Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Filter Basis for Convolutional Neural Network Compression

Convolutional neural networks (CNNs) based solutions have achieved state-of-the-art performances for many computer vision tasks, including classification and super-resolution of images. Usually the success of these methods comes with a cost of millions of parameters due to stacking deep convolutional layers. Moreover, quite a large number of filters are also used for a single convolutional layer, which exaggerates the parameter burden of current methods. Thus, in this paper, we try to reduce the number of parameters of CNNs by learning a basis of the filters in convolutional layers. For the forward pass, the learned basis is used to approximate the original filters and then used as parameters for the convolutional layers. We validate our proposed solution for multiple CNN architectures on image classification and image super-resolution benchmarks and compare favorably to the existing state-of-the-art in terms of reduction of parameters and preservation of accuracy. Code is available at https://github.com/ofsoundof/learning_filter_basis
Graphical construction of spatial Gibbs random graphs

We consider a random graph model on [Formula: see text] that incorporates the interplay between the statistics of the graph and the underlying space where the vertices are located. Based on a graphical construction of the model as the invariant measure of a birth and death process, we prove the existence and uniqueness of a measure defined on graphs with vertices in [Formula: see text], which coincides with the limit along the measures over graphs with the finite vertex set. As a consequence, theoretical properties, such as exponential mixing of the infinite volume measure and central limit theorem for averages of a real-valued function of the graph, are obtained. Moreover, a perfect simulation algorithm based on the clan of ancestors is described in order to sample a finite window of the equilibrium measure defined on [Formula: see text].
Water vapor detection in the transmission spectra of HD 209458 b with the CARMENES NIR channel

Aims. We aim at detecting water vapor in the atmosphere of the hot Jupiter HD 209458 b and perform a multi-band study in the near infrared with CARMENES. Methods. The water vapor absorption lines from the atmosphere of the planet are Doppler-shifted due to the large change in its radial velocity during transit. This shift is of the order of tens of km s−1, whilst the Earth’s telluric and the stellar lines can be considered quasi-static. We took advantage of this shift to remove the telluric and stellar lines using SYSREM, which performs a principal component analysis including proper error propagation. The residual spectra contain the signal from thousands of planetary molecular lines well below the noise level. We retrieve the information from those lines by cross-correlating the residual spectra with models of the atmospheric absorption of the planet. Results. We find a cross-correlation signal with a signal-to-noise ratio (S/N) of 6.4, revealing H2O in HD 209458 b. We obtain a net blueshift of the signal of –5.2 −1.3+2.6 km s−1 that, despite the large error bars, is a firm indication of day- to night-side winds at the terminator of this hot Jupiter. Additionally, we performed a multi-band study for the detection of H2O individually from the three near infrared bands covered by CARMENES. We detect H2O from its 0.96–1.06 μm band with a S/N of 5.8, and also find hints of a detection from the 1.06–1.26 μm band, with a low S/N of 2.8. No clear planetary signal is found from the 1.26–1.62 μm band. Conclusions. Our significant H2O signal at 0.96–1.06 μm in HD 209458 b represents the first detection of H2O from this band individually, the bluest one to date. The unfavorable observational conditions might be the reason for the inconclusive detection from the stronger 1.15 and 1.4 μm bands. H2O is detected from the 0.96–1.06 μm band in HD 209458 b, but hardly in HD 189733 b, which supports a stronger aerosol extinction in the latter, in line with previous studies. Future data gathered at more stable conditions and with larger S/N at both optical and near-infrared wavelengths could help to characterize the presence of aerosols in HD 209458 b and other planets.
Using negative-latency gravitational wave alerts to detect prompt radio bursts from binary neutron star mergers with the Murchison Widefield Array

We examine how fast radio burst (FRB)-like signals predicted to be generated during the merger of a binary neutron star (BNS) may be detected in low-frequency radio observations triggered by the aLIGO/Virgo gravitational-wave detectors. The rapidity, directional accuracy, and sensitivity of follow-up observations with the Murchison Widefield Array (MWA) are considered. We show that with current methodology, the rapidity criterion fails for triggered MWA observations above 136 MHz for BNS mergers within the aLIGO/Virgo horizon, for which little dispersive delay is expected. A calculation of the expected reduction in response time by triggering on ‘negative latency’ alerts from aLIGO/Virgo observations of gravitational waves generated by the BNS inspiral is presented. This allows for observations up to 300 MHz where the radio signal is expected to be stronger. To compensate for the poor positional accuracy expected from these alerts, we propose a new MWA observational mode that is capable of viewing one-quarter of the sky. We show the sensitivity of this mode is sufficient to detect an FRB-like burst from an event similar to GW 170817 if it occurred during the ongoing aLIGO/Virgo third science run (O3).
Application of Randomized Quadrature Formulas to the Finite Element Method for Elliptic Equations

The implementation of the finite element method for linear elliptic equations requires to assemble the stiffness matrix and the load vector. In general, the entries of this matrix-vector system are not known explicitly but need to be approximated by quadrature rules. If the coefficient functions of the differential operator or the forcing term are irregular, then standard quadrature formulas, such as the barycentric quadrature rule, may not be reliable. In this paper we investigate the application of two randomized quadrature formulas to the finite element method for such elliptic boundary value problems with irregular coefficient functions. We give a detailed error analysis of these methods, discuss their implementation, and demonstrate their capabilities in several numerical experiments.
Fractals, nonextensive statistics, and QCD

In this work we analyse how scaling properties of Yang-Mills field theory manifest as self-similarity of truncated n-point functions by scale evolution. The presence of such structures, which actually behaves as fractals, allow for recurrent non-perturbative calculation of any vertex. Some general properties are indeed independent of the perturbative order, what simplifies the non-perturbative calculations. We show that for sufficiently high perturbative orders a statistical approach can be used, the non extensive statistics is obtained, and the Tsallis index, $q$, is deduced in terms of the field theory parameters. The results are applied to QCD in the one-loop approximation, where $q$ can be calculated, resulting in a good agreement with the value obtained experimentally. We discuss how this approach allows to understand some intriguing experimental findings in high energy collisions, as the behavior of multiplicity against collision energy, long-tail distributions and the fractal dimension observed in intermittency analysis.
Null Foliations of Spacetime and the Geometry of Black Hole Horizons

In this work, a method for constructing null foliations of spacetime is presented. This method is used to specify equivalence classes of null generators, whose representatives can be associated lightlike co-normals that are locally affine geodesic and thus locally orthogonal to embedded null hypersurfaces of spacetime. The main benefit of the proposed procedure is the fact that it is less geometrically restrictive than the traditional dual-null approaches to general relativity, but nevertheless allows for the conclusion that spacetimes can be foliated by suitable pairs of normalized null geodesic vector fields. This is demonstrated by the example of different black hole spacetimes, that is, by members of the Kerr-Newman family, according to which a said foliation and an associated equivalence class of null generators are explicitly constructed.
A threshold‐free summary index for quantifying the capacity of covariates to yield efficient treatment rules

When data on treatment assignment, outcomes, and covariates from a randomized trial are available, a question of interest is to what extent covariates can be used to optimize treatment decisions. Statistical hypothesis testing of covariate‐by‐treatment interaction is ill‐suited for this purpose. The application of decision theory results in treatment rules that compare the expected benefit of treatment given the patient's covariates against a treatment threshold. However, determining treatment threshold is often context‐specific, and any given threshold might seem arbitrary when the overall capacity towards predicting treatment benefit is of concern. We propose the Concentration of Benefit index (Cb), a threshold‐free metric that quantifies the combined performance of covariates towards finding individuals who will benefit the most from treatment. The construct of the proposed index is comparing expected treatment outcomes with and without knowledge of covariates when one of a two randomly selected patients is to be treated. We show that the resulting index can also be expressed in terms of the integrated efficiency of individualized treatment decision over the entire range of treatment thresholds. We propose parametric and semiparametric estimators, the latter being suitable for out‐of‐sample validation and correction for optimism. We used data from a clinical trial to demonstrate the calculations in a step‐by‐step fashion. The proposed index has intuitive and theoretically sound interpretation and can be estimated with relative ease for a wide class of regression models. Beyond the conceptual developments, various aspects of estimation and inference for such a metric need to be pursued in future research. R code that implements the method for a variety of regression models is provided at (https://github.com/msadatsafavi/txBenefit).
Nonequilibrium orbital transitions via applied electrical current in calcium ruthenates

Simultaneous control of structural and physical properties via applied electrical current poses a key, new research topic and technological significance. Studying the spin-orbit-coupled antiferromagnet Ca2RuO4, with 3% Mn doping to weaken the violent first-order transition at 357 K for more robust measurements, we find that a small applied electrical current couples to the lattice by significantly reducing its orthorhombicity and octahedral rotations, concurrently diminishing the 125 K- antiferromagnetic transition and inducing a new, orbital order below 80 K. Our effort to establish a phase diagram reveals a critical regime near a current density of 0.15 A/cm2 that separates the vanishing antiferromagnetic order and the new orbital order. Further increasing current density (> 1 A/cm2) enhances competitions between relevant interactions in a metastable manner, leading to a peculiar glassy behavior above 80 K. The coupling between the lattice and nonequilibrium driven current is interpreted theoretically in terms of t2g orbital occupancies. The current-controlled lattice is the driving force of the observed novel phenomena.
Efficient Deep Neural Networks

Author(s): Wu, Bichen | Advisor(s): Keutzer, Kurt W | Abstract: The success of deep neural networks (DNNs) is attributable to three factors: increased compute capacity, more complex models, and more data. These factors, however, are not always present, especially for edge applications such as autonomous driving, augmented reality, and internet-of-things. Training DNNs requires a large amount of data, which is difficult to obtain. Edge devices such as mobile phones have limited compute capacity, and therefore, require specialized and efficient DNNs. However, due to the enormous design space and prohibitive training costs, designing efficient DNNs for different target devices is challenging. So the question is, with limited data, compute capacity, and model complexity, can we still successfully apply deep neural networks? This dissertation focuses on the above problems and improving the efficiency of deep neural networks at four levels. Model efficiency: we designed neural networks for various computer vision tasks and achieved more than 10x faster speed and lower energy. Data efficiency: we developed an advanced tool that enables 6.2x faster annotation of a LiDAR point cloud. We also leveraged domain adaptation to utilize simulated data, bypassing the need for real data. Hardware efficiency: we co-designed neural networks and hardware accelerators and achieved 11.6x faster inference. Design efficiency: the process of finding the optimal neural networks is time-consuming. Our automated neural architecture search algorithms discovered, using 421x lower computational cost than previous search methods, models with state-of-the-art accuracy and efficiency.
Rank shift conditions and reductions of 2d-Toda theory

This paper focuses on different reductions of 2-dimensional (2d-)Toda hierarchy. Symmetric and skew symmetric moment matrices are firstly considered, resulting in the differential relations between symmetric/skew symmetric tau functions and 2d-Toda's tau functions, respectively. Furthermore, motivated by the Cauchy two-matrix model and Bures ensemble from random matrix theory, we study the rank one shift condition in symmetric case and rank two shift condition in skew symmetric case, from which the C-Toda hierarchy and B-Toda hierarchy are found respectively, together with their special Lax matrices and integrable structures.
Exploring the multiphase medium in MKW 08: from the central active galaxy up to cluster scales

Context. The study of the brightest cluster galaxy (BCG) coronae embedded in noncool core (NCC) galaxy clusters is crucial to understand the BCG’s role in galaxy cluster evolution as well as the activation of the self-regulated cooling and heating mechanism in the central regions of galaxy clusters. Aims. We explore the X-ray properties of the intracluster medium (ICM) of the NCC galaxy cluster MKW 08 and the BCG corona, along with their interface region. With recent and deep archival Chandra observations, we study the BCG corona in detail, and with archival XMM-Newton observations, we investigate the implications of the central active galactic nuclei (AGN) on the BCG. Methods. We carry out imaging and spectral analyses of MKW 08 with archival XMM-Newton and Chandra X-ray observations. Results. Our spectral analysis suggests the presence of a central AGN by a power-law with a photon index of Γ ≃ 1.8 at the core of its BCG. Although the ICM does not exhibit a cluster scale cool core, the BCG manifests itself as a mini cool core characterized by a cooling time as short as 64 Myr at r = 3 kpc centered at the galaxy. The isothermality of the BCG corona seems to favor mechanical feedback from the central AGN as the major source of gas heating. The gas pressure profile of this mini cool core suggests that the BCG coronal gas reaches pressure equilibrium with the hotter and less dense ICM inside an interface of nearly constant pressure, delimited by radii 4 ≤ r ≤ 10 kpc at the galactic center. As revealed by the presence of a metal enriched tail (Z ≃ 0.5–0.9 Z⊙) extending up to 40 kpc, the BCG corona seems to be experiencing ram-pressure stripping by the surrounding ICM and/or interacting with a nearby galaxy, IC 1042.
A new sample of southern radio galaxies: host-galaxy masses and star-formation rates

In this study we define a new sample of distant powerful radio galaxies in order to study their host galaxy properties and provide targets for future observations of H i absorption with new radio telescopes and to understand the fuelling and feedback from such sources. We have cross-matched the Sydney University Molonglo Sky Survey (SUMSS) radio catalogue at 843 MHz with the VISTA Hemisphere Survey (VHS) near-infrared catalogue using the likelihood ratio technique. Photometric redshifts from the Dark Energy Survey are then used to assign redshifts to the radio source counterparts. We found a total of 249 radio sources with photometric redshifts over a 148 deg2 region. By fitting the optical and near-infrared photometry with spectral synthesis models we determine the stellar mass and star formation rates of the radio sources, finding typical stellar masses of 1011–1012 M⊙ for the powerful high-redshift radio galaxies. We also find a population of low-mass blue galaxies. However, by comparing the derived star formation rates to the radio luminosity, we suggest that these sources are false positives in our likelihood ratio analysis. Follow-up, higher resolution (≲5 arcsec) radio imaging would help alleviate these mid-identifications, as the limiting factor in our cross-identifications is the low resolution (∼45 arcsec) of the SUMSS radio imaging.
Runtime Analysis of Fitness-Proportionate Selection on Linear Functions

This paper extends the runtime analysis of non-elitist evolutionary algorithms (EAs) with fitness-proportionate selection from the simple OneMax function to the linear functions. Not only does our analysis cover a larger class of fitness functions, it also holds for a wider range of mutation rates. We show that with overwhelmingly high probability, no linear function can be optimised in less than exponential time, assuming bitwise mutation rate $\Theta(1/n)$ and population size $\lambda=n^k$ for any constant $k>2$. In contrast to this negative result, we also show that for any linear function with polynomially bounded weights, the EA achieves a polynomial expected runtime if the mutation rate is reduced to $\Theta(1/n^2)$ and the population size is sufficiently large. Furthermore, the EA with mutation rate $\chi/n=\Theta(1/n)$ and modest population size $\lambda=\Omega(\ln n)$ optimises the scaled fitness function $e^{(\chi+\varepsilon)f(x)}$ for any linear function $f$ and any $\varepsilon>0$ in expected time $O(n\lambda\ln\lambda+n^2)$. These upper bounds also extend to some additively decomposed fitness functions, such as the Royal Road functions. We expect that the obtained results may be useful not only for the development of the theory of evolutionary algorithms, but also for biological applications, such as the directed evolution.
Effects of sense of control and social presence on customer experience and e-service quality

This study considers sense of control and social presence as major design attributes and explores their influence on customers’ affective responses and cognitive evaluations of e-services. In an experimental study, we constructed a hypothetical news portal offering varying levels of user control and social presence. Statistical analyses of the data indicate that websites designed to provide a greater sense of control and social presence result in greater pleasure and arousal among users, more positive evaluations of e-service quality, and stronger revisit intentions for the e-service platform. A well-designed website interface with an appropriate social presence and customizable interface appears to create an optimal online environment for offering desirable service experiences and thereby retaining customers.
An 11 Earth-mass, Long-period Sub-Neptune Orbiting a Sun-like Star

Although several thousands of exoplanets have now been detected and characterized, observational biases have led to a paucity of long-period, low-mass exoplanets with measured masses and a corresponding lag in our understanding of such planets. In this paper we report the mass estimation and characterization of the long-period exoplanet Kepler-538b. This planet orbits a Sun-like star (V = 11.27) with M⊙ and R⊙. Kepler-538b is a R⊕ sub-Neptune with a period of P = 81.73778 ± 0.00013 days. It is the only known planet in the system. We collected radial velocity (RV) observations with the High Resolution Echelle Spectrometer (HIRES) on Keck I and High Accuracy Radial velocity Planet Searcher in North hemisphere (HARPS-N) on the Telescopio Nazionale Galileo (TNG). We characterized stellar activity by a Gaussian process with a quasi-periodic kernel applied to our RV and cross-correlation function FWHM observations. By simultaneously modeling Kepler photometry, RV, and FWHM observations, we found a semi-amplitude of m s−1 and a planet mass of M⊕. Kepler-538b is the smallest planet beyond P = 50 days with an RV mass measurement. The planet likely consists of a significant fraction of ices (dominated by water ice), in addition to rocks/metals, and a small amount of gas. Sophisticated modeling techniques such as those used in this paper, combined with future spectrographs with ultra high-precision and stability will be vital for yielding more mass measurements in this poorly understood exoplanet regime. This in turn will improve our understanding of the relationship between planet composition and insolation flux and how the rocky to gaseous transition depends on planetary equilibrium temperature.
IN PERFORMANCE-BASED LANGUAGE ASSESSMENT : RATING SCALES AND RATER TRAINING

Performance-based assessment has gained more attention from ELT practitioners since the actual performances produced by students are evaluated in this type of assessment. However, the assessment of students’ performances involves more complicated procedures when compared with more traditional testing methods. This paper, therefore, points out crucial considerations in adopting this type of assessment in a language class. First, the article introduces the concepts of performance-based language assessment and its major characteristics. Then, the two main characteristics: rating scales and rater training are discussed. In the rating scales section, different types of rating scales, as well as approaches in developing rating scales, are explored with the emphasis on the scales used for assessing writing. Finally, the paper presents the roles of rater training in performance-based assessment and how to conduct such training.
1 THE GENETIC ARCHITECTURE OF NEURODEVELOPMENTAL DISORDERS

There are several hundred known genetic syndromes that affect neural development and result in intellectual disability (ID), epilepsy, or other neurological or psychiatric symptoms. These include recognized syndromes that often manifest with symptoms of autism spectrum disorders (ASD) or schizophrenia (SZ), such as Fragile X syndrome, Rett syndrome, tuberous sclerosis, velocardio-facial syndrome, and many others. For ASD, it has been known for many years that these syndromes account for a significant but still small fraction (5–10%) of all cases (Miles, 2011). What has not been clear is whether such cases, associated with single mutations, represent a typical mode by which such conditions arise or are, alternatively, exceptional and quite distinct from the general etiology of idiopathic ASD, epilepsy, SZ, or ID (Wray and Visscher, 2010). Other common disorders including dyslexia, specific language impairment, obessive-compulsive disorder, and