Miracle-dz/UniFAR-FTUs huggingface dataset:
Target-Embedding Autoencoders for Supervised Representation Learning

Autoencoder-based learning has emerged as a staple for disciplining representations in unsupervised and semi-supervised settings. This paper analyzes a framework for improving generalization in a purely supervised setting, where the target space is high-dimensional. We motivate and formalize the general framework of target-embedding autoencoders (TEA) for supervised prediction, learning intermediate latent representations jointly optimized to be both predictable from features as well as predictive of targets---encoding the prior that variations in targets are driven by a compact set of underlying factors. As our theoretical contribution, we provide a guarantee of generalization for linear TEAs by demonstrating uniform stability, interpreting the benefit of the auxiliary reconstruction task as a form of regularization. As our empirical contribution, we extend validation of this approach beyond existing static classification applications to multivariate sequence forecasting, verifying their advantage on both linear and nonlinear recurrent architectures---thereby underscoring the further generality of this framework beyond feedforward instantiations.
Studies in Astronomical Time-series Analysis. VII. An Enquiry Concerning Nonlinearity, the rms–Mean Flux Relation, and Lognormal Flux Distributions

A broad and widely used class of stationary, linear, additive time-series models can have statistical properties that many authors have asserted imply that the underlying process must be nonlinear, nonstationary, multiplicative, or inconsistent with shot noise. This result is demonstrated with exact and numerical evaluation of the model flux distribution function and dependence of flux standard deviation on mean flux (here and in the literature called the rms–flux relation). These models can (1) exhibit normal, lognormal, or other flux distributions; (2) show linear or slightly nonlinear rms–mean flux dependencies; and (3) match arbitrary second-order statistics of the time-series data. Accordingly, the above assertions cannot be made on the basis of statistical time-series analysis alone. Also discussed are ambiguities in the meaning of terms relevant to this study—linear, stationary, and multiplicative—and functions that can transform observed fluxes to a normal distribution as well as or better than the logarithm.
Instability of algebraic standing waves for nonlinear Schrödinger equations with double power nonlinearities

We consider a nonlinear Schr\"odinger equation with double power nonlinearity \begin{align*} i\partial_t u+\Delta u-|u|^{p-1}u+|u|^{q-1}u=0,\quad (t,x)\in\mathbb{R}\times\mathbb{R}^N, \end{align*} where $1<p<q<1+4/(N-2)_+$. Due to the defocusing effect from the lower power order nonlinearity, the equation has algebraically decaying standing waves with zero frequency, which we call algebraic standing waves, as well as usual standing waves decaying exponentially with positive frequency. In this paper we study stability properties of two types of standing waves. We prove strong instability for all frequencies when $q\ge 1+4/N$ and instability for small frequencies when $q<1+4/N$, which especially give the first results on stability properties of algebraic standing waves. The instability result for small positive frequency when $q<1+4/N$ not only improves previous results in one-dimensional case but also gives a first result on instability in higher-dimensional case. The key point in our approach is to take advantage of algebraic standing waves.
Ternary Feature Masks: continual learning without any forgetting

In this paper, we propose an approach without any forgetting to continual learning for the task-aware regime, where at inference the task-label is known. By using ternary masks we can upgrade a model to new tasks, reusing knowledge from previous tasks while not forgetting anything about them. Using masks prevents both catastrophic forgetting and backward transfer. We argue -- and show experimentally -- that avoiding the former largely compensates for the lack of the latter, which is rarely observed in practice. In contrast to earlier works, our masks are applied to the features (activations) of each layer instead of the weights. This considerably reduces the number of mask parameters to be added for each new task; with more than three orders of magnitude for most networks. The encoding of the ternary masks into two bits per feature creates very little overhead to the network, avoiding scalability issues. Our masks do not permit any changes to features which are used by previous tasks. As this may be too restrictive to allow learning of new tasks, we add task-specific feature normalization. This way, already learned features can adapt to the current task without changing the behavior of these features for previous tasks. Extensive experiments on several finegrained datasets and ImageNet show that our method outperforms current state-of-the-art while reducing memory overhead in comparison to weight-based approaches.
Quantifying Engagement with Citations on Wikipedia

Wikipedia is one of the most visited sites on the Web and a common source of information for many users. As an encyclopedia, Wikipedia was not conceived as a source of original information, but as a gateway to secondary sources: according to Wikipedia’s guidelines, facts must be backed up by reliable sources that reflect the full spectrum of views on the topic. Although citations lie at the heart of Wikipedia, little is known about how users interact with them. To close this gap, we built client-side instrumentation for logging all interactions with links leading from English Wikipedia articles to cited references during one month, and conducted the first analysis of readers’ interactions with citations. We find that overall engagement with citations is low: about one in 300 page views results in a reference click (0.29% overall; 0.56% on desktop; 0.13% on mobile). Matched observational studies of the factors associated with reference clicking reveal that clicks occur more frequently on shorter pages and on pages of lower quality, suggesting that references are consulted more commonly when Wikipedia itself does not contain the information sought by the user. Moreover, we observe that recent content, open access sources, and references about life events (births, deaths, marriages, etc.) are particularly popular. Taken together, our findings deepen our understanding of Wikipedia’s role in a global information economy where reliability is ever less certain, and source attribution ever more vital.
Model-independent Constraints on Lorentz Invariance Violation: Implication from Updated Gamma-Ray Burst Observations

Astrophysical observations provide a unique opportunity to test possible signatures of Lorentz invariance violation (LIV), due to the high energies and long distances involved. In quantum theory of gravity, one may expect the modification of the dispersion relation between energy and momentum for photons, which can be probed with the time lag (the arrival time delay between light curves in different energy bands) of gamma-ray bursts (GRBs). In this paper, by using the detailed time delay measurements of GRB 160625B at different energy bands, as well as 23 time delay GRBs covering the redshift range of z = 0.168–2.5 (which were measured at different energy channels from the light curves), we propose an improved model-independent method (based on the newly compiled sample of H(z) measurements) to probe the energy-dependent velocity due to the modified dispersion relation for photons. In the framework of a more complex and reasonable theoretical expression to describe the time delays, our results imply that the intrinsic time lags can be better described with more GRB time delay data. More importantly, through direct fitting of the time delay measurements of a sample of GRBs, our limit on the LIV energy scale is comparable to that with unknown constant for the intrinsic time lag, much lower than the Planck energy scale in both linear LIV and quadratic LIV cases.
Onset of Turbulent Fast Magnetic Reconnection Observed in the Solar Atmosphere

Fast magnetic reconnection powers explosive events throughout the universe, from gamma-ray bursts to solar flares. Despite its importance, the onset of astrophysical fast reconnection is the subject of intense debate and remains an open question in plasma physics. Here we report high-cadence observations of two reconnection-driven solar microflares obtained by the Interface Region Imaging Spectrograph that show persistent turbulent flows preceding flaring. The speeds of these flows are comparable to the local sound speed initially, suggesting the onset of fast reconnection in a highly turbulent plasma environment. Our results are in close quantitative agreement with the theory of turbulence-driven reconnection as well as with numerical simulations in which fast magnetic reconnection is induced by turbulence.
An approach to constrain models of accreting neutron stars with the use of an equation of state

We investigate X-ray bursts during the thermal evolution of an accreting neutron star that corresponds to the X-ray burster GS 1826$-$24. Physical quantities of the neutron star are included using an equation of state below and above the nuclear matter density. We adopt an equation of state and construct an approximate network that saves computational time and calculates nuclear energy generation rates accompanying the abundance evolutions. The mass and radius of the neutron star are found by solving the stellar evolution equations from the center to the surface; this involves necessary information such as the nuclear energy generation in accreting layers, heating from the crust, and neutrino emissions inside the stellar core. We reproduce the light curve and recurrence time of the X-ray burst from GS 1826$-$24 within the standard deviation of 1$\sigma$ for the assumed accretion rate, metallicity, and equation of state. It is concluded that the observed recurrence time is consistent with the theoretical model with metallicity of the initial CNO elements $Z_{\rm CNO} = 0.01$. We suggest that the nuclear reaction rates responsible for the $rp$-process should be examined in detail, because the rates may change the shape of the light curve and our conclusion.
Towards Robust DNNs: An Taylor Expansion-Based Method for Generating Powerful Adversarial Examples

: Although deep neural networks (DNNs) have achieved successful applications in many fields, they are vulnerable to adversarial examples. Adversarial training is one of the most effective methods to improve the robustness of DNNs, and it is generally considered as a minimax point problem that minimizes the loss function and maximizes the perturbation. Therefore, powerful adversarial examples can effectively simulate perturbation maximization to solve the minimax problem. In paper, a novel method was proposed to generate more powerful adversarial examples for robust adversarial training. The main idea is to approximates the output of DNNs in the input neighborhood by using the Taylor expansion, and then optimizes it by using the Lagrange multiplier method to generate adversarial examples. The experiment results show it can effectively improve the robust of DNNs trained with these powerful adversarial examples.
Operator and entanglement growth in nonthermalizing systems: Many-body localization and the random singlet phase

We characterize the growth and spreading of operators and entanglement in two paradigmatic non-thermalizing phases - the many-body localized phase and the random singlet phase - using out-of-time-ordered correlators, the entanglement contour, and operator entanglement. We contrast these phases with strongly thermalizing holographic conformal field theories and fully localized Anderson insulators. We obtain a phenomenological description of the operator and state dynamics of these phases and demonstrate the utility of the entanglement contour and operator entanglement measures as useful probes of slowly scrambling and non-thermalizing dynamics.
A recent starbust in the low surface brightness galaxy UGC 628

We present the star-formation history of the low surface brightness (LSB) galaxy UGC 628 as part of the MUSCEL program (MUltiwavelength observations of the Structure, Chemistry, and Evolution of LSB galaxies). The star-formation histories of LSB galaxies represent a significant gap in our knowledge of galaxy assembly, with implications for dark matter / baryon feedback, IGM gas accretion, and the physics of star formation in low metallicity environments. Our program uses ground-based IFU spectra in tandem with space-based UV and IR imaging to determine the star-formation histories of LSB galaxies in a spatially resolved fashion. In this work we present the fitted history of our first target to demonstrate our techniques and methodology. Our technique splits the history of this galaxy into 15 semi-logarithmically spaced timesteps. Within each timestep the star-formation rate of each spaxel is assumed constant. We then determine the set of 15 star-formation rates that best recreate the spectra and photometry measured in each spaxel. Our main findings with respect to UGC 628 are: a) the visible properties of UGC 628 have varied over time, appearing as a high surface brightness spiral earlier than 8 Gyr ago and a starburst galaxy during a recent episode of star formation several tens of Myr ago, b) the central bar/core region was established early, around 8-10 Gyr ago, but has been largely inactive since, and c) star formation in the past 3 Gyr is best characterised as patchy and sporadic.
Embedding codimension of the space of arcs

Abstract We introduce a notion of embedding codimension of an arbitrary local ring, establish some general properties and study in detail the case of arc spaces of schemes of finite type over a field. Viewing the embedding codimension as a measure of singularities, our main result can be interpreted as saying that the singularities of the arc space are maximal at the arcs that are fully embedded in the singular locus of the underlying scheme, and progressively improve as we move away from said locus. As an application, we complement a theorem of Drinfeld, Grinberg and Kazhdan on formal neighbourhoods in arc spaces by providing a converse to their theorem, an optimal bound for the embedding codimension of the formal model appearing in the statement, a precise formula for the embedding dimension of the model constructed in Drinfeld’s proof and a geometric meaningful way of realising the decomposition stated in the theorem.
Polarized emission by aligned grains in the Mie regime: Application to protoplanetary disks observed by ALMA

Context. The azimuthal polarization patterns observed in some protoplanetary disks by the Atacama Large Millimetre Array (ALMA) at millimeter wavelengths have raised doubts about whether they are truly produced by dust grains that are aligned with the magnetic field lines. These conclusions were based on the calculations of dust polarized emission in the Rayleigh regime, that is, for grain sizes that are much smaller than the wavelength. However, the grain size in such disks is typically estimated to be in the range of 0.1−1 mm from independent observations. Aims. We study the dust polarization properties of aligned grains in emission in the Mie regime, that is, when the mean grain size approaches the wavelength. Methods. By using the T-MATRIX and DustEM codes, we computed the spectral dependence of the polarization fraction in emission for grains in perfect spinning alignment for various grain size distributions. We restricted our study to weakly-elongated oblate and prolate grains of astrosilicate composition that have a mean size ranging from 10 μm to 1 mm. Results. In the submillimeter and millimeter wavelength range, the polarization by B-field aligned grains becomes negative for grains larger than ∼250 μm, meaning that the polarization vector becomes parallel to the B-field. The transition from the positive to the negative polarization occurs at a wavelength of λ ∼ 1 mm. The regime of negative polarization does not exist for grains that are smaller than ∼100 μm. Conclusions. When using realistic grain size distributions for disks with grains up to the submillimeter sizes, the polarization direction of thermal emission by aligned grains is shown to be parallel to the direction of the magnetic field over a significant fraction of the wavelengths typically used to observe young protoplanetary disks. This property may explain the peculiar azimuthal orientation of the polarization vectors in some of the disks observed with ALMA and attest to the conserved ability of dust polarized emission to trace the magnetic field in disks.
A growth adjusted price-earnings ratio

The purpose of this paper is to introduce a new growth adjusted price-earnings measure (GA-P/E) and assess its efficacy as measure of value and predictor of future stock returns. Taking inspiration from the interpretation of the traditional price-earnings ratio as a period of time, the new measure computes the requisite payback period whilst accounting for earnings growth. Having derived the measure, we outline a number of its properties before conducting an extensive empirical study utilising a sorted portfolio methodology. We find that the returns of the low GA-P/E stocks exceed those of the high GA-P/E stocks, both in an absolute sense and also on a risk-adjusted basis. Furthermore, the returns from the low GA-P/E porfolio was found to exceed those of the value portfolio arising from a P/E sort on the same pool of stocks. Finally, the returns of our GA-P/E sorted porfolios were subjected to analysis by conducting regressions against the standard Fama and French risk factors.
Normalization of Input-output Shared Embeddings in Text Generation Models

Neural Network based models have been state-of-the-art models for various Natural Language Processing tasks, however, the input and output dimension problem in the networks has still not been fully resolved, especially in text generation tasks (e.g. Machine Translation, Text Summarization), in which input and output both have huge sizes of vocabularies. Therefore, input-output embedding weight sharing has been introduced and adopted widely, which remains to be improved. Based on linear algebra and statistical theories, this paper locates the shortcoming of existed input-output embedding weight sharing method, then raises methods for improving input-output weight shared embedding, among which methods of normalization of embedding weight matrices show best performance. These methods are nearly computational cost-free, can get combined with other embedding techniques, and show good effectiveness when applied on state-of-the-art Neural Network models. For Transformer-big models, the normalization techniques can get at best 0.6 BLEU improvement compared to the original version of model on WMT'16 En-De dataset, and similar BLEU improvements on IWSLT 14' datasets. For DynamicConv models, 0.5 BLEU improvement can be attained on WMT'16 En-De dataset, and 0.41 BLEU improvement on IWSLT 14' De-En translation task is achieved.
Reproducing the CO-to-H2 conversion factor in cosmological simulations of Milky-Way-mass galaxies

We present models of CO(1–0) emission from Milky-Way-mass galaxies at redshift zero in the FIRE-2 cosmological zoom-in simulations. We calculate the molecular abundances by post-processing the simulations with an equilibrium chemistry solver while accounting for the effects of local sources, and determine the emergent CO(1–0) emission using a line radiative transfer code. We find that the results depend strongly on the shielding length assumed, which, in our models, sets the attenuation of the incident UV radiation field. At the resolution of these simulations, commonly used choices for the shielding length, such as the Jeans length, result in CO abundances that are too high at a given H2 abundance. We find that a model with a distribution of shielding lengths, which has a median shielding length of ∼3 pc in cold gas (T < 300 K) for both CO and H2, is able to reproduce both the observed CO(1–0) luminosity and inferred CO-to-H2 conversion factor at a given star formation rate compared with observations. We suggest that this short shielding length can be thought of as a subgrid model, which controls the amount of radiation that penetrates giant molecular clouds.
Tensor-Based Grading: A Novel Patch-Based Grading Approach for the Analysis Of Deformation Fields in Huntington's Disease

The improvements in magnetic resonance imaging have led to the development of numerous techniques to better detect structural alterations caused by neurodegenerative diseases. Among these, the patch-based grading framework has been proposed to model local patterns of anatomical changes. This approach is attractive because of its low computational cost and its competitive performance. Other studies have proposed to analyze the deformations of brain structures using tensor-based morphometry, which is a highly interpretable approach. In this work, we propose to combine the advantages of these two approaches by extending the patch-based grading framework with a new tensor-based grading method that enables us to model patterns of local deformation using a log-Euclidean metric. We evaluate our new method in a study of the putamen for the classification of patients with pre-manifest Huntington's disease and healthy controls. Our experiments show a substantial increase in classification accuracy (87.5 ± 0.5 vs. 81.3 ± 0.6) compared to the existing patch-based grading methods, and a good complement to putamen volume, which is a primary imaging-based marker for the study of Hunting-ton's disease.
Interpretable End-to-End Urban Autonomous Driving With Latent Deep Reinforcement Learning

Unlike popular modularized framework, end-to-end autonomous driving seeks to solve the perception, decision and control problems in an integrated way, which can be more adapting to new scenarios and easier to generalize at scale. However, existing end-to-end approaches are often lack of interpretability, and can only deal with simple driving tasks like lane keeping. In this article, we propose an interpretable deep reinforcement learning method for end-to-end autonomous driving, which is able to handle complex urban scenarios. A sequential latent environment model is introduced and learned jointly with the reinforcement learning process. With this latent model, a semantic birdeye mask can be generated, which is enforced to connect with certain intermediate properties in today’s modularized framework for the purpose of explaining the behaviors of learned policy. The latent space also significantly reduces the sample complexity of reinforcement learning. Comparison tests in a realistic driving simulator show that the performance of our method in urban scenarios with crowded surrounding vehicles dominates many baselines including DQN, DDPG, TD3 and SAC. Moreover, through masked outputs, the learned model is able to provide a better explanation of how the car reasons about the driving environment.