Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Machine Learning Techniques for Activities of Daily Living Classification with Electromyographic Data*

Advances in data science and wearable robotic devices present an opportunity to improve rehabilitation outcomes. Some of these devices incorporate electromyography (EMG) electrodes that sense physiological patient activity, making it possible to develop rehabilitation systems able to assess the patient's progress when performing activities of daily living (ADLs). However, additional research is needed to improve the ability to interpret EMG signals. To address this issue, an off-line classification approach for the 26 upper-limb ADLs included in the KIN-MUS UJI dataset is presented in this paper. The ADLs were performed by 22 subjects, while seven EMG signals were recorded from their forearms. From variable-length EMG time windows, 18 features were computed, and 13 features more were extracted from frequency domain windows. The classification performance of five different machine learning techniques, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), Gated Recurrent Unit (GRU) network, XGBoost, and Random Forests, were compared. CNN performed best amongst individual models, with an accuracy above 80%, compared to SVM with 77%, GRU with 73.9%, and the tree-based models below 64%. Ensemble learning with four CNN models achieved an even higher accuracy of 86%. These results suggest that the CNN ensemble model is capable of classifying EMG signals for most ADLs, which could be used in off-line quantitative assessment of robotic rehabilitation outcomes.
THE EXTENDED OPTICAL DISK OF M101

We have used deep, wide-field optical imaging to study the faint outskirts of the luminous spiral galaxy M101 (NGC 5457) as well as its surrounding environment. Over 6 deg2, our imaging has a limiting surface brightness of μB ∼ 29.5 mag arcsec−2, and has revealed the stellar structure of M101's disk out to nearly 25′ (50 kpc), 3 times our measured R25 isophotal size of the optical disk. At these radii, the well-known asymmetry of the inner disk slews 180°, resulting in an asymmetric plume of light at large radius which follows the very extended H i disk to the northeast of M101. This plume has very blue colors (B − V ∼ 0.2), suggesting that it is the somewhat more evolved (few hundred Myr to ∼1 Gyr) counterpart of the young far-ultraviolet emitting population traced by Galaxy Evolution Explorer imaging. We also detect another, redder spur of extended light to the east of the disk, and both structures are reminiscent of features produced during fly-by galaxy interactions. However, we see no evidence of very extended tidal tails around M101 or any of its companions which might be expected from a recent encounter with a massive companion. We consider the properties of M101's outer disk in light of possible past interactions with the nearby companion galaxies NGC 5477 and NGC 5474. The detection of optical starlight at such large radii gives us the ability to study star formation histories and stellar populations in outer disks over longer timescales than those traced by the UV or Hα emitting populations. Our data suggest an ongoing buildup of M101's outer disk due to encounters in the group environment triggering extended star formation and tidal heating of existing disk populations.
Fast Multi-Modulus Blind Deconvolution Algorithms

A novel class of fast Multi-Modulus algorithms (fastMMA) for Blind Source Separation (BSS) and deconvolution are presented in this work. These are obtained through a fast fixed-point optimization rule used to minimize the Multi-Modulus (MM) criterion. Here, two BSS versions are provided to separate the sources either by finding the separation matrix at once or by separating a single source each time using a fast deflation technique. Further, the latter method is extended to cover systems of convolutive nature. Interestingly, these algorithms are implicitly shown to belong to the fixed step-size gradient descent family, henceforth, an algebraic variable step-size is proposed to make these algorithms converge even much faster. Apart from being computationally and performance-wise attractive, the new algorithms are free of any user-defined parameters.
Cross-Modality Super-Resolution of Satellite Gravity Data for Geophysical Exploration

Gravity surveys have played an essential role in many geoscience fields, especially as an early screening tool for subsurface hydrocarbon exploration. Since land-based gravity surveys are often expensive and difficult to obtain in remote places, we explore the use of satellite-derived gravity data which is available throughout the Earth and updated periodically. Since the accuracy and resolution of the gravity measured from satellites are lower than land-based gravity measurements, the satellite-based data was enhanced through a deep-learning-based super-resolution (SR) technique. The SR-enhanced Bouguer and free-air gravity anomaly data were used for the classification of hydrocarbon regions using supervised machine learning. Results indicate the successful application of supervised machine learning for hydrocarbon classification using SR-enhanced Bouguer and free-air gravity anomalies with high prediction accuracy.
A Random Tree Forest decision support system to personalize upper extremity robot-assisted rehabilitation in stroke: a pilot study

Robotic-based rehabilitation administered by means of serious games certainly represents the frontier of rehabilitation treatments, offering a high degree of customization of therapy, to meet individual patients’ needs and to tailor a proper rehabilitation therapy. Despite the rush on developing complex rehabilitation systems, they often do not provide clinicians with long-term information about the outcome of rehabilitation, thus, not supporting them in the initial set-up phase of the therapy. In this paper, a Random-Forest based system was trained and tested to provide a prediction at discharge of several clinical scales outcomes (i.e. FMA, ARAT, and MI), having clinical scale scores and measures from the robotic system at the enrollment as inputs. The dataset includes 25 post-stroke patients from different clinics, that underwent a variable number of days of rehabilitation with a robotic treatment. Results have shown that the system is able to predict the final outcome with an accuracy ranging from 60% to 73% on the selected scales. Also results provide information on which variables are more relevant for the prediction of outcome of therapy, in particular clinical scales scores such as FMA, ARAT, MI, NRS, PCS, and MCS and robotic automatically extracted measurements related to patient’s work expenditure and time. This supports the idea of using such a system in a clinical environment in a decision support tool for clinicians.
The dust mass in z > 6 normal star-forming galaxies

We interpret recent ALMA observations of z > 6 normal star forming galaxies by means of a semi-numerical method, which couples the output of a cosmological hydrodynamical simulation with a chemical evolution model which accounts for the contribution to dust enrichment from supernovae, asymptotic giant branch stars and grain growth in the interstellar medium. We find that while stellar sources dominate the dust mass of small galaxies, the higher level of metal enrichment experienced by galaxies with Mstar > 10 9 M⊙ allows efficient grain growth, which provides the dominant contribution to the dust mass. Even assuming maximally efficientsupernova dust production, the observed dust mass of the z = 7.5 galaxy A1689-zD1 requires very efficient grain growth. This, in turn, implies that in this galaxy the average density of the cold and dense gas, where grain growth occurs, is comparable to that inferred from observations of QSO host galaxies at similar redshifts. Although plausible, the upper limits on the dust continuum emission of galaxies at 6.5 < z < 7.5 show that these conditions must not apply to the bulk of the high redshift galaxy population.
Shimura correspondence of Maass wave forms of half integral weight

Introduction. In [11], Shimura showed the existence of a correspondence between modular forms of half integral weight and those of integral weight which commutes with the action of Hecke operators, and he exhibited some close relations between them. Using theta series attached to quadratic forms, Shintani [12] gave an inverse mapping to the Shimura mapping. Following the ideas of Shintani, Niwa [7] reformulated the Shimura correspondence by representing it as the inner product of modular forms of half integral weight. This Shintani–Niwa’s theta lifting was generalized to the case of holomorphic modular forms on the tube domain associated with quadratic forms of signature (2, n − 2) by Oda [8] and by Rallis and Schiffmann [9]. These liftings only concern the case of holomorphic modular forms. On the other hand, Asai [1] and Friedberg [3] and [4] treated the theta lifting for non-holomorphic modular forms. Thus, a question arises whether we can generalize the Shimura correspondence to such forms. The purpose of this note is to derive a correspondence Ψ between nonholomorphic modular forms F of half integral weight and those of integral weight and to describe an explicit relation between the Fourier coefficients of F and Ψ(F ). Our results yield some generalizations of Shimura’s result [11]. Section 1 is of preparatory character: we recall the definition of and basic results on Maass wave forms of integral weight and half integral weight. In Section 2, we summarize the definition and properties of the theta series Θ(z, g) introduced in [7] and [12]. Using Θ(z, g), we construct a mapping Ψ of Maass wave forms of half integral weight to those of integral weight. In Section 3, we introduce some theta series and we derive a transformation formula for them. In that section we employ some methods of Niwa. We verify that Θ(z, g) splits into some simpler theta series. Niwa [7] showed this decomposition only for diagonal elements g = (∗ 0 0 ∗ ) ∈ SL(2,R), which was sufficient for computation of the Fourier coefficients of the image of the Shimura lifting of holomorphic modular forms of half integral weight. We
Identifying corresponding SAR-Optical patches using Inception-inspired Pseudo-Siamese CNN

Even for humans, at times interpreting a SAR image can prove to be quite challenging, let alone finding and identifying the corresponding optical patch. The problem of identifying corresponding SAR-optical patches finds many applications in remote sensing and computer vision. While there have been attempts to solve the problem with the use of convolutional neural networks like Siamese networks, the excellence of more recent advances can be used to solve it more effectively. This paper is an attempt towards the same. We use Inception-inspired convolutional modules in a Pseudo-Siamese network to make progress in solving the problem of identifying corresponding patches in SAR and Optical images.
TESTING A SCALE-INDEPENDENT METHOD TO MEASURE THE MASS OF BLACK HOLES

Estimating the black hole mass at the center of galaxies is a fundamental step not only for understanding the physics of accretion, but also for the cosmological evolution of galaxies. Recently, a new method, based solely on X-ray data, was successfully applied to determine the black hole mass in Galactic systems. Since X-rays are thought to be produced via Comptonization process both in stellar and supermassive black holes, in principle, the same method may be applied to estimate the mass in supermassive black holes. In this work we test this hypothesis by performing a systematic analysis of a sample of active galactic nuclei, whose black hole mass has been already determined via reverberation mapping and which possess high-quality XMM-Newton archival data. The good agreement obtained between the black hole masses derived with this novel scaling technique and the reverberation mapping values suggests that this method is robust and works equally well on stellar and supermassive black holes, making it a truly scale-independent technique for black hole determination.
ASSESSMENT OF GRID IMPEDANCE IMPACT ON SUPRAHARMONIC PROPAGATION

Supraharmonic (SH) propagation is determined by the impedance of both the grid and the devices connected to it. Few attempts to characterize this combined dependency have been done. The interest of grid operators is in counteracting the propagation of SHs upstream to maintain power quality. Characterizing the impact of impedance on SH propagation gives information to strategically counteract this propagation to the upstream grid. This paper presents an experimental case study for the assessment of the sensitivity of SH propagation to changes in impedance of the grid at the delivery point. The results are then compared to the changes of SH propagation provoked by the connection of low-voltage (LV) equipment.
Helmholtz-Zentrum Dresden-Rossendorf (HZDR) Synchronized helicity oscillations: A link between planetary tides and the solar cycle?

Recent years have seen an increased interest in the question of whether the gravitational action of planets could have an influence on the solar dynamo. Without discussing the observational validity of the claimed correlations, we ask for a possible physical mechanism that might link the weak planetary forces with solar dynamo action. We focus on the helicity oscillations that were recently found in simulations of the current-driven, kink-type Tayler instability, which is characterized by an m = 1 azimuthal dependence. We show how these helicity oscillations can be resonantly excited by some m = 2 perturbation that reflects a tidal oscillation. Specifically, we speculate that the 11.07 years tidal oscillation induced by the Venus–Earth–Jupiter system may lead to a 1:1 resonant excitation of the oscillation of the α-effect. Finally, in the framework of a reduced, zero-dimensional α–Ω dynamo model we recover a 22.14-year cycle of the solar dynamo.
Physical-layer Security of Uplink mmWave Transmissions in Cellular V2X Networks

In this paper, we investigate physical-layer security of the uplink millimeter wave communications for a cellular vehicle-to-everything (C-V2X) network comprised of a large number of base stations (BSs) and different categories of V2X nodes, including vehicles, pedestrians, and road side units. Considering the dynamic change and randomness of the topology of the C-V2X network, we model the roadways, the V2X nodes on each roadway, and the BSs by a Poisson line process, a 1D Poisson point process (PPP), and a 2D PPP, respectively. We propose two uplink association schemes for a typical vehicle, namely, the smallest-distance association (SDA) scheme and the largest-power association (LPA) scheme, and we establish a tractable analytical framework to comprehensively assess the security performance of the uplink transmission, by leveraging the stochastic geometry theory. Specifically, for each association scheme, we first obtain new expressions for the association probability of the typical vehicle, and then derive the overall connection outage probability and secrecy outage probability by calculating the Laplace transform of the aggregate interference power. Numerical results are presented to validate our theoretical analysis, and we also provide interesting insights into how the security performance is influenced by various system parameters, including the densities of V2X nodes and BSs. Moreover, we show that the LPA scheme outperforms the SDA scheme in terms of secrecy throughput.
Multimodal Hyperspectral Unmixing via Attention Networks

Owing to the powerful feature extraction and representation capabilities, deep learning (DL) has been successfully applied in hyperspectral unmixing (HU). However, only relying on hyperspectral data for unmixing fails to distinguish objects with similar spectral information, resulting in the degradation of unmixing performance. To this end, this paper presents a novel multimodal unmixing network, MUNet for short, by considering the height information of light detection and ranging (LiDAR) data in a squeeze-and-excitation (SE) attention fashion to guide the unmixing process toward a more accurate performance. MUNet is capable of efficiently embedding the height information obtained from LiDAR data into the autoencoder unmixing architecture through the attention mechanism, thereby fusing more spatial information to obtain ideal unmixing results. Experimental results conducted on the real multimodal dataset demonstrate the effectiveness and superiority of the proposed MUNet compared to several state-of-the-art deep unmixing approaches.
Search for CP T and Lorentz Violation in B 0 -B 0 Oscillations with Dilepton Events

We report results of a search for CPT and Lorentz violation in B{sup 0}-{bar B}{sup 0} oscillations using inclusive dilepton events from 232 million {Upsilon}(4S) {yields} B{bar B} decays recorded by the BABAR detector at the PEP-II B Factory at SLAC. We find 2.8{sigma} significance, compatible with no signal, for variations in the complex CPT violation parameter z at the Earth's sidereal frequency and extract values for the quantities {Delta}a{sub {mu}} in the general Lorentz-violating standard-model extension. The spectral powers for variations in z over the frequency range 0.26 year{sup -1} to 2.1 day{sup -1} are also compatible with no signal.
Grapevine Varieties Identification Using Vision Transformers

The grape variety plays an important role in the wine production chain, thus identifying it is crucial for production control. Ampelographers, professionals who identify grape varieties through plant visual analysis, are scarce, and molecular markers are expansive to identify grape varieties on a large scale. In this context, Deep Learning models become an effective way to handle ampelographers scarcity. In this work, we explore the benefit of using deep learning vision transformers architecture relative to conventional CNN to identify 12 grapevine varieties using leaf-centred RGB images acquired in the field. We train an Xception model as a baseline and four different configurations of the ViT_B model. The best model achieved 0.96 of Fl-score, outperforming the state-of-the-art convolutional-based model in the used dataset.
Asymmetric Collaborative Network: Transferable Lifelong Learning for Remote Sensing Images

Lifelong learning is important in remote sensing image understanding, especially in an open world where streaming of data are incrementally available. Current related research mainly focuses on preserving learned knowledge (i.e., avoiding catastrophic forgetting) while devoting less attention to the exploitation of historical knowledge to facilitate the learning of new knowledge. Here, we propose a new framework to bridge this gap. It consists of two sub-networks that memorize old and learn new knowledge separately, and exploit the synergy of transfer cells and triple distillation to take advantage of the valuable knowledge from previous learned tasks to facilitate learning new tasks while avoiding forgetting old tasks. Furthermore, it uses an asymmetric structure considering feature generality on historical tasks and scale- and channel-feature dependence on remote sensing images for specific tasks. Experimental results obtained in scene classification on several open benchmarks demonstrate the effectiveness of the framework.
Derivative expansion at small mass for the spinor effective action

We study the small-mass limit of the one-loop spinor effective action, comparing the derivative expansion approximation with exact numerical results that are obtained from an extension to spinor theories of the partial-wave cutoff method. In this approach, one can compute numerically the renormalized one-loop effective action for radially separable gauge field background fields in spinor QED. We highlight an important difference between the small-mass limit of the derivative expansion for spinor and scalar theories.
Stability of black holes in Einstein-charged scalar field theory in a cavity

Can a black hole that suffers a superradiant instability evolve towards a “hairy” configuration which is stable? We address this question in the context of Einstein-charged scalar field theory. First, we describe a family of static black hole solutions which possess charged scalar-field hair confined within a mirror-like boundary. Next, we derive a set of equations which govern the linear, spherically symmetric perturbations of these hairy solutions. We present numerical evidence which suggests that, unlike the vacuum solutions, the (single-node) hairy solutions are stable under linear perturbations. Thus, it is plausible that stable hairy black holes represent the end point of the superradiant instability of electrically charged Reissner-Nordstrom black holes in a cavity; we outline ways to explore this hypothesis.
Application of the PGNet to Minimize Closure Phases in a Multi-Temporal InSAR Algorithm

In a multi-temporal (MT) interferometric synthetic aperture radar (InSAR) algorithm, if the Itoh condition is not met, the closure phase of zero or near zero is no longer true. To resolve it, we apply a phase gradient network (PGNet) to assess a cell's phase gradient not subjected to the Itoh condition. The PGNet has been trained over rugged terrain in Mao County (32.06°N, 103.65°E), Sichuan Province, China. Then, the network was applied to predict phase gradients of three differential interferograms, i.e., 18 July-30 July, 30 July-11 August, and 11 August-18 July of 2017, Jiuzhaigou, Sichuan. A Ms (surface-wave magnitude) 7.0 earthquake (33.20° N, 103.82° E) occurred on 8 August 2017 in Jiuzhaigou. The PGNet has 1,175,000 cells with 0 for the horizontal closure phase gradient and 1,149,000 for the vertical closure phase gradient. (n = 1,413,120) For the Itoh condition-based method, the cell numbers are 984,800 horizontally and 1,094,000 vertically, less than those after the PGNet. Thus, the PGNet is more effective in minimizing the closure phase than the Itoh condition, improving an MT-InSAR algorithm's performance.
Quotient Equations and Integrals of Motion for Massive Spinor Field

In this article a group-theoretical aspect of the method of dimensional reduction is presented. Then, on the base of symmetry analysis of an anisotropic space geometrical description of dimensional reduction of equation for massive spinor field is given. Formula for claculating components of the energy-momentum tensor from the variables of the field factor-equations is derived.
The Stability of Standing Waves

We determine the modulational stability of standing waves with small group velocity in quasi-onedimensional systems slightly above the threshold of a supercritical Hopf bifurcation. The stability limits are given by two different long-wavelength destabilisation mechanisms and generically also a short-wavelength destabilisation. The Eckhaus parabola is shifted off-center and can be convex from below or above. For nonzero group velocity the Newell criterion, which near the cross-over from standing to traveling waves becomes a rather weak condition, does not determine the destabilisation of all standing waves in one dimension. The cross-over to the non-local equations that are asymptotically valid near threshold is discussed in detail. Close to the transition from standing to traveling waves complex dynamics can arise due to the competition of counter-propagating waves and the wavenumber selection by sources. Our results yield necessary conditions for the stability of traveling rectangles in quasi-twodimensional systems with axial anisotropy and form a starting point for understanding the spatio-temporal chaos of traveling oblique rolls observed in electroconvection of nematic liquid crystals.