Miracle-dz/UniFAR-FTUs huggingface dataset:
A Framework for Inclusion of Unmodelled Contact Tasks Dynamics in Industrial Robotics

This paper presents a method to include unmodeled dynamics of load or a robot’s end-effector into algorithms for collision detection or general understanding of a robot’s operation context. The approach relies on the application of a previously developed modification of the Dynamic Time Warping algorithm, as well as a universally applicable algorithm for identifying kinematic parameters. The entire process can be applied to arbitrary robot configuration, and it does not require identification of dynamic parameters. The paper addresses the two main categories of contact tasks with unmodelled dynamics, which are determined based on whether the external contact force has a consistent profile in the end effector or base coordinate. Conclusions for representative examples analysed in the paper are applicable to tasks such as load manipulation, press bending, and crimping for the first type of forces and applications such as drilling, screwdriving, snap-fit, bolting, and riveting assembly for the latter category. The results presented in the paper are based on realistic testing with measurements obtained from an industrial robot.
Building an efficient content based image retrieval system by changing the database structure

The amount of interest in digital images is growing because of the advent of the World Wide Web. Many different industries are making use of digital images today, including medicine, design, journalism, education, and more. Images can be stored and retrieved using a variety of techniques and methods. Nevertheless, the majority of those engines are dependent on Meta data (key-words, descriptions, tags). Although efficient, costly, and able to target keywords and tags with each image, and these engines have their shortcomings. Though it may be possible to use a filtering system to give more accurate results, there are additional issues.
Real-time study of diffusive and ballistic transport in spin-1/2 chains using the adaptive time-dependent density matrix renormalization group method

Using the adaptive time-dependent density matrix renormalization group method, we numerically study the spin dynamics and transport in one-dimensional spin-1/2 systems at zero temperature. Instead of computing transport coefficients from linear-response theory, we study the real-time evolution of the magnetization starting from spatially inhomogeneous initial states. In particular, we are able to analyze systems far away from equilibrium with this setup. By computing the time dependence of the variance of the magnetization, we can distinguish diffusive from ballistic regimes, depending on model parameters. For the example of the anisotropic spin-1/2 chain and at half filling, we find the expected ballistic behavior in the easy-plane phase, while in the massive regime the dynamics of the magnetization is diffusive. Our approach allows us to tune the deviation of the initial state from the ground state and the qualitative behavior of the dynamics turns out to be valid even for highly perturbed initial states in the case of easy-plane exchange anisotropies. We further cover two examples of nonintegrable models, the frustrated chain and the two-leg spin ladder, and we encounter diffusive transport in all massive phases. In the former system, our results indicate ballistic behavior in the critical phase. We propose that the study of the time dependence of the spatial variance of particle densities could be instrumental in the characterization of the expansion of ultracold atoms in optical lattices as well.
MEASUREMENT OF THE ELECTRIC CURRENT IN A kpc-SCALE JET

We present radio emission, polarization, and Faraday rotation maps of the radio jet of the galaxy 3C303. From these data we derive the magnetoplasma and electrodynamic parameters of this 50 kpc long jet. For one component of this jet we obtain for the first time a direct determination of a galactic-scale electric current (∼3 × 1018 A), and its direction—positive away from the active galactic nucleus. Our analysis strongly supports a model where the jet energy flow is mainly electromagnetic.
Numerical Three Stepped Distance Relay

This paper describes the design and development of numerical three stepped distance relay for the protection of high voltage line. The characteristic of the relay is generated by the software designed in Visual Basic 6. The software calculates the magnitude of resistance and reactance by using differential equation principle. The analog voltage and the current signal are given to the computer through Advantech device. The software samples the input analog signals and estimates impedance for characteristic development. Trip signal is generated by software according to the compared results. Keywords—Three Stepped Distance Relay, numerical relay, Protection of Transmission Line, Relay Software , Advantech Device USB4711/A
Sense and Sensitivity: How Focus Determines Meaning

1. Introduction. 2. Intonation and Meaning. 3. Three degrees of association. 4. Compositional analysis of focus. 5. Pragmatic Explanations of Focus. 6. Association with Reduced Material. 7. Extraction and Ellipsis. 8. Monotonicity and Presupposition. 9. Exclusives: facts and history. 10. Exclusives: a discourse account. 11. Conclusion
The Empirical Likelihood MPEC Approach to Demand Estimation

The family of Generalized Empirical Likelihood (GEL) estimators provide a number of potential advantages relative to Generalized Method of Moments (GMM) estimators. While it is well known these estimators share an asymptotic distribution, the GEL estimators may perform better in finite sample, particularly in the case of many weak instruments. A relatively new literature has documented that finite-sample bias in the demand estimation problem of Berry, Levinsohn, and Pakes (1995) is often large, especially in the absence of exogenous cost shifting instruments. This paper provides a formulation for a computationally tractable GEL estimator based on the MPEC method of Su and Judd (2012) and adapts it to the BLP problem. When compared to GMM, the GEL estimator performs substantially better, reducing the bias by as much as 90%. Furthermore, it is possible to use analytic bias correction to reduce the bias even more and obtain accurate estimates with relatively small numbers of markets.
An (FA)-group that is not (FR)

We construct examples of finitely generated groups L that have non-trivial actions on $\mathbb{R}$-trees but which cannot act, without fixing a vertex, on any simplicial tree. Moreover, any finitely presented group mapping onto L does have a fixed point-free action on some simplicial tree.
Water in star-forming regions with Herschel : highly excited molecular emission from the NGC 1333 IRAS 4B outflow

During the embedded phase of pre-main sequence stellar evolution, a disk forms from the dense envelope while an accretiondriven outflow carves out a cavity within the envelope. Highly excited (E � = 1000−3000 K) H2O emission in spatially unresolved Spitzer/IRS spectra of a low-mass Class 0 object, NGC 1333 IRAS 4B, has previously been attributed to the envelope-disk accretion shock. However, the highly excited H2O emission could instead be produced in an outflow. As part of the survey of low-mass sources in the Water in Star Forming Regions with Herschel (WISH-LM) program, we used Herschel/PACS to obtain a far-IR spectrum and several Nyquist-sampled spectral images to determine the origin of excited H2O emission from NGC 1333 IRAS 4B. The spectrum has high signal-to-noise in a rich forest of H2O, CO, and OH lines, providing a near-complete census of far-IR molecular emission from a Class 0 protostar. The excitation diagrams for the three molecules all require fits with two excitation temperatures. The highly excited component of H2O emission is characterized by subthermal excitation of ∼1500 K gas with a density of ∼3 × 10 6 cm −3 , conditions that also reproduce the mid-IR H2O emission detected by Spitzer. On the other hand, a high density, low temperature gas can reproduce the H2O spectrum observed by Spitzer but underpredicts the H2O lines seen by Herschel. Nyquist-sampled spectral maps of several lines show two spatial components of H2O emission, one centered at ∼5 �� (1200 AU) south of the central source at the position of the blueshifted outflow lobe and a heavily extincted component centered on-source. The redshifted outflow lobe is likely completely obscured, even in the far-IR, by the optically thick envelope. Both spatial components of the far-IR H2O emission are consistent with emission from the outflow. In the blueshifted outflow lobe over 90% of the gas-phase O is molecular, with H2 Ot wice as abundant than CO and 10 times more abundant than OH. The gas cooling from the IRAS 4B envelope cavity walls is dominated by far-IR H2O emission, in contrast to stronger [O I] and CO cooling from more evolved protostars. The high H2O luminosity may indicate that the shock-heated outflow is shielded from UV radiation produced by the star and at the bow shock.
EXTENDING COSMOLOGICAL TESTS OF GENERAL RELATIVITY WITH THE SQUARE KILOMETRE ARRAY

Tests of general relativity (GR) are still in their infancy on cosmological scales, but forthcoming experiments promise to greatly improve their precision over a wide range of distance scales and redshifts. One such experiment, the Square Kilometre Array (SKA), will carry out several wide and deep surveys of resolved and unresolved neutral hydrogen (H i) 21 cm line-emitting galaxies, mapping a significant fraction of the sky from 0 ≤ z ≲ 6 ? > . I present forecasts for the ability of a suite of possible SKA H i surveys to detect deviations from GR by reconstructing the cosmic expansion and growth history. SKA Phase 1 intensity mapping surveys can achieve sub-1% measurements of f &sgr; 8 ? > out to z ≈ 1 ? > , with an SKA1-MID Band 2 survey out to z ≲ 0.6 able to surpass contemporary spectroscopic galaxy surveys such as DESI and Euclid in terms of constraints on modified gravity parameters if challenges such as foreground contamination can be tackled effectively. A more futuristic Phase 2 H i survey of ∼ 10 9 ? > spectroscopic galaxy redshifts would be capable of detecting a ∼ 2 % ? > modification of the Poisson equation out to z ≈ 2.
Revival Duration and Determinants of ASEAN Machinery Trade During COVID-19 Pandemic and the Global Financial Crisis

ABSTRACT This study performs survival analysis to evaluate duration of revived and new machinery import and the hazard ratios (HRs) of covariates related to the global financial crisis (GFC) and COVID-19 pandemic in the Association of Southeast Asian Nations (ASEAN). The results indicate that large tariff margins decreased the possibility of disruption (HR: 0.8024) to Japanese import from ASEAN countries after revived during the COVID-19 pandemic and increased the possibility of disruption (HR: 1.0338) to Chinese import from ASEAN countries of new import during the GFC.
LETTER TO THE EDITOR: Zeeman measurements of a hyperfine resolved infrared transition of D35Cl2+

We report the first measurement of effective electron g-factors in a doubly positively charged molecule (molecular dication); the measurements were made using a fast-ion-beam/laser-beam spectrometer. We measured Zeeman splittings of a rotational line within the v = 2←1 vibrational band of the X3Σ- electronic ground state of D35Cl2+. The Zeeman splittings allowed us to assign the line as a PQ23(6) fine structure transition and to measure the effective upper and lower state effective electron g-factors as g(v' = 2, N' = 5, J' = 5) = 1.0 ± 0.4 and g(v'' = 1, N'' = 6, J'' = 5) = 1.85 ± 0.05. The large differences in the g-factors arise from vibrationally dependent spin-orbit coupling with a repulsive A3Π state.
Fast Registration of Point Cloud Based on Custom Semantic Extraction

With the increase in the amount of 3D point cloud data and the wide application of point cloud registration in various fields, the question of whether it is possible to quickly extract the key points of registration and perform accurate coarse registration has become a question to be urgently answered. In this paper, we proposed a novel semantic segmentation algorithm that enables the extracted feature point cloud to have a clustering effect for fast registration. First of all, an adaptive technique was proposed to determine the domain radius of a local point. Secondly, the feature intensity of the point is scored through the regional fluctuation coefficient and stationary coefficient calculated by the normal vector, and the high feature region to be registered is preliminarily determined. In the end, FPFH is used to describe the geometric features of the extracted semantic feature point cloud, so as to realize the coarse registration from the local point cloud to the overall point cloud. The results show that the point cloud can be roughly segmented based on the uniqueness of semantic features. The use of a semantic feature point cloud can make the point cloud have a very fast response speed based on the accuracy of coarse registration, almost equal to that of using the original point cloud, which is conducive to the rapid determination of the initial attitude.
A Preliminary Investigation into Approximating Power Transistor Switching Behavior using a Multilayer Perceptron

This paper presents an approach for reproducing key characteristics of non-linear, high frequency switching transients using a multilayer perceptron neural network. Training data is generated using variable time-step transient simulations of a half-bridge switching cell of SPICE transistor models together with constrained yet randomized combinations of DC-link voltage, drain currents and lumped loop inductances. Using the example of peak turn-OFF voltage overshoot for SiC and Si power transistors, the multilayer perceptrons show a mean error of less than (0.9 ± 1.3)%. The predictions of the multilayer perceptron are then compared to preliminary measurements made using a SiC half-bridge test-bench where good agreement is observed especially for higher drain currents. With continued development, such a neural network could be used in coarse, fixed-time-step simulations of any “half-bridge-based” circuit to offer typically unavailable high-fidelity information with negligible computation time. For example, a designer could choose a transistor and quickly see the limits on allowable loop inductance to avoid excessive voltage overshoot for their simulated current waveforms, or see an estimate for voltage overshoot if the loop inductances are known.
Assessment Strategies to Evaluate the Mediterranean Lifestyle: A Systematic Review

The Mediterranean Lifestyle (MLS) has been related to better health and quality of life. However, there is no consensus on how to assess this lifestyle. The main objective of this work was to systematically review the methodology used in different studies on the evaluation of the MLS. The specific objectives were (1) to analyze the MLS components evaluated in previous studies, (2) to explore the assessment instruments available for the analysis of the MLS, and (3) to identify the psychometric properties of these instruments. The search was carried out using the PubMed, Scopus, Web of Science, and ScienceDirect databases with the purpose of identifying those published articles in which the MLS was assessed. The review included 26 studies linked to the assessment of the MLS. Of these studies, only four exclusively used a tool to analyze MLS components globally. These studies included two questionnaires and three different indexes. None of them, however, evaluated all of the recognized MLS components, and food preparation was the least frequently evaluated component. Given the clear importance of analyzing MLS adherence and the lack of consensus in previous research, an evaluation tool needs to be created to comprehensively assess all of the MLS dimensions by means of appropriate psychometric properties.
Performance Comparison Classification using k-Nearest Neighbors and Random Forest Classification Techniques

This research presents 1) k-Nearest Neighbors Algorithm and 2) Random Forest Algorithm, nowadays, both algorithms are popular. A problem of classification is a concern for researchers to optimize the efficiency of classification accurately. Researcher therefore conducted experiments with four different data sets from UCI data set test, including with: a) Iris dataset, which is a well-known dataset b) A computer dataset which is more complex instances and classes c) Dry been which is a diverse dataset and complexity of instance and class classification and d) Yeast dataset which is a dataset containing instances and classes with challenging class attributes. Results from the k-Nearest Neighbors method reveal that when considering accuracy, the Iris dataset has the highest value at 96% of all K, followed by the Dry beans dataset test at 74% of K=4, the Computer dataset at 48% of K=2 and the Yeast dataset at 56% of K =5. Considering Recall values, they reveal that the Iris dataset is 100% of all K, the computer dataset is 100% of K=5, the Dry Been dataset is 97% at K=4 and the Yeast dataset is 100% of K=2 and K=3. Considering Precision values, they reveal that the Iris dataset is highest at 85% of all K, the Computer dataset is 78% at K=5, the Dry bean dataset is 95% at K=2 and the Yeast dataset is 83% at K=4. The results of the random forest experiments are: 1) the Iris dataset with Accuracy 94%, Recall 95% and Precision 94% 2) the Computer dataset Accuracy 18%, Recall 65% and Precision 78% 3) the Dry Bean dataset Accuracy 87%, Recall 100% and Precision 95% 4) the Yeast dataset Accuracy 75%, Recall 100%, and Precision 84%. The k-Nearest Neighbors function works well with a small number of instances data. It is performed well with the Iris and the Computer datasets. The Random Forest algorithm is performed well with a large number of instances data, it is performed well with the Dry Been and Yeast test datasets.
Bioinformatics and Experimental Analyses Reveal Immune-Related LncRNA–mRNA Pair AC011483.1-CCR7 as a Biomarker and Therapeutic Target for Ischemic Cardiomyopathy

Ischemic cardiomyopathy (ICM), which increases along with aging, is the leading cause of heart failure. Currently, immune response is believed to be critical in ICM whereas the roles of immune-related lncRNAs remain vague. In this study, we aimed to systematically analyze immune-related lncRNAs in the aging-related disease ICM. Here, we downloaded publicly available RNA-seq data from ischemic cardiomyopathy patients and non-failing controls (GSE116250). Weighted gene co-expression network analysis (WGCNA) was performed to identify key ICM-related modules. The immune-related lncRNAs of key modules were screened by co-expression analysis of immune-related mRNAs. Then, a competing endogenous RNA (ceRNA) network, including 5 lncRNAs and 13 mRNAs, was constructed using lncRNA–mRNA pairs which share regulatory miRNAs and have significant correlation. Among the lncRNA–mRNA pairs, one pair (AC011483.1-CCR7) was verified in another publicly available ICM dataset (GSE46224) and ischemic cell model. Further, the immune cell infiltration analysis of the GSE116250 dataset revealed that the proportions of monocytes and CD8+ T cells were negatively correlated with the expression of AC011483.1-CCR7, while plasma cells were positively correlated, indicating that AC011483.1-CCR7 may participate in the occurrence and development of ICM through immune cell infiltration. Together, our findings revealed that lncRNA–mRNA pair AC011483.1-CCR7 may be a novel biomarker and therapeutic target for ICM.
Creating Rigorous Open Cloaks

Based on compressing coordinate transformation (CCT) that transforms a region into a smaller region, a rigorous method for designing a cloak with controllable open window is proposed. The open cloak consists of a portion of an ideal closed cloak and an independent system whose position and parameters can be controlled. The system could be circular or diamond-shaped with coordinate transformation in radius direction and one axis of Cartesian coordinates, respectively. This cloak is able to externally hide an arbitrary-shaped object with arbitrary material property including PEC, and the object is open to the outside space. Full-wave simulations validate this rigorous cloaking concept.
Two in one? A possible dual radio-emitting nucleus in the quasar SDSS J1425+3231

The radio-emitting quasar SDSS J1425+3231 (z = 0.478) was recently found to have double-peaked narrow [O III] optical emission lines. Based on the analysis of the optical spectrum, Peng et al. suggested that this object harbours a dual active galactic nucleus (AGN) system, with two supermassive black holes (SMBHs) separated on a kpc scale. SMBH pairs should be ubiquitous according to hierarchical galaxy formation scenarios in which the host galaxies and their central black holes grow together via interactions and eventual mergers. Yet the number of presently confirmed dual SMBHs on kpc or smaller scales remains small. A possible way to obtain direct observational evidence for duality is to conduct high-resolution radio interferometric measurements, provided that both AGN are in an evolutionary phase when some activity is going on in the radio. We used the technique of very long baseline interferometry (VLBI) to image SDSS J1425+3231. Observations made with the European VLBI Network at 1.7- and 5-GHz frequencies in 2011 revealed compact radio emission at sub-mJy flux density levels from two components with a projected linear separation of similar to 2.6 kpc. These two components support the possibility of a dual AGN system. The weaker component remained undetected at 5 GHz due to its steep radio spectrum. Further study will be necessary to securely rule out a jetshock interpretation of the less dominant compact radio source. Assuming the dual AGN interpretation, we discuss black hole masses, luminosities and accretion rates of the two components using available X-ray, optical and radio data. While high-resolution radio interferometric imaging is not an efficient technique to search blindly for dual AGN, it is an invaluable tool to confirm the existence of selected candidates.
Can You Do That Again? Time Series Consolidation as a Robust Method of Tailoring Gesture Recognition to Individual Users

Robust inter-session modeling of gestures is still an open learning challenge. A sleeve equipped with capacitive strap sensors was used to capture two gesture data sets from a convenience sample of eight subjects. Two pipelines were explored. In FILT a novel two-stage algorithm was introduced which uses an unsupervised learning algorithm to find samples representing gesture transitions and discards them prior to training and validating conventional models. In TSC a confusion matrix was used to automatically consolidate commonly confused class labels, resulting in a set of gestures tailored to an individual subject’s abilities. The inter-session testing accuracy using the Time Series Consolidation (TSC) method increased from a baseline inter-session average of 42.47 ± 3.83% to 93.02% ± 4.97% while retaining an average of 5.29 ± 0.46 out of the 11 possible gesture categories. These pipelines used classic machine learning algorithms which require relatively small amounts of data and computational power compared to deep learning solutions. These methods may also offer more flexibility in interface design for users suffering from handicaps limiting their manual dexterity or ability to reliably make gestures, and be possible to implement on edge devices with low computational power.