Miracle-dz/UniFAR-FTUs huggingface dataset:
Disc versus wind accretion in X-ray pulsar GX 301-2

GX 301-2 provides a rare opportunity to study both disk and wind accretion in a same target. We report Insight-HXMT observations of the spin-up event of GX 301-2 happened in 2019 and compare with those of wind-fed state. The pulse profiles of the initial rapid spin-up period are dominated by one main peak, while those of the later slow spin-up period are composed of two similar peaks, as those of wind-fed state. These behaviors are confirmed by Fermi/GBM data, which also show that during the rapid spin-up period, the main peak increases with luminosity up to 8× 10 erg s, but the faint peak keeps almost constant. The absorption column densities during the spin-up period are ∼ 1.5 × 10 cm, much less than those of wind-fed state at similar luminosity (∼ 9 × 10 cm), supporting the scenario that most of material is condensed into a disk during the spin-up period. We discuss possible differences between disk and wind accretion that may explain the observed different trend of pulse profiles.
Searching for pulsars in the Galactic centre at 3 and 2 mm

Pulsars in the Galactic centre promise to enable unparalleled tests of gravity theories and black hole physics and to serve as probes of the stellar formation history and evolution and the interstellar medium in the complex central region of the Milky Way. The community has surveyed the innermost region of the galaxy for decades without detecting a population of pulsars, which is puzzling. A strong scattering of the pulsed signals in this particular direction has been argued to be a potential reason for the non-detections. Scattering has a strong inverse dependence on observing frequency, therefore an effective way to alleviate its effect is to use higher frequencies in a survey for pulsars in the Galactic centre, in particular, close to the supermassive black hole Sagittarius A*. We present the first pulsar survey at short millimetre wavelengths, using several frequency bands between 84 and 156 GHz (λ = 3.5–1.92 mm), targeted to the Galactic centre. The observations were made with the Institut de Radioastronomie Millimétrique 30m Telescope in 28 epochs between 2016 December and 2018 May. This survey is the first that is essentially unaffected by scattering and therefore unbiased in population coverage, including fast-spinning pulsars that might be out of reach of lower-frequency Galactic centre surveys. We discovered no new pulsars and relate this result mainly to the decreased flux density of pulsars at high frequencies, combined with our current sensitivity. However, we demonstrate that surveys at these extremely high radio frequencies are capable of discovering new pulsars, analyse their sensitivity limits with respect to a simulated Galactic centre pulsar population, and discuss the main challenges and possible improvements for similar surveys in the future.
Gravitational lensing in LoTSS DR2: extremely faint 144-MHz radio emission from two highly magnified quasars

We report extremely faint 144 MHz radio emission from two gravitationally lensed quasars, SDSS J1004+4112 (z = 1.730) and SDSS J2222+2745 (z = 2.803), using the LOFAR TwoMetre Sky Survey (LoTSS) data release 2. After correcting for the lensing magnifications, the two objects have intrinsic flux-densities of 13 ± 2 and 58 ± 6 μJy, respectively, corresponding to 144 MHz rest-frame luminosities of 1023.2±0.2 and 1024.42±0.05 W Hz−1, respectively. In the case of SDSS J1004+4112, the intrinsic flux density is close to the confusion limit of LoTSS, making this radio source the faintest to be detected thus far at low frequencies, and the lowest luminosity known at z & 0.65. Under the assumption that all of the radio emission is due to star-formation processes, the quasar host galaxies are predicted to have star-formation rates of 5.5+1.8 −1.4 and 73 +34 −22 M yr −1, respectively. Further multi-wavelength observations at higher angular resolution will be needed to determine if any of the detected radio emission is due to weak jets associated with the quasars.
Facial expression and attributes recognition based on multi-task learning of lightweight neural networks

In this paper, the multi-task learning of lightweight convolutional neural networks is studied for face identification and classification of facial attributes (age, gender, ethnicity) trained on cropped faces without margins. The necessity to fine-tune these networks to predict facial expressions is highlighted. Several models are presented based on lightweight architectures, such as MobileNet, EfficientNet and RexNet. It was experimentally demonstrated that they lead to near state-of-the-art results in age, gender and race recognition on the UTKFace dataset and emotion classification on the AffectNet dataset. Moreover, it is shown that the usage of the trained models as feature extractors of facial regions in video frames leads to 4.5% higher accuracy than the previously known state-of-the-art single models for the AFEW and the VGAF datasets from the EmotiW challenges.
Protected states in AdS3 backgrounds from integrability

We write down the algebraic Bethe ansatz for string theory on AdS3 × S3 × T4 and AdS3 × S3 × K3 in its orbifold limits. We use it to determine the wave-functions of protected closed strings in these backgrounds and prove that their energies are protected to all orders in α′. We further apply the ABA to find the wave functions of protected states of AdS3 × S3 × S3 × S1 and its Z 2 orbifold. Our findings match with protected spectrum calculations from supergravity, Sym N orbifolds and apply to the complete moduli space of these theories, excluding orbifold blow-up modes for which further analysis is necessary.
The GIST and RIST of Iterative Self-Training for Semi-Supervised Segmentation

We consider the task of semi-supervised semantic segmentation, where we aim to produce pixel-wise semantic object masks given only a small number of human-labeled training examples. We focus on iterative self-training methods in which we explore the behavior of self-training over multiple refinement stages. We show that iterative self-training leads to performance degradation if done naïvely with a fixed ratio of human-labeled to pseudo-labeled training examples. We propose Greedy Iterative Self-Training (GIST) and Random Iterative Self-Training (RIST) strategies that alternate between training on either human-labeled data or pseudo-labeled data at each refinement stage, resulting in a performance boost rather than degradation. We further show that GIST and RIST can be combined with existing semi-supervised learning methods to boost performance.
Unboring ideals

Our main object of interest is the following notion: we say that a topological space space $X$ is in FinBW($\mathcal{I}$), where $\mathcal{I}$ is an ideal on $\omega$, if for each sequence $(x_n)_{n\in\omega}$ in $X$ one can find an $A\notin\mathcal{I}$ such that $(x_n)_{n\in A}$ converges in $X$. We define an ideal $\mathcal{BI}$ which is critical for FinBW($\mathcal{I}$) in the following sense: Under CH, for every ideal $\mathcal{I}$, $\mathcal{BI}\not\leq_K\mathcal{I}$ ($\leq_K$ denotes the Kat\v{e}tov preorder of ideals) iff there is an uncountable separable space in FinBW($\mathcal{I}$). We show that $\mathcal{BI}\not\leq_K\mathcal{I}$ and $\omega_1$ with the order topology is in FinBW($\mathcal{I}$), for all $\bf{\Pi^0_4}$ ideals $\mathcal{I}$. We examine when FinBW($\mathcal{I}$)$\setminus$FinBW($\mathcal{J}$) is nonempty: we prove under MA($\sigma$-centered) that for $\bf{\Pi^0_4}$ ideals $\mathcal{I}$ and $\mathcal{J}$ this is equivalent to $\mathcal{J}\not\leq_K\mathcal{I}$. Moreover, answering in negative a question of M. Hru\v{s}\'ak and D. Meza-Alc\'antara, we show that the ideal $\text{Fin}\times\text{Fin}$ is not critical among Borel ideals for extendability to a $\bf{\Pi^0_3}$ ideal. Finally, we apply our results in studies of Hindman spaces and in the context of analytic P-ideals.
Comparison of Wide-Band Vibrotactile and Friction Modulation Surface Gratings

This article seeks to understand conditions under which virtual gratings produced via vibrotaction and friction modulation are perceived as similar and to find physical origins in the results. To accomplish this, we developed two single-axis devices, one based on electroadhesion and one based on out-of-plane vibration. The two devices had identical touch surfaces, and the vibrotactile device used a novel closed-loop controller to achieve precise control of out-of-plane plate displacement under varying load conditions across a wide ranget of frequencies. A first study measured the perceptual intensity equivalence curve of gratings generated under electroadhesion and vibrotaction across the 20-400 Hz frequency range. A second study assessed the perceptual similarity between two forms of skin excitation given the same driving frequency and same perceived intensity. Our results indicate that it is largely the out-of-plane velocity that predicts vibrotactile intensity relative to shear forces generated by friction modulation. A high degree of perceptual similarity between gratings generated through friction modulation and through vibrotaction is apparent and tends to scale with actuation frequency suggesting perceptual indifference to the manner of fingerpad actuation in the upper frequency range.
Double forms: Part I. Regular elliptic bilaplacian operators

Double forms are sections of the vector bundles ΛT ∗M⊗ΛmT ∗M, where in this work (M, g) is a compact Riemannian manifold with boundary. We study graded second-order differential operators on double forms, which are used in physical applications. A Combination of these operators yields a fourth-order operator, which we call a double bilaplacian. We establish the regular ellipticity of the double bilaplacian for several sets of boundary conditions. Under additional conditions, we obtain a Hodge-like decomposition for double forms, whose components are images of the second-order operators, along with a biharmonic element. This analysis lays foundations for resolving several topics in incompatible elasticity, most prominently the existence of stress potentials and Saint-Venant compatibility.
Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning

Recently many algorithms were devised for reinforcement learning (RL) with function approximation. While they have clear algorithmic distinctions, they also have many implementation differences that are algorithm-independent and sometimes under-emphasized. Such mixing of algorithmic novelty and implementation craftsmanship makes rigorous analyses of the sources of performance improvements across algorithms difficult. In this work, we focus on a series of off-policy inference-based actor-critic algorithms -- MPO, AWR, and SAC -- to decouple their algorithmic innovations and implementation decisions. We present unified derivations through a single control-as-inference objective, where we can categorize each algorithm as based on either Expectation-Maximization (EM) or direct Kullback-Leibler (KL) divergence minimization and treat the rest of specifications as implementation details. We performed extensive ablation studies, and identified substantial performance drops whenever implementation details are mismatched for algorithmic choices. These results show which implementation details are co-adapted and co-evolved with algorithms, and which are transferable across algorithms: as examples, we identified that tanh Gaussian policy and network sizes are highly adapted to algorithmic types, while layer normalization and ELU are critical for MPO's performances but also transfer to noticeable gains in SAC. We hope our work can inspire future work to further demystify sources of performance improvements across multiple algorithms and allow researchers to build on one another's both algorithmic and implementational innovations.
A Closer Look at Fourier Spectrum Discrepancies for CNN-generated Images Detection

CNN-based generative modelling has evolved to produce synthetic images indistinguishable from real images in the RGB pixel space. Recent works have observed that CNN-generated images share a systematic shortcoming in replicating high frequency Fourier spectrum decay attributes. Furthermore, these works have successfully exploited this systematic shortcoming to detect CNN-generated images reporting up to 99% accuracy across multiple state-of-the-art GAN models.In this work, we investigate the validity of assertions claiming that CNN-generated images are unable to achieve high frequency spectral decay consistency. We meticulously construct a counterexample space of high frequency spectral decay consistent CNN-generated images emerging from our handcrafted experiments using DCGAN, LSGAN, WGAN-GP and StarGAN, where we empirically show that this frequency discrepancy can be avoided by a minor architecture change in the last upsampling operation. We subsequently use images from this counterexample space to successfully bypass the recently proposed forensics detector which leverages on high frequency Fourier spectrum decay attributes for CNN-generated image detection.Through this study, we show that high frequency Fourier spectrum decay discrepancies are not inherent characteristics for existing CNN-based generative models—contrary to the belief of some existing work—, and such features are not robust to perform synthetic image detection. Our results prompt re-thinking of using high frequency Fourier spectrum decay attributes for CNN-generated image detection. Code and models are available at https://keshik6.github. io/Fourier-Discrepancies-CNN-Detection/
QBugs: A Collection of Reproducible Bugs in Quantum Algorithms and a Supporting Infrastructure to Enable Controlled Quantum Software Testing and Debugging Experiments

Reproducibility and comparability of empirical results are at the core tenet of the scientific method in any scientific field. To ease reproducibility of empirical studies, several benchmarks in software engineering research, such as Defects4J, have been developed and widely used. For quantum software engineering research, however, no benchmark has been established yet. In this position paper, we propose a new benchmark—named QBugs—which will provide experimental subjects and an experimental infrastructure to ease the evaluation of new research and the reproducibility of previously published results on quantum software engineering.
A probabilistic deep learning approach to automate the interpretation of multi-phase diffraction spectra

Autonomous synthesis and characterization of inorganic materials requires the automatic and accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep learning algorithm to identify complex multi-phase mixtures. At the core of this algorithm lies an ensemble convolutional neural network trained on simulated diffraction spectra, which are systematically augmented with physics-informed perturbations to account for artifacts that can arise during experimental sample preparation and synthesis. Larger perturbations associated with off-stoichiometry are also captured by supplementing the training set with hypothetical solid solutions. Spectra containing mixtures of materials are analyzed with a newly developed branching algorithm that utilizes the probabilistic nature of the neural network to explore suspected mixtures and identify the set of phases that maximize confidence in the prediction. Our model is benchmarked on simulated and experimentally measured diffraction spectra, showing exceptional performance with accuracies exceeding those given by previously reported methods based on profile matching and deep learning. We envision that the algorithm presented here may be integrated in experimental workflows to facilitate the high-throughput and autonomous discovery of inorganic materials.
Unpaired Single-Image Depth Synthesis with cycle-consistent Wasserstein GANs

Real-time estimation of actual environment depth is an essential module for various autonomous system tasks such as localization, obstacle detection and pose estimation. During the last decade of machine learning, extensive deployment of deep learning methods to computer vision tasks yielded successful approaches for realistic depth synthesis out of a simple RGB modality. While most of these models rest on paired depth data or availability of video sequences and stereo images, there is a lack of methods facing single-image depth synthesis in an unsupervised manner. Therefore, in this study, latest advancements in the field of generative neural networks are leveraged to fully unsupervised single-image depth synthesis. To be more exact, two cycle-consistent generators for RGB-to-depth and depth-to-RGB transfer are implemented and simultaneously optimized using the Wasserstein-1 distance. To ensure plausibility of the proposed method, we apply the models to a self acquised industrial data set as well as to the renown NYU Depth v2 data set, which allows comparison with existing approaches. The observed success in this study suggests high potential for unpaired single-image depth estimation in real world applications.
BASE Layers: Simplifying Training of Large, Sparse Models

We introduce a new balanced assignment of experts (BASE) layer for large language models that greatly simplifies existing high capacity sparse layers. Sparse layers can dramatically improve the efficiency of training and inference by routing each token to specialized expert modules that contain only a small fraction of the model parameters. However, it can be difficult to learn balanced routing functions that make full use of the available experts; existing approaches typically use routing heuristics or auxiliary expert-balancing loss functions. In contrast, we formulate token-to-expert allocation as a linear assignment problem, allowing an optimal assignment in which each expert receives an equal number of tokens. This optimal assignment scheme improves efficiency by guaranteeing balanced compute loads, and also simplifies training by not requiring any new hyperparameters or auxiliary losses. Code is publicly released at https://github.com/pytorch/fairseq/
Stellar Metallicities from SkyMapper Photometry. II. Precise Photometric Metallicities of ∼280,000 Giant Stars with [Fe/H] < −0.75 in the Milky Way

The Milky Way’s metal-poor stars are nearby ancient objects that are used to study early chemical evolution and the assembly and structure of the Milky Way. Here we present reliable metallicities of ∼280,000 stars with −3.75 ≲ [Fe/H] ≲ −0.75 down to g = 17 derived using metallicity-sensitive photometry from the second data release of the SkyMapper Southern Survey. We use the dependency of the flux through the SkyMapper v filter on the strength of the Ca ii K absorption features, in tandem with SkyMapper u, g, i photometry, to derive photometric metallicities for these stars. We find that metallicities derived in this way compare well to metallicities derived in large-scale spectroscopic surveys, and we use such comparisons to calibrate and quantify systematics as a function of location, reddening, and color. We find good agreement with metallicities from the APOGEE, LAMOST, and GALAH surveys, based on a standard deviation of σ ∼ 0.25 dex of the residuals of our photometric metallicities with respect to metallicities from those surveys. We also compare our derived photometric metallicities to metallicities presented in a number of high-resolution spectroscopic studies to validate the low-metallicity end ([Fe/H] < −2.5) of our photometric metallicity determinations. In such comparisons, we find the metallicities of stars with photometric [Fe/H] < −2.5 in our catalog show no significant offset and a scatter of σ ∼ 0.31 dex level relative to those in high-resolution work when considering the cooler stars (g − i > 0.65) in our sample. We also present an expanded catalog containing photometric metallicities of ∼720,000 stars as a data table for further exploration of the metal-poor Milky Way.
Bit-Mixer: Mixed-precision networks with runtime bit-width selection

Mixed-precision networks allow for a variable bit-width quantization for every layer in the network. A major limitation of existing work is that the bit-width for each layer must be predefined during training time. This allows little flexibility if the characteristics of the device on which the network is deployed change during runtime. In this work, we propose Bit-Mixer, the very first method to train a meta-quantized network where during test time any layer can change its bit-width without affecting at all the overall network’s ability for highly accurate inference. To this end, we make 2 key contributions: (a) Transitional Batch-Norms, and (b) a 3-stage optimization process which is shown capable of training such a network. We show that our method can result in mixed precision networks that exhibit the desirable flexibility properties for on-device deployment without compromising accuracy. Code will be made available.
R A ] 3 0 M ar 2 02 1 CHARACTERIZING TOTAL NEGATIVITY AND TESTING THEIR INTERVAL HULLS PROJESH

A matrix is called totally negative (totally non-positive) of order k, if all its minors of size at most k are negative (non-positive). The objective of this article is to provide several novel characterizations of total negativity via the (a) sign non-reversal property, (b) variation diminishing property, and (c) Linear Complementarity Problem. More strongly, each of these three characterizations uses a single test vector. As an application of the sign non-reversal property, we study the interval hull of two rectangular matrices. In particular, we identify two matrices C(A,B) in the interval hull of matrices A and B that test total negativity of order k, simultaneously for the entire interval hull. We also show analogous characterizations for totally non-positive matrices. These novel characterizations may be considered similar in spirit to fundamental results characterizing totally positive matrices by Brown–Johnstone–MacGibbon [J. Amer. Statist. Assoc. 1981] (see also Gantmacher–Krein, 1950), Choudhury–Kannan–Khare [Bull. London Math. Soc. , in press] and Choudhury [2021 preprint]. Finally using a 1950 result of Gantmacher–Krein, we show that totally negative/non-positive matrices can not be detected by (single) test vectors from orthants other than the open bi-orthant that have coordinates with alternating signs, via the sign non-reversal property or the variation diminishing property.