Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Generalization by Controlling Label-Noise Information in Neural Network Weights

In the presence of noisy or incorrect labels, neural networks have the undesirable tendency to memorize information about the noise. Standard regularization techniques such as dropout, weight decay or data augmentation sometimes help, but do not prevent this behavior. If one considers neural network weights as random variables that depend on the data and stochasticity of training, the amount of memorized information can be quantified with the Shannon mutual information between weights and the vector of all training labels given inputs, $I(w : \mathbf{y} \mid \mathbf{x})$. We show that for any training algorithm, low values of this term correspond to reduction in memorization of label-noise and better generalization bounds. To obtain these low values, we propose training algorithms that employ an auxiliary network that predicts gradients in the final layers of a classifier without accessing labels. We illustrate the effectiveness of our approach on versions of MNIST, CIFAR-10, and CIFAR-100 corrupted with various noise models, and on a large-scale dataset Clothing1M that has noisy labels.
Bayesian gradient sensing in the presence of rotational diffusion

Biological cells estimate concentration gradients of signaling molecules with a precision that is limited not only by sensing noise, but additionally by the cell’s own stochastic motion. We ask for the theoretical limits of gradient estimation in the presence of both motility and sensing noise. We introduce a minimal model of a stationary chemotactic agent in the plane subject to rotational diffusion with rotational diffusion coefficient D. The agent uses Bayesian estimation to optimally infer the gradient direction relative to itself from noisy concentration measurements. Meanwhile, this direction changes on a time-scale 1/D. We show that the optimal effective measurement time, which characterizes the time interval over which past gradient measurements should be averaged to reduce sensing noise, does not scale with the rotational diffusion time 1/D, but with the square root (rD)−1/2, where r is a rate of information gain defined as a signal-to-noise ratio normalized per unit time. This result for gradient sensing parallels a recent result by Mora et al (2019 Phys. Rev. Lett.) for sensing absolute concentration in time-varying environments.
Seasonal and Trend Forecasting of Tourist Arrivals: An Adaptive Multiscale Ensemble Learning Approach

The accurate seasonal and trend forecasting of tourist arrivals is a very challenging task. In the view of the importance of seasonal and trend forecasting of tourist arrivals, and limited research work paid attention to these previously. In this study, a new adaptive multiscale ensemble (AME) learning approach incorporating variational mode decomposition (VMD) and least square support vector regression (LSSVR) is developed for short-, medium-, and long-term seasonal and trend forecasting of tourist arrivals. In the formulation of our developed AME learning approach, the original tourist arrivals series are first decomposed into the trend, seasonal and remainders volatility components. Then, the ARIMA is used to forecast the trend component, the SARIMA is used to forecast seasonal component with a 12-month cycle, while the LSSVR is used to forecast remainder volatility components. Finally, the forecasting results of the three components are aggregated to generate an ensemble forecasting of tourist arrivals by the LSSVR based nonlinear ensemble approach. Furthermore, a direct strategy is used to implement multi-step-ahead forecasting. Taking two accuracy measures and the Diebold-Mariano test, the empirical results demonstrate that our proposed AME learning approach can achieve higher level and directional forecasting accuracy compared with other benchmarks used in this study, indicating that our proposed approach is a promising model for forecasting tourist arrivals with high seasonality and volatility.
Distributed Algorithms that Solve Boolean Equation Systems

In this paper, we propose distributed algorithms that solve a system of Boolean equations over a network, where each node in the network possesses only one Boolean equation from the system. The Boolean equation assigned at any particular node is a private equation known to this node only, and the nodes aim to compute the exact set of solutions to the system without exchanging their local equations. We show that each private Boolean equation can be locally lifted to a linear algebraic equation under a basis of Boolean vectors, leading to a network linear equation that is distributedly solvable. A number of exact or approximate solutions to the induced linear equation are then computed at each node from different initial values. The solutions to the original Boolean equations are eventually computed locally via a Boolean vector search algorithm. We prove that given solvable Boolean equations, when the initial values of the nodes for the distributed linear equation solving step are i.i. d selected according to a uniform distribution in a high-dimensional cube, our algorithms return the exact solution set of the Boolean equations at each node with high probability. Furthermore, we present an algorithm for distributed verification of the satisfiability of Boolean equations, and prove its correctness. The proposed algorithms put together become a complete framework for distributedly solving Boolean equations: verifying satisfiability and computing the solution set whenever satisfiable.
Characterizing the spatial pattern of solar supergranulation using the bispectrum

Context. The spatial power spectrum of supergranulation does not fully characterize the underlying physics of turbulent convection. For example, it does not describe the non-Gaussianity in the horizontal flow divergence. Aims. Our aim is to statistically characterize the spatial pattern of solar supergranulation beyond the power spectrum. The next-order statistic is the bispectrum. It measures correlations of three Fourier components and is related to the nonlinearities in the underlying physics. It also characterizes how a skewness in the dataset is generated by the coupling of three Fourier components. Methods. We estimated the bispectrum of supergranular horizontal surface divergence maps that were obtained using local correlation tracking (LCT) and time-distance helioseismology (TD) from one year of data from the helioseismic and magnetic imager on-board the solar dynamics observatory starting in May 2010. Results. We find significantly nonzero and consistent estimates for the bispectrum using LCT and TD. The strongest nonlinearity is present when the three coupling wave vectors are at the supergranular scale. These are the same wave vectors that are present in regular hexagons, which have been used in analytical studies of solar convection. At these Fourier components, the bispectrum is positive, consistent with the positive skewness in the data and consistent with supergranules preferentially consisting of outflows surrounded by a network of inflows. We use the bispectral estimates to generate synthetic divergence maps that are very similar to the data. This is done by a model that consists of a Gaussian term and a weaker quadratic nonlinear component. Using this method, we estimate the fraction of the variance in the divergence maps from the nonlinear component to be of the order of 4–6%. Conclusions. We propose that bispectral analysis is useful for understanding the dynamics of solar turbulent convection, for example for comparing observations and numerical models of supergranular flows. This analysis may also be useful to generate synthetic flow fields.
Project AMIGA: The Circumgalactic Medium of Andromeda

Project AMIGA (Absorption Maps In the Gas of Andromeda) is a survey of the circumgalactic medium (CGM) of Andromeda (M31, ≃ 300 kpc) along 43 QSO sightlines at impact parameters 25 ≤ R ≤ 569 kpc (25 at R ≲ ). We use ultraviolet absorption measurements of Si ii, Si iii, Si iv, C ii, and C iv from the Hubble Space Telescope/Cosmic Origins Spectrograph and O vi from the Far Ultraviolet Spectroscopic Explorer to provide an unparalleled look at how the physical conditions and metals are distributed in the CGM of M31. We find that Si iii and O vi have a covering factor near unity for R ≲ 1.2 and ≲1.9 , respectively, demonstrating that M31 has a very extended ∼104–105.5 K ionized CGM. The metal and baryon masses of the 104–105.5 K CGM gas within are ≳108 and ≳4 × 1010 (Z/0.3 Z⊙)−1 M⊙, respectively. There is not much azimuthal variation in the column densities or kinematics, but there is with R. The CGM gas at R ≲ 0.5 is more dynamic and has more complicated, multiphase structures than at larger radii, perhaps a result of more direct impact of galactic feedback in the inner regions of the CGM. Several absorbers are projected spatially and kinematically close to M31 dwarf satellites, but we show that those are unlikely to give rise to the observed absorption. Cosmological zoom simulations of ∼L* galaxies have O vi extending well beyond as observed for M31 but do not reproduce well the radial column density profiles of the lower ions. However, some similar trends are also observed, such as the lower ions showing a larger dispersion in column density and stronger dependence on R than higher ions. Based on our findings, it is likely that the Milky Way has a ∼104–105.5 K CGM as extended as for M31 and their CGM (especially the warm–hot gas probed by O vi) are overlapping.
Fragmentation modelling of the 2019 August impact on Jupiter

On 7th August 2019, an impact flash lasting $\sim1$s was observed on Jupiter. The video of this event was analysed to obtain the lightcurve and determine the energy release and initial mass. We find that the impactor released a total energy of $96-151$ kilotons of TNT, corresponding to an initial mass between $190-260$ metric tonnes with a diameter between $4-10$m. We developed a fragmentation model to simulate the atmospheric breakup of the object and reproduce the lightcurve. We model three different materials: cometary, stony and metallic at speeds of $60$, $65 $ and $70$ km/s to determine the material makeup of the impacting object. The slower cases are best fit by a strong, metallic object while the faster cases require a weaker material.
Can we detect the stellar differential rotation of WASP-7 through the Rossiter–McLaughlin observations?

The Rossiter-McLaughlin (RM) effect is the radial velocity signal generated when an object transits a rotating star. Stars rotate differentially and this affects the shape and amplitude of this signal, on a level that can no longer be ignored with precise spectrographs. Highly misaligned planets provide a unique opportunity to probe stellar differential rotation via the RM effect, as they cross several stellar latitudes. In this sense, WASP-7, and its hot Jupiter with a projected misalignment of \sim 90°, is one of the most promising targets. The aim of this work is to understand if the stellar differential rotation is measurable through the RM signal for systems with a geometry similar to WASP-7. In this sense, we use a modified version of SOAP3.0 to explore the main hurdles that prevented the precise determination of the differential rotation of WASP-7. We also investigate whether the adoption of the next generation spectrographs, like ESPRESSO, would solve these issues. Additionally, we assess how instrumental and stellar noise influence this effect and the derived geometry of the system. We found that, for WASP-7, the white noise represents an important hurdle in the detection of the stellar differential rotation, and that a precision of at least 2m/s or better is essential.
The convex invertible cone structure of positive real odd rational matrix functions

Positive real odd matrix functions, often referred to as positive real lossless matrix functions, play an important role in many applications in multi-port electrical systems. In this paper we present closer analogues to some of the known results for the scalar, one-port, case in the multi-port setting. Specifically, we determine necessary and sufficient conditions for the well studied partial fraction formula to represent functions in the class of positive real odd matrix functions, and explicit minimal state space realization formulas for the inverse (admittance) of a function in this class, which itself is also a positive real odd matrix function. Doing so, enables us to provide a partial analogue of the poles-zero interlacing behavior from the scalar case.
Re-evaluating scaling methods for distributed parallel systems

The paper explains why Amdahl's Law shall be interpreted specifically for distributed parallel systems and why it generated so many debates, discussions, and abuses. We set up a general model and list many of the terms affecting parallel processing. We scrutinize the validity of neglecting certain terms in different approximations, with special emphasis on the famous scaling laws of parallel processing. We clarify that when using the right interpretation of terms, Amdahl's Law is the governing law of all kinds of parallel processing. Amdahl's Law describes among others the history of supercomputing, the inherent performance limitation of the different kinds of parallel processing and it is the basic Law of the 'modern computing' paradigm, that the computing systems working under extreme computing conditions are desperately needed.
Hierarchical models vs. transfer learning for document-level sentiment classification

Documents are composed of smaller pieces - paragraphs, sentences, and tokens - that have complex relationships between one another. Sentiment classification models that take into account the structure inherent in these documents have a theoretical advantage over those that do not. At the same time, transfer learning models based on language model pretraining have shown promise for document classification. However, these two paradigms have not been systematically compared and it is not clear under which circumstances one approach is better than the other. In this work we empirically compare hierarchical models and transfer learning for document-level sentiment classification. We show that non-trivial hierarchical models outperform previous baselines and transfer learning on document-level sentiment classification in five languages.
ZFIRE: Measuring Electron Density with [O ii] as a Function of Environment at z = 1.62

The global star formation rates (SFR) of galaxies at fixed stellar masses increase with redshift and are known to vary with environment up to z ∼ 2. We explore here whether the changes in the SFRs also apply to the electron densities of the interstellar medium by measuring the [O ii] ( , ) ratio for cluster and field galaxies at z ∼ 2. We measure a median electron density of = 366 ± 84 cm for six galaxies (with 1σ scatter = 163 cm ) in the Ultra-Deep Survey (UDS) protocluster at z = 1.62. We find that the median electron density of galaxies in the UDS protocluster environment is three times higher compared to the median electron density of field galaxies ( = 113 ± 63 cm and 1σ scatter = 79 cm ) at comparable redshifts, stellar mass, and SFR. However, we note that a sample of six protocluster galaxies is insufficient to reliably measure the electron density in the average protocluster environment at z ∼ 2. We conclude that the electron density increases with redshift in both cluster and field environments up to z ∼ 2 ( = 30 ± 1 cm for z ∼ 0 to = 254 ± 76 cm for z ∼ 1.5). We find tentative evidence (∼2.6σ) for a possible dependence of electron density on environment, but the results require confirmation with larger sample sizes.
A Simplified Fully Quantized Transformer for End-to-end Speech Recognition

While significant improvements have been made in recent years in terms of end-to-end automatic speech recognition (ASR) performance, such improvements were obtained through the use of very large neural networks, unfit for embedded use on edge devices. That being said, in this paper, we work on simplifying and compressing Transformer-based encoder-decoder architectures for the end-to-end ASR task. We empirically introduce a more compact Speech-Transformer by investigating the impact of discarding particular modules on the performance of the model. Moreover, we evaluate reducing the numerical precision of our network's weights and activations while maintaining the performance of the full-precision model. Our experiments show that we can reduce the number of parameters of the full-precision model and then further compress the model 4x by fully quantizing to 8-bit fixed point precision.
On Adaptive Attacks to Adversarial Example Defenses

Adaptive attacks have (rightfully) become the de facto standard for evaluating defenses to adversarial examples. We find, however, that typical adaptive evaluations are incomplete. We demonstrate that thirteen defenses recently published at ICLR, ICML and NeurIPS---and chosen for illustrative and pedagogical purposes---can be circumvented despite attempting to perform evaluations using adaptive attacks. While prior evaluation papers focused mainly on the end result---showing that a defense was ineffective---this paper focuses on laying out the methodology and the approach necessary to perform an adaptive attack. We hope that these analyses will serve as guidance on how to properly perform adaptive attacks against defenses to adversarial examples, and thus will allow the community to make further progress in building more robust models.
Discovery of a pre-cataclysmic binary with unusual chromaticity of the eclipsed white dwarf by the GPX survey

We report the discovery of a relatively bright eclipsing binary system, which consists of a white dwarf and a main sequence K7 star with clear signs of chromospheric and spot activity. The light curve of this system shows $\sim0.2$mag ellipsoidal variability with a period of 0.297549d and a short total eclipse of the white dwarf. Based on our analysis of the spectral and photometric data, we estimated the parameters of the system. The K7V star is tidally deformed but does not fill its Roche lobe (the filling factor is about 0.86). The orbital inclination is $i=73^\circ.1\pm 0^\circ.2$, the mass ratio is $q=M_2/M_1\approx 0.88$. The parameters of the K7V star are $M_2\approx 0.64$M$_{\odot}$, $R_2=0.645\pm 0.012$R$_{\odot}$, $T_2\approx 4070$K. The parameters of the white dwarf are $M_1\approx 0.72$M$_{\odot}$, $R_1=0.013\pm 0.003$R$_{\odot}$, $T_1=8700\pm 1100$K. Photometric observations in different bands revealed that the maximum depth of the eclipse is in the \textit{SDSS r} filter, which is unusual for a system of a white dwarf and a late main sequence star. We suspect that this system is a product of the evolution of a common envelope binary star, and that the white dwarf accretes the stellar wind from the secondary star (the so-called low-accretion rate polar, LARP).
Survey on Individual Differences in Visualization

Developments in data visualization research have enabled visualization systems to achieve great general usability and application across a variety of domains. These advancements have improved not only people's understanding of data, but also the general understanding of people themselves, and how they interact with visualization systems. In particular, researchers have gradually come to recognize the deficiency of having one‐size‐fits‐all visualization interfaces, as well as the significance of individual differences in the use of data visualization systems. Unfortunately, the absence of comprehensive surveys of the existing literature impedes the development of this research. In this paper, we review the research perspectives, as well as the personality traits and cognitive abilities, visualizations, tasks, and measures investigated in the existing literature. We aim to provide a detailed summary of existing scholarship, produce evidence‐based reviews, and spur future inquiry.
1324- and 2143-avoiding Kazhdan–Lusztig immanants and k-positivity

Abstract Immanants are functions on square matrices generalizing the determinant and permanent. Kazhdan–Lusztig immanants, which are indexed by permutations, involve $q=1$ specializations of Type A Kazhdan–Lusztig polynomials, and were defined by Rhoades and Skandera (2006, Journal of Algebra 304, 793–811). Using results of Haiman (1993, Journal of the American Mathematical Society 6, 569–595) and Stembridge (1991, Bulletin of the London Mathematical Society 23, 422–428), Rhoades and Skandera showed that Kazhdan–Lusztig immanants are nonnegative on matrices whose minors are nonnegative. We investigate which Kazhdan–Lusztig immanants are positive on k-positive matrices (matrices whose minors of size $k \times k$ and smaller are positive). The Kazhdan–Lusztig immanant indexed by v is positive on k-positive matrices if v avoids 1324 and 2143 and for all noninversions $i< j$ of v, either $j-i \leq k$ or $v_j-v_i \leq k$ . Our main tool is Lewis Carroll’s identity.
Gauss–Bonnet–Chern approach to the averaged Universe

The standard model of cosmology with postulated dark energy and dark matter sources may be considered as a fairly successful fitting model to observational data. However, this model leaves the question of the physical origin of these dark components open. Fully relativistic contributions that act like dark energy on large scales and like dark matter on smaller scales can be found through generalization of the standard model by spatially averaging the inhomogeneous Universe within general relativity. The spatially averaged 3 + 1 Einstein equations are effective balance equations that need a closure condition. Heading for closure we here explore topological constraints. Results are straightforwardly obtained for averaged 2 + 1 model universes. For the relevant 3 + 1 case, we employ a method based on the Gauss–Bonnet–Chern theorem generalized to Lorentzian spacetimes and implement a sandwich approach to obtain spatial average properties. The 3 + 1 topological approach supplies us with a new equation linking evolution of scalar invariants of the expansion tensor to the norm of the Weyl tensor. From this we derive general evolution equations for averaged scalar curvature and kinematical backreaction, and we discuss related evolution equations on this level of the hierarchy of averaged equations. We also discuss the relation between topological properties of cosmological manifolds and dynamical topology change, e.g. as resulting from the formation of black holes.
Does more involved fathering imply a double burden for fathers in Norway

While long total work hours (paid plus unpaid work) have usually been framed as a problem for employed women, researchers now ask whether more involved fathering practices imply a double burden for men, too. Based on the Norwegian Time Use Survey 2010, and using three different measures of total workload, our analyses suggest that the father's total workload exceeds the mother's when he works full time and she part time and there are school-aged children in the household. Fathers also perceive more time pressure than mothers in these couples. Full-time work for both partners may give a longer total workload for mothers, but the difference is more modest than in full-time / part-time couples and is not statistically significant in our sample. Gender differences in total workload vary during the week, with longest hours for fathers on weekdays, and longest hours for mothers on weekends.