the ray and pixel transformers to learn the interactions between rays and pixels. NeRFA demonstrates superior performance over NeRF and NerFormer on four datasets: DeepVoxels, Blender, LLFF, and CO3D. Besides, NeRFA establishes a new state-of-the-art under two settings: the single-scene view synthesis and the category-centric novel view synthesis.
Finite element dynamical subgrid-scale model for low Mach number flows with radiative heat transfer

Purpose – The purpose of this paper is to present a finite element approximation of the low Mach number equations coupled with radiative equations to account for radiative heat transfer. For high-temperature flows this coupling can have strong effects on the temperature and velocity fields. Design/methodology/approach – The basic numerical formulation has been proposed in previous works. It is based on the variational multiscale (VMS) concept in which the unknowns of the problem are divided into resolved and subgrid parts which are modeled to consider their effect into the former. The aim of the present paper is to extend this modeling to the case in which the low Mach number equations are coupled with radiation, also introducing the concept of subgrid scales for the radiation equations. Findings – As in the non-radiative case, an important improvement in the accuracy of the numerical scheme is observed when the nonlinear effects of the subgrid scales are taken into account. Besides it is possible to show.
Using data informativity for online stabilization of unknown switched linear systems

This work studies data-driven switched controller design for discrete-time switched linear systems. Instead of having access to the full system dynamics, an initialization phase is performed, during which noiseless measurements of the state and the input are collected for each mode. Under certain conditions on these measurements, we develop a stabilizing switched controller for the switched system. To be precise, the controller switches between identifying the active mode of the system and applying a predetermined stabilizing feedback. We prove that if the system switches according to certain specifications, this controller stabilizes the closed-loop system. Simulations on a network example illustrate our approach.
Density waves in protoplanetary discs excited by eccentric planets: linear theory

Spiral density waves observed in protoplanetary discs have often been used to infer the presence of embedded planets. This inference relies both on simulations as well as the linear theory of planet-disc interaction developed for planets on circular orbits to predict the morphology of the density wake. In this work we develop and implement a linear framework for calculating the structure of the density wave in a gaseous disc driven by an eccentric planet. Our approach takes into account both the essential azimuthal and temporal periodicities of the problem, allowing us to treat any periodic perturbing potential (i.e. not only that of an eccentric planet). We test our framework by calculating the morphology of the density waves excited by an eccentric, low-mass planet embedded in a globally isothermal disc and compare our results to the recent direct numerical simulations (and heuristic wavelet analysis) of the same problem by Zhu and Zhang. We find excellent agreement with the numerical simulations, capturing all the complex eccentric features including spiral bifurcations, wave crossings and planet-wave detachments, with improved accuracy and detail compared with the wavelet method. This illustrates the power of our linear framework in reproducing the morphology of complicated time-dependent density wakes, presenting it as a valuable tool for future studies of eccentric planet-disc interactions.
Sharp High-dimensional Central Limit Theorems for Log-concave Distributions

Let $X_1,\dots,X_n$ be i.i. d. log-concave random vectors in $\mathbb R^d$ with mean 0 and covariance matrix $\Sigma$. We study the problem of quantifying the normal approximation error for $W=n^{-1/2}\sum_{i=1}^nX_i$ with explicit dependence on the dimension $d$. Specifically, without any restriction on $\Sigma$, we show that the approximation error over rectangles in $\mathbb R^d$ is bounded by $C(\log^{13}(dn)/n)^{1/2}$ for some universal constant $C$. Moreover, if the Kannan-Lov\'asz-Simonovits (KLS) spectral gap conjecture is true, this bound can be improved to $C(\log^{3}(dn)/n)^{1/2}$. This improved bound is optimal in terms of both $n$ and $d$ in the regime $\log n=O(\log d)$. We also give $p$-Wasserstein bounds with all $p\geq2$ and a Cram\'er type moderate deviation result for this normal approximation error, and they are all optimal under the KLS conjecture. To prove these bounds, we develop a new Gaussian coupling inequality that gives almost dimension-free bounds for projected versions of $p$-Wasserstein distance for every $p\geq2$. We prove this coupling inequality by combining Stein's method and Eldan's stochastic localization procedure.
Status of the Compressed Baryonic Matter (CBM) Experiment at FAIR

The CBM experiment at the Facility for Antiproton and Ion Research (FAIR) aims to explore the QCD phase diagram in the region of high net-baryon densities using nucleus-nucleus collisions ($\sqrt{s_{NN}}$ = 2.9 - 4.9 GeV). CBM will be utilizing peak interaction rates of up to 10 MHz and an advanced triggerless data acquisition scheme to provide it with an unique access to rare physics probes required to accomplish CBM's physics goals. This contribution will summarise CBM's progress in terms of its physics performance simulations and the status of the comprising detector sub-systems, including their involvement in FAIR Phase-0 activities.
On the initial value of PageRank

PageRank is used by Google for ranking web pages to present search results for a user query. Here, we have shown that the PageRank of a vertex depends on its initial value which is also known as an intrinsic, non-network contribution. We analytically proved that PageRank value of vertices become proportional to their degrees if the intrinsic, non-network contributions of the vertices are proportional to their degrees or zeros. Simulated and empirical data are used to support our study. In addition, we have shown that localization of PageRank depends on the intrinsic, non-network contribution.
Some semilattice decompositions of dimonoids

Abstract We show that the system of axioms of a dimonoid is independent and prove that every dimonoid with a commutative operation is a semilattice of archimedean subdimonoids, every dimonoid with a commutative periodic semigroup is a semilattice of unipotent subdimonoids, every dimonoid with a commutative operation is a semilattice of a-connected subdimonoids and every idempotent dimonoid is a semilattice of rectangular subdimonoids.
SV assimilation in Catalan and the implications for an asymmetrical *NC constraint

This paper reviews the facts about the proposed asymmetry of the *NC constraint, that is, the constraint against nasal-voicless obstruent sequences proposed by Pater (1996). The analysis presented in this paper is framed within a theory, which includes the feature SV (sonorant voicing) in the representation of sonorants and possibly voiced obstruents. I first reexamine the facts motivating the *NC contraint and the effects it triggers. Then, I illustrate the existence of a constraint on voiceless obstruent-nasal sequences (*CN), and argue that this constraint is better represented as a constraint on sequences of a voiceless obstruent followed by an SV-bearing consonant (*C-SV). Finally, I will show that the *NC constraint can also be redefined as a *SV-C constraint, thus showing the actual symmetry of the two constraints.
Data Analysis for CHAWS Modeling

Abstract : We report here on results on two related areas of plasma-structure interaction: the analysis of data on ion collection by a negative probe in the wake of large objects (CHAWS experiment), and the modeling of electron collection by a bare positive tether in a low Earth orbit. In the first area, our analysis contributed to a number of important conclusions, including the identification of a probable density probe miscalibration, quantification of the role of H = ion collection (particularly at low bias), and description of the cylindrical-to-spherical probe sheath transition as bias increases. In the tether modeling ara, we have been able to move beyond the classical analysis of Laframboise for a non-moving, non-magnetized cylindrical probe by including both a meso-thermal plasma flow, and a cross-magnetic field, both representative of orbital conditions. The full Particle-in-Cell codes we developed predict that, in the presence of the cross-flow, current collection exceeds the Orbital Motion Limit by a factor close to 2. Preliminary analysis of these results indicate that the strong plasma fluctuations revealed by the simulation in the frontal (ion stagnation) area are responsible for the current excess.
Semiparametic models and estimation procedures for binormal ROC curves with multiple biomarkers

In diagnostic medicine, there is great interest in developing strategies for combining biomarkers in order to optimize classification accuracy. A popular model that has been used for receiver operating characteristic (ROC) curve modelling when one biomarker is available is the binormal model. Extension of the model to accommodate multiple biomarkers has not been considered in this literature. Here, we consider a multivariate binormal framework for combining biomarkers using copula functions that leads to a natural multivariate extension of the binormal model. Estimation in this model will be done using rank-based procedures. We show that the Van der Waerden rank score coefficient estimation procedure can be used for the multivariate binormal model. We also discuss adjustment for covariates in this class of models. We provide a simple two-stage estimation procedure that can be fit using standard software packages. Asymptotic results of the proposed methods are given. The techniques are applied to data from two cancer biomarker studies.
FIRST ORDER DERIVATIVE METHOD DEVELOPMENT AND VALIDATION FOR SIMULTANEOUS ESTIMATION OF AZITHROMYCIN AND DEXAMETHASONE FROM EYE DROPS

Simple, precise, accurate, and sensitive first order derivative spectroscopic method have been developed for the estimation of Azithromycin and Dexamethasone in market formulation. The wavelength maxima for Azithromycin and Dexamethasone are 215.16 nm and 239.07 nm respectively. For the first order derivative spectroscopic method, the zero crossing points (ZCPs) for Azithromycin and Dexamethasone were obtained at 256.63 nm and 214.35 nm respectively in 0.1N NaOH using Shimadzu 1800 UV- visible double beam spectrophotometer. The proposed method were successfully utilised for the determination of Azithromycin and Dexamethasone in eye drops, with good linearity, high percentage of recovery and acceptable precision. Different analytical validation parameters like linearity, accuracy, precision, limit of detection and limit of quantification were determined according to International Conference on Harmonization ICH Q2 (R1).
Effect of water deficit and sowing date on oil and protein contents in soybean co-inoculated with Azospirillum brasilense 1

influence the concentration of components (Wang et al. 2015). Water deficit has been indicated as a decisive factor in the dynamics of forming and accumulating reserves in the seeds. Mertz-Henning et al. (2018) observed that drought conditions during the reproductive phase of soybean seeds favor an increase in the protein content and a decrease in the oil content. Ghassemi-Golezani & Lotfi (2013) also had similar The soybean chemical composition is strongly influenced by genetic factors, as well as their interaction with the environment and management practices. Among the environmental factors, water deficit and temperature are those that most contribute to modify the chemical composition of beans, especially oil and protein contents. The present study aimed to assess the effects of co-inoculation with Azospirillum brasilense on soybean oil and protein contents. Two field experiments were carried out on different sowing dates. The design consisted of randomized blocks, in a split-split-plot arrangement, with four replications, including two irrigation depths (25 % and 100 % of the ETc), two inoculation methods [ Bradyrhizobium japonicum (strains Semia 5079 and Semia 5080) and Azospirillum brasilense (strains AbV 5 and AbV 6 + Bradyrhizobium japonicum )] and two cultivars (ANTA 82 and TMG 132). The co-inoculation did not affect the oil and protein contents of beans for any of the assessed management conditions. The water deficit in the reproductive stage, combined with the sowing dates, altered the oil and protein contents in both the assessed cultivars, showing, respectively, a higher average protein and oil content in the beans for the cultivars TMG 132 and ANTA 82.
Inﬂuence of gaseous cavities in ship-hydrodynamic problems: a simpliﬁed study

Several phenomena of interest in ship hydrodynamics involve the presence of time-varying air cavities in the water ﬂow. Two of the most well known, with important practical consequences, are the erosion of propeller blades and the noise. The former is connected with propellers working near the air-water interface and, in particular, to the formation of air cavities during cavitation events. It is caused by the propeller interaction with the air-water two-phase ﬂuid and represents a danger for the success of the vessel operations. The latter is connected with entrapment of air cavities during breaking and fragmentation of the air-water interface. It is a consequence of the collapse of such bubbles evolving in the surrounding water and its major relevance is in military context. Cavitating bubbles are characterized by a uniform interior pressure equal to the vapor pressure. Instead, entrapped cavities have in principle a non uniform pressure. However, in many practical cases their inner pressure can be approximated as uniform with value generally time dependent, in particular compressibility may matter. Assuming a uniform pressure in the cavity means that the effect of the interior air ﬂow on the surrounding water and structures is neglected. The importance of air cavities entrapped during free-surface breaking has been investigated numerically by Colagrossi and Landrini (2003) in connection with the problem of a dam breaking followed by a water-wall impact. When the air was modeled inside a cavity entrapped after a wave breaking event, the pressure in the surrounding water and along the close structures changed substantially with respect to the case of a single-phase (water) simulation. Very high loads on breakwaters interacting with breaking waves were