Miracle-dz/UniFAR-FTUs huggingface dataset:
A measurement of small-scale features using ionospheric scintillation. Comparison with refractive shift measurements

Abstract We present a study of scintillation induced by the mid-latitude ionosphere. By implementing methods currently used in Interplanetary Scintillation studies to measure amplitude scintillation at low frequencies, we have proven it is possible to use the Murchison Widefield Array to study ionospheric scintillation in the weak regime, which is sensitive to structures on scales ${\sim}300$ m at our observing frequency of 154 MHz, where the phase variance on this scale was $0.06\, \textrm{rad}^{2}$ in the most extreme case observed. Analysing over 1000 individual 2-min observations, we compared the ionospheric phase variance with that inferred with previous measurements of refractive shifts, which are most sensitive to scales almost an order of magnitude larger. The two measurements were found to be highly correlated (Pearson correlation coefficient 0.71). We observed that for an active ionosphere, the relationship between these two metrics is in line with what would be expected if the ionosphere’s structure is described by Kolmogorov turbulence between the relevant scales of 300 and 2000 m. In the most extreme ionospheric conditions, the refractive shifts were sometimes found to underestimate the small-scale variance by a factor of four or more, and it is these ionospheric conditions that could have significant effects on radio astronomy observations.
Equal rank local theta correspondence as a strong Morita equivalence

Let ( G, H ) be one of the equal rank reductive dual pairs ( Mp 2 n , O 2 n +1 ) or ( U n , U n ) over a nonarchimedean local ﬁeld of characteristic zero. It is well-known that the theta correspondence establishes a bijection between certain subsets, say b G θ and b H θ , of the tempered duals of G and H . We prove that this bijection arises from an equivalence between the categories of representations of two C ∗ -algebras whose spectra are b G θ and b H θ . This equivalence is implemented by the induction functor associated to a Morita equivalence bimodule (in the sense of Rieﬀel) which we construct using the oscillator representation. As an immediate corollary, we deduce that the bijection is functorial and continuous with respect to weak inclusion. We derive further consequences regarding the transfer of characters and preservation of formal degrees.
Propagation Map Reconstruction via Interpolation Assisted Matrix Completion

Constructing a propagation map from a set of scattered measurements finds important applications in many areas, such as localization, spectrum monitoring and management. Classical interpolation-type methods have poor performance in regions with very sparse measurements. Recent advance in matrix completion has the potential to reconstruct a propagation map from sparse measurements, but the spatial resolution is limited. This paper proposes to integrate interpolation with matrix completion to exploit both the spatial correlation and the potential low rank structure of the propagation map. The proposed method first enriches matrix observations using interpolation, and develops the statistics of the interpolation error based on a local polynomial regression model. Then, two uncertainty-aware matrix completion algorithms are developed to exploit the interpolation error statistics. It is numerically demonstrated that the proposed method outperforms Kriging and other state-of-the-art schemes, and reduces the mean squared error (MSE) of propagation map reconstruction by 10%–50% for a medium to large number of measurements.
Shift-tolerant Perceptual Similarity Metric

Existing perceptual similarity metrics assume an image and its reference are well aligned. As a result, these metrics are often sensitive to a small alignment error that is imperceptible to the human eyes. This paper studies the effect of small misalignment, specifically a small shift between the input and reference image, on existing metrics, and accordingly develops a shift-tolerant similarity metric. This paper builds upon LPIPS, a widely used learned perceptual similarity metric, and explores architectural design considerations to make it robust against imperceptible misalignment. Specifically, we study a wide spectrum of neural network elements, such as anti-aliasing filtering, pooling, striding, padding, and skip connection, and discuss their roles in making a robust metric. Based on our studies, we develop a new deep neural network-based perceptual similarity metric. Our experiments show that our metric is tolerant to imperceptible shifts while being consistent with the human similarity judgment.
Mid-level Representation Enhancement and Graph Embedded Uncertainty Suppressing for Facial Expression Recognition

Facial expression is an essential factor in conveying human emotional states and intentions. Although remarkable advancement has been made in facial expression recognition (FER) task, challenges due to large variations of expression patterns and unavoidable data uncertainties still remain. In this paper, we propose mid-level representation enhancement (MRE) and graph embedded uncertainty suppressing (GUS) addressing these issues. On one hand, MRE is introduced to avoid expression representation learning being dominated by a limited number of highly discriminative patterns. On the other hand, GUS is introduced to suppress the feature ambiguity in the representation space. The proposed method not only has stronger generalization capability to handle different variations of expression patterns but also more robustness to capture expression representations. Experimental evaluation on Aff-Wild2 have verified the effectiveness of the proposed method.
Resonant jumps induced by stationary tidal perturbation: a two-for-one deal

Extreme-mass-ratio inspirals (EMRIs) are promising target sources for space-based interferometers such as LISA, Taiji, and Tianqin. Depending on the astrophysical environment, such as close perturbers or an accretion disk, EMRI orbital evolution may deviate from the predictions of general relativity in vacuum. In particular, we focus on the resonance jumps, i.e. the changes of the conserved quantities induced by a stationary perturbation to the background Kerr geometry. Using Hamiltonian formulation, we provide a closed relation between the jump in Carter constant and that in the axial component of angular momentum. It is also shown that the obtained relation is consistent with the fitting formulae computed for the tidal resonance in previous works.
Scalable Proof Producing Multi-Threaded SAT Solving with Gimsatul through Sharing instead of Copying Clauses

We give a first account of our new parallel SAT solver Gimsatul. Its key feature is to share clauses physically in memory instead of copying them, which is the method of other state-of-the-art multi-threaded SAT solvers to exchange clauses logically. Our approach keeps information about which literals are watched in a clause local to a solving thread but shares the actual immutable literals of a clause globally among all solving threads. This design gives quite remarkable parallel scalability, allows aggressive clause sharing while keeping memory usage low and produces more compact proofs.
Inverse Airborne Optical Sectioning

We present Inverse Airborne Optical Sectioning (IAOS), an optical analogy to Inverse Synthetic Aperture Radar (ISAR). Moving targets, such as walking people, that are heavily occluded by vegetation can be made visible and tracked with a stationary optical sensor (e.g., a hovering camera drone above forest). We introduce the principles of IAOS (i.e., inverse synthetic aperture imaging), explain how the signal of occluders can be further suppressed by filtering the Radon transform of the image integral, and present how targets’ motion parameters can be estimated manually and automatically. Finally, we show that while tracking occluded targets in conventional aerial images is infeasible, it becomes efficiently possible in integral images that result from IAOS.
Deep Model-Based Architectures for Inverse Problems Under Mismatched Priors

There is a growing interest in deep model-based architectures (DMBAs) for solving imaging inverse problems by combining physical measurement models and learned image priors specified using convolutional neural nets (CNNs). For example, well-known frameworks for systematically designing DMBAs include plug-and-play priors (PnP), deep unfolding (DU), and deep equilibrium models (DEQ). While the empirical performance and theoretical properties of DMBAs have been widely investigated, the existing work in the area has primarily focused on their performance when the desired image prior is known exactly. This work addresses the gap in the prior work by providing new theoretical and numerical insights into DMBAs under mismatched CNN priors. Mismatched priors arise naturally when there is a distribution shift between training and testing data, for example, due to test images being from a different distribution than images used for training the CNN prior. They also arise when the CNN prior used for inference is an approximation of some desired statistical estimator (MAP or MMSE). Our theoretical analysis provides explicit error bounds on the solution due to the mismatched CNN priors under a set of clearly specified assumptions. Our numerical results compare the empirical performance of DMBAs under realistic distribution shifts and approximate statistical estimators.
Strongly Lensed Transient Sources: A Review

The past decades have witnessed a lot of progress in gravitational lensing with two main targets: stars and galaxies (with active galactic nuclei). The success is partially attributed to the continuous luminescence of these sources making the detection and monitoring relatively easy. With the running of ongoing and upcoming large facilities/surveys in various electromagnetic and gravitational-wave bands, the era of time-domain surveys would guarantee constant detection of strongly lensed explosive transient events, for example, supernovae in all types, gamma ray bursts with afterglows in all bands, fast radio bursts, and even gravitational waves. Lensed transients have many advantages over the traditional targets in studying the Universe, and magnification effect helps to understand the transients themselves at high redshifts. In this review article, on base of the recent achievements in literature, we summarize the methods of searching for different kinds of lensed transient signals, the latest results on detection and their applications in fundamental physics, astrophysics, and cosmology. At the same time, we give supplementary comments as well as prospects of this emerging research direction that may help readers who are interested in entering this field.
Four-dimensional emittance measurements of ultrafast electron diffraction optics corrected up to sextupole order

Ultrafast electron diffraction (UED) is a technique in which short-pulse electron beams can probe the femtosecond-scale evolution of atomic structure in matter driven far from equilibrium. As an accelerator physics challenge, UED imposes stringent constraints on the brightness of the probe electron beam. The low normalized emittance employed in UED, often at the 10 nm scale and below, is particularly sensitive to both applied field aberrations and space charge effects. The role of aberrations is increasingly important in small probe systems that often feature multiple orders of magnitude variations in beam size during transport. In this work, we report the correction of normal quadrupole, skew quadrupole, and sextupole aberrations via dedicated corrector elements in an ultrafast electron micro-diffraction beamline. To do this, we generate precise 4-dimensional phase space maps of rf-compressed electron beams, and find excellent agreement with aberration-free space charge simulations. Finally, we discuss the role a probe-forming aperture can play in improving the brightness of bunches with appreciable space charge effects.
Generic Approach to Visualization of Time Series Data

- Time series is a collection of data instances that are ordered according to a time stamp. Stock prices, temperature, etc are examples of time series data in real life. Time series data are used for forecasting sales, predicting trends. Visualization is the process of visually representing data or the relationship between features of a data either in a two-dimensional plot or a three-dimensional plot. Visualizing the time series data constitutes an important part of the process for working with a time series dataset. Visualizing the data not only helps in the modelling process but it can also be used to identify trends and features that cause those trends. In this work, we take a real-life time series dataset and analyse how the target feature relates to other features of the dataset through visualization. From the work that has been carried out, we present an effective method of visualization for time series data which will be much useful for machine learning modelling with such datasets.
α ′ -corrected black holes in four and higher dimensions

We consider the low-energy eﬀective action of string theory at order α ′ , including R 2 -corrections to the Einstein-Hilbert gravitational action and non-trivial dilaton coupling. By means of a convenient ﬁeld redeﬁnition, we manage to express the theory in a frame that enables us to solve its ﬁeld equations analytically and perturbatively in α ′ for a static spherically symmetric ansatz in an arbitrary number of dimensions. The set of solutions we obtain is compatible with asymptotically ﬂat geometries exhibiting a regular event horizon at which the dilaton is well-behaved.
Neural Radiance Transfer Fields for Relightable Novel-view Synthesis with Global Illumination

Given a set of images of a scene, the re-rendering of this scene from novel views and lighting conditions is an important and challenging problem in Computer Vision and Graphics. On the one hand, most existing works in Computer Vision usually impose many assumptions regarding the image formation process, e.g. direct illumination and predefined materials, to make scene parameter estimation tractable. On the other hand, mature Computer Graphics tools allow modeling of complex photo-realistic light transport given all the scene parameters. Combining these approaches, we propose a method for scene relighting under novel views by learning a neural precomputed radiance transfer function, which implicitly handles global illumination effects using novel environment maps. Our method can be solely supervised on a set of real images of the scene under a single unknown lighting condition. To disambiguate the task during training, we tightly integrate a differentiable path tracer in the training process and propose a combination of a synthesized OLAT and a real image loss. Results show that the recovered disentanglement of scene parameters improves significantly over the current state of the art and, thus, also our re-rendering results are more realistic and accurate.
Clinical, Endocrine and Neuroimaging Findings in Girls with Central Precocious Puberty.

CONTEXT The etiology of central precocious puberty (CPP) include a spectrum of conditions. Girls with CPP younger than 6 should undergo cranial magnetic resonance imaging (MRI), but it remains controversial whether all girls who develop CPP between the ages of 6 and 8 require neuroimaging examination. OBJECTIVE To investigate the frequency of brain MRI abnormalities in girls diagnosed with CPP and the relationship between maternal factors, their age at presentation, clinical signs and symptoms, hormonal profiles and neuroimaging findings. METHODS Data from 112 girls who showed clinical pubertal progression before 8 years of age who underwent brain MRI were collected between January 2005 and September 2019. RESULTS MRI was normal in 47 (42%) idiopathics (I), 54 (48%) had hypothalamic-pituitary anomalies (HPA) and/or extra-HP anomalies (EHPA), and 11 (10%) had brain tumors or tumor-like conditions (BT/TL), including three with neurological signs. Associated pre-existing disorders were documented in 16. Girls with BT/TL had a higher LH peak after GnRH test (P=0.01) than I, and those over 6 years had a higher cranio-caudal diameter of the pituitary gland (P=0.01); their baseline FSH and LH (P=0.004), and peak FSH (P=0.01) and LH (P=0.05) values were higher than I. Logistic regression showed maternal age at menarche (P=0.02) and peak FSH (P=0.02) as BT/TL risk factors. CONCLUSIONS MRI provides valuable information in girls with CPP by demonstrating that less than half have a normal brain MRI and that few can have significant intracranial lesions after the age of 6, despite the absence of suggestive neurological signs.
Visualization deep learning model for automatic arrhythmias classification

Objective. With the improvement of living standards, heart disease has become one of the common diseases that threaten human health. Electrocardiography (ECG) is an effective way of diagnosing cardiovascular diseases. With the rapid growth of ECG examinations and the shortage of cardiologists, accurate and automatic arrhythmias classification has become a research hotspot. The main purpose of this paper is to improve accuracy in detecting abnormal ECG patterns. Approach. A hybrid 1D Resnet-GRU method, consisting of the Resnet and gated recurrent unit (GRU) modules, is proposed to implement classification of arrhythmias from 12-lead ECG recordings. In addition, the focal Loss function is used to solve the problem of unbalanced datasets. Based on the proposed 1D Resnet-GRU model, we use class-discriminative visualization to improve interpretability and transparency as an additional step. In this paper, the Grad-CAM++ mechanism has been employed to the trained network model and generate thermal images superimposed on raw signals to explore underlying explanations of various ECG segments. Main results. The experimental results show that the proposed method can achieve a high score of 0.821 (F1-score) in classifying 9 kinds of arrythmias, and Grad-CAM++ not only provides insight into the predictive power of the model, but is also consistent with the diagnostic approach of the arrhythmia classification. Significance. The proposed method can effectively select and integrate ECG features to achieve the goal of end-to-end arrhythmia classification by using 12-lead ECG signals, which can serve a promising and useful way for automatic arrhythmia classification, and can provide an explainable deep leaning model for clinical diagnosis.
Relationship Between Hypovitaminosis D and Fractures Among Adolescents With Overweight or Obesity

Adolescents with overweight/obesity are at risk for vitamin D insufficiency and deficiency. Both overweight/obesity and vitamin D insufficiency/deficiency may predispose to fractures. We enrolled 103 participants (53.3% females, 15.9 ± 2.2 years) in a retrospective case-control study to determine whether an association exists between fractures and a low 25-hydroxyvitamin D (25[OH]D) among adolescents whose body mass index (BMI) ≥ 85 percentile. Cases (n = 28) sustaining a low/medium impact fracture were matched to controls (n = 75) without a fracture history. A conditional-logistic regression analysis addressing the common vitamin D insufficiency/deficiency cutoffs was used. Overweight, obesity, and significant obesity rates were 10.7%, 53.4%, and 35.9%, respectively. Mean (±SD) 25(OH)D was 16.5 ± 6.4 ng/mL. In all, 25(OH)D insufficiency rates (level <20 ng/mL) were 70.5%. Matched cases and controls had similar 25(OH)D insufficiency/deficiency rates (P > .05). Controlling for race and seasonality showed no association between fractures and 25(OH)D insufficiency/deficiency (P > .05). These data suggest that fractures are not associated with low 25(OH)D levels among adolescents whose BMI ≥ 85th percentile.
COSMIC-RAY COMPOSITION AT 10$sup 17$-10$sup 18$ ev

Evidence is presented, based on the proton-energy dependence of the proportion of mu particles produced in proton-induced showers, that cosmic primaries having total energies greater than about 10/sup 17/ ev are nearly all protons. The ratio of the number of mu particles to the total number of particles is measured in air showers for which the primary energies are above 10/ sup 17/ ev, and the distributions indicate that these showers are produced by primary protons. (T.F.H.)
EXPOSURE RELATIONSHIP INFORMATION WITH THE LEVEL OF KNOWLEDGE AND BEHAVIOUR BSE IN WOMEN

The incidence of breast cancer by 40 per 100,000 women and will increase if there are one or more risk factors for breast cancer. Breast self-examination (BSE) is the early detection of breast cancer is the most widely recommended for every woman. The American Cancer Society recommends young women after the age of 20 to be screened with BSE every month. Nowadays there is a tendency of breast cancer experienced by women with age (15-20an). The purpose of this study to determine the relationship of information exposure to the level of knowledge of women about breast cancer and BSE behavior. Descriptive analytic research with cross sectional study sampled women aged 20-65 years living in the of Tegalrejo Yogyakarta total of 100 respondents. The sampling technique using a multistage random sampling. Bivariate data analysis using Chi Square with significance level ρ <0.05 and CI 95% . The results of chi-square analysis of a significant relationship between exposure information with the level of knowledge about breast cancer and BSE with a p-value (0.021) and the OR value of 4.015. The results of chi-square analysis of a significant relationship between exposure information with BSE practice with a p-value (0.002) and the OR value of 3,3. In conclusion, hhere is a relationship between the exposure of information to the level of knowledge and breast self examanation behavior.
Wavelet Improvement of the Over-Rejection of Unit Root Test Under GARCH Errors: An Application to Swedish Immigration Data

In this article, we use the wavelet technique to improve the over-rejection problem of the traditional Dickey–Fuller tests for unit root when the data is associated with volatility like the GARCH(1, 1) effect. The logic of this technique is based on the idea that the wavelet spectrum decomposition can separate out information of different frequencies in the data series. We prove that the asymptotic distribution of the test in the wavelet environment is still the same as the traditional Dickey–Fuller type of tests. The finite sample property is improved when the data suffers from GARCH error. The investigation of the size property and the finite sample distribution of the test is carried out by Monte Carlo experiment. An empirical example with data on the net immigration to Sweden during the period 1950–2000 is used to illustrate the performance of the wavelet improved test under GARCH errors. The results reveal that using the traditional Dickey–Fuller type of tests, the unit root hypothesis is rejected while our wavelet improved test do not reject as it is more robust to GARCH errors in finite samples.
R EFLECTIONS ON THE D EVELOPMENT OF THE M USICAL G ESTURES T OOLBOX FOR P YTHON

The paper presents the Musical Gestures Toolbox (MGT) for Python, a collection of modules targeted at researchers working with video recordings. The toolbox includes video visualization techniques such as creating motion videos, motion history images, and motiongrams. These visualizations allow for studying video recordings from different temporal and spatial perspectives. The toolbox also includes basic computer vision methods, and it is designed to integrate well with audio analysis toolboxes. The MGT was initially developed to analyze music-related body motion (of musicians, dancers, and perceivers) but is equally helpful for other disciplines working with video recordings of humans, such as linguistics, pedagogy, psychology, and medicine.