Miracle-dz/UniFAR-FTUs huggingface dataset:
Quasars as high-redshift standard candles

The non-linear relation between the X-ray and ultraviolet (UV) luminosity in quasars has been used to derive quasar distances and to build a Hubble diagram at redshifts up to $z\sim$ 7. This cosmological application is based on the assumption of independence of the relation on redshift and luminosity. We want to test the reliability of this hypothesis by studying the spectroscopic properties of high-redshift quasars in the X-ray and UV bands. We performed a one-by-one analysis of a sample of 130 quasars at $z>$ 2.5 with high-quality X-ray and UV spectroscopic observations. We found that not only the X-ray to UV correlation still holds at these redshifts, but its intrinsic dispersion is as low as 0.12 dex (previous works reached 0.20$-$0.22 dex). For a sample of quasars at $z\sim$ 3 with particularly high-quality observations the dispersion further drops to 0.09 dex, a value entirely accountable for by intrinsic variability and source geometry effects. The composite spectra of these quasars, in both the X-rays and the UV, do not show any difference with respect to the average spectra of quasars at lower redshifts. The absence of any spectral difference between high- and low-$z$ quasars and the tightness of the X-ray to UV relation suggests that no evolutionary effects are present in the relation. Therefore, it can be safely employed to derive quasar distances. Under this assumption, we obtain a measurement of the luminosity distance at $z\sim$ 3 with 15 % uncertainty, and in a 4$\sigma$ tension with the concordance model.
Learning Gait Representation From Massive Unlabelled Walking Videos: A Benchmark

Gait depicts individuals’ unique and distinguishing walking patterns and has become one of the most promising biometric features for human identification. As a fine-grained recognition task, gait recognition is easily affected by many factors and usually requires a large amount of completely annotated data that is costly and insatiable. This paper proposes a large-scale self-supervised benchmark for gait recognition with contrastive learning, aiming to learn the general gait representation from massive unlabelled walking videos for practical applications via offering informative walking priors and diverse real-world variations. Specifically, we collect a large-scale unlabelled gait dataset GaitLU-1M consisting of 1.02M walking sequences and propose a conceptually simple yet empirically powerful baseline model GaitSSB. Experimentally, we evaluate the pre-trained model on four widely-used gait benchmarks, CASIA-B, OU-MVLP, GREW and Gait3D with or without transfer learning. The unsupervised results are comparable to or even better than the early model-based and GEI-based methods. After transfer learning, GaitSSB outperforms existing methods by a large margin in most cases, and also showcases the superior generalization capacity. Further experiments indicate that the pre-training can save about 50% and 80% annotation costs of GREW and Gait3D. Theoretically, we discuss the critical issues for gait-specific contrastive framework and present some insights for further study. As far as we know, GaitLU-1M is the first large-scale unlabelled gait dataset, and GaitSSB is the first method that achieves remarkable unsupervised results on the aforementioned benchmarks.
Accurate and Real-time Pseudo Lidar Detection: Is Stereo Neural Network Really Necessary?

The proposal of Pseudo-Lidar representation has significantly narrowed the gap between visual-based and active Lidar-based 3D object detection. However, current researches exclusively focus on pushing the accuracy improvement of Pseudo-Lidar by taking the advantage of complex and time-consuming neural networks. Seldom explore the profound characteristics of Pseudo-Lidar representation to obtain the promoting opportunities. In this paper, we dive deep into the pseudo Lidar representation and argue that the performance of 3D object detection is not fully dependent on the high precision stereo depth estimation. We demonstrate that even for the unreliable depth estimation, with proper data processing and refining, it can achieve comparable 3D object detection accuracy. With this finding, we further show the possibility that utilizing fast but inaccurate stereo matching algorithms in the Pseudo-Lidar system to achieve low latency responsiveness. In the experiments, we develop a system with a less powerful stereo matching predictor and adopt the proposed refinement schemes to improve the accuracy. The evaluation on the KITTI benchmark shows that the presented system achieves competitive accuracy to the state-of-the-art approaches with only 23 ms computing, showing it is a suitable candidate for deploying to real car-hold applications.
Kinetics and Parameters of Epitaxial Monolayered Continuous large area Molybdenum disulfide Growth

The growth of large crystallite continuous monolayer materials like molybdenum disulfide (MoS 2 ) with desired morphology via chemical vapor deposition (CVD) remains a challenge. In CVD, the complex interplay of various factors like growth temperature, precursors, and nature of the substrate decides the crystallinity, crystallite size, and coverage area of the grown MoS 2 monolayer. In the present work, we report about the role of weight fraction of molybdenum trioxide (MoO 3 ), Sulfur, and carrier gas flow rate towards nucleation and monolayer growth. The concentration of MoO 3 weight fraction has been found to govern the self-seeding process and decides the density of nucleation sites affecting morphology and coverage area. Carrier gas flow of 100 sccm argon results into large crystallite continuous films with lower coverage area (70 %), while the flow rate of 150 sccm results into 92 % coverage area with reduced crystallite size. Through systematic variation of experimental parameters, we have established the recipe for the growth of large crystallite atomically thin MoS 2 suitable for optoelectronic devices.
Domain Agnostic Few-shot Learning for Speaker Verification

Deep learning models for veriﬁcation systems often fail to generalize to new users and new environments, even though they learn highly discriminative features. To address this problem, we propose a few-shot domain generalization framework that learns to tackle distribution shift for new users and new domains. Our framework consists of domain-speciﬁc and domain-aggregation networks, which are the experts on speciﬁc and combined domains, respectively. By using these networks, we generate episodes that mimic the presence of both novel users and novel domains in the training phase to eventually produce better generalization. To save memory, we reduce the number of domain-speciﬁc networks by clustering similar domains together. Upon extensive evaluation on artiﬁcially generated noise domains, we can explicitly show generalization ability of our framework. In addition, we apply our proposed methods to the existing competitive architecture on the standard benchmark, which shows further performance improvements.
Radiative Magnetohydrodynamic Simulation of the Confined Eruption of a Magnetic Flux Rope: Magnetic Structure and Plasma Thermodynamics

It is widely believed that magnetic flux ropes are the key structure of solar eruptions; however, their observable counterparts are not clear yet. We study a flare associated with flux rope eruption in a comprehensive radiative magnetohydrodynamic simulation of flare-productive active regions, especially focusing on the thermodynamic properties of the plasma involved in the eruption and their relation to the magnetic flux rope. The preexisting flux rope, which carries cold and dense plasma, rises quasi-statically before the onset of eruptions. During this stage, the flux rope does not show obvious signatures in extreme ultraviolet (EUV) emission. After the flare onset, a thin “current shell” is generated around the erupting flux rope. Moreover, a current sheet is formed under the flux rope, where two groups of magnetic arcades reconnect and create a group of postflare loops. The plasma within the “current shell,” current sheet, and postflare loops are heated to more than 10 MK. The postflare loops give rise to abundant soft X-ray emission. Meanwhile, a majority of the plasma hosted in the flux rope is heated to around 1 MK, and the main body of the flux rope is manifested as a bright arch in cooler EUV passbands such as the AIA 171 Å channel.
TTS-CGAN: A Transformer Time-Series Conditional GAN for Biosignal Data Augmentation

—Signal measurement appearing in the form of time series is one of the most common types of data used in medical machine learning applications. Such datasets are often small in size, expensive to collect and annotate, and might involve privacy issues, which hinders our ability to train large, state- of-the-art deep learning models for biomedical applications. For time-series data, the suite of data augmentation strategies we can use to expand the size of the dataset is limited by the need to maintain the basic properties of the signal. Generative Adversarial Networks (GANs) can be utilized as another data augmentation tool. In this paper, we present TTS-CGAN, a transformer-based conditional GAN model that can be trained on existing multi-class datasets and generate class-speciﬁc synthetic time-series sequences of arbitrary length. We elaborate on the model architecture and design strategies. Synthetic sequences generated by our model are indistinguishable from real ones, and can be used to complement or replace real signals of the same type, thus achieving the goal of data augmentation. To evaluate the quality of the generated data, we modify the wavelet coherence metric to be able to compare the similarity between two sets of signals, and also conduct a case study where a mix of synthetic and real data are used to train a deep learning model for sequence classiﬁcation. Together with other visualization techniques and qualitative evaluation approaches, we demonstrate that TTS-CGAN generated synthetic data are similar to real data, and that our model performs better than the other state-of-the-art GAN models built for time-series data GAN model and veriﬁed its performance on multiple image synthesis tasks.
Comparison of metadata with relevance for bibliometrics between Microsoft Academic Graph and OpenAlex until 2020

With the announcement of the retirement of Microsoft Academic Graph (MAG), the non-profit organization OurResearch announced that they would provide a similar resource under the name OpenAlex. Thus, we compare the metadata with relevance to bibliometric analyses of the latest MAG snapshot with an early OpenAlex snapshot. Practically all works from MAG were transferred to OpenAlex preserving their bibliographic data publication year, volume, first and last page, DOI as well as the number of references that are important ingredients of citation analysis. More than 90% of the MAG documents have equivalent document types in OpenAlex. Of the remaining ones, especially reclassifications to the OpenAlex document types journal-article and book-chapter seem to be correct and amount to more than 7%, so that the document type specifications have improved significantly from MAG to OpenAlex. As another item of bibliometric relevant metadata, we looked at the paper-based subject classification in MAG and in OpenAlex. We found significantly more documents with a subject classification assignment in OpenAlex than in MAG. On the first and second level, the classification structure is nearly identical. We present data on the subject reclassifications on both levels in tabular and graphical form. The assessment of the consequences of the abundant subject reclassifications on field-normalized bibliometric evaluations is not in the scope of the present paper. Apart from this open question, OpenAlex seems to be overall at least as suited for bibliometric analyses as MAG for publication years before 2021 or maybe even better because of the broader coverage of document type assignments.
Semi-analytic forecasts for JWST – VI. Simulated light-cones and galaxy clustering predictions

In anticipation of the new era of high-redshift exploration marked by the commissioning of the James Webb Space Telescope (JWST), we present two sets of galaxy catalogues that are designed to aid the planning and interpretation of observing programs. We provide a set of 40 wide-field light-cones with footprints spanning approximately ∼1000 arcmin2 containing galaxies up to z = 10, and a new set of eight ultradeep light-cones with 132 arcmin2 footprints, containing galaxies up to z ∼ 12 down to the magnitudes expected to be reached in the deepest JWST surveys. These mock light-cones are extracted from dissipationless N-body simulations and populated with galaxies using the well-established, computationally efficient Santa Cruz semi-analytic model for galaxy formation. We provide a wide range of predicted physical properties, and simulated photometry from Near-Infrared Camera (NIRCam) and many other instruments. We explore the predicted counts and luminosity functions and angular two-point correlation functions for galaxies in these simulated light-cones. We also explore the predicted field-to-field variance using multiple light-cone realizations. We find that these light-cones reproduce the available measurements of observed clustering from 0.2 ≲ z ≲ 7.5 very well. We provide predictions for galaxy clustering at high redshift that may be obtained from future JWST observations. All of the light-cones presented here are made available through a web-based interactive data release portal.
On the amplification of security and privacy risks by post-hoc explanations in machine learning models

A variety of explanation methods have been proposed in recent years to help users gain insights into the results returned by neural networks, which are otherwise complex and opaque black-boxes. However, explanations give rise to potential side-channels that can be leveraged by an adversary for mounting attacks on the system. In particular, post-hoc explanation methods that highlight input dimensions according to their importance or relevance to the result also leak information that weakens security and privacy. In this work, we perform the first systematic characterization of the privacy and security risks arising from various popular explanation techniques. First, we propose novel explanation-guided black-box evasion attacks that lead to 10 times reduction in query count for the same success rate. We show that the adversarial advantage from explanations can be quantified as a reduction in the total variance of the estimated gradient. Second, we revisit the membership information leaked by common explanations. Contrary to observations in prior studies, via our modified attacks we show significant leakage of membership information (above 100% improvement over prior results), even in a much stricter black-box setting. Finally, we study explanation-guided model extraction attacks and demonstrate adversarial gains through a large reduction in query count.
Joint Generator-Ranker Learning for Natural Language Generation

Generate-then-rank is a widely used mechanism for text generation, where a generator produces multiple text candidates and a ranker chooses the best one among the text candidates. However, existing methods usually train the generator and the ranker individually, neglecting the mutual feedback that could further enhance the generation quality. To tackle this limitation, we propose JGR, a novel joint training algorithm that integrates the generator and the ranker in a single framework. JGR optimizes the generator with a hybrid objective that combines data likelihood and ranker reward, and trains the ranker with a contrastive loss that compares the generator outputs. By iteratively updating the generator and the ranker, JGR can effectively harmonize their learning and enhance their quality jointly. We evaluate JGR on various text generation tasks and demonstrate that it surpasses existing methods on four public datasets across three common generation scenarios. Our code and models are publicly available at https://github.com/microsoft/ProphetNet/tree/master/JGR.
Untangling the Galaxy. IV. Empirical Constraints on Angular Momentum Evolution and Gyrochronology for Young Stars in the Field

We present a catalog of ∼100,000 periodic variable stars in Transiting Exoplanet Survey Satellite (TESS) full-frame image data among members of widely distributed moving groups identified with Gaia in the previous papers in the series. By combining the periods from our catalog attributable to rotation with previously derived rotation periods for benchmark open clusters, we develop an empirical gyrochronology relation of angular momentum evolution that is valid for stars with ages 10–1000 Myr. Excluding stars rotating faster than 2 days, which we find are predominantly binaries, we achieve a typical age precision of ≈0.2–0.3 dex and improving at older ages. Importantly, these empirical relations apply to not only FGK-type stars but also M-type stars, due to the angular momentum distribution being much smoother, simpler, continuous, and monotonic as compared to the rotation period distribution. As a result, we are also able to begin tracing in fine detail the nature of angular momentum loss in low-mass stars as functions of mass and age. We characterize the stellar variability amplitudes of the cool stars as functions of mass and age, which may correlate with the starspot covering fractions. We also identify pulsating variables among the hotter stars in the catalog, including δ Scuti, γ Dor, and slowly pulsating B-type variables. These data represent an important step forward in being able to estimate precise ages of FGK- and M-type stars in the field, starting as early as the pre-main-sequence phase of evolution.
Adapted variable density subsampling for compressed sensing

Recent results in compressed sensing showed that the optimal subsampling strategy should take into account the sparsity pattern of the signal at hand. This oracle-like knowledge, even though desirable, nevertheless remains elusive in most practical application. We try to close this gap by showing how the sparsity patterns can instead be characterised via a probability distribution on the supports of the sparse signals allowing us to again derive optimal subsampling strategies. This probability distribution can be easily estimated from signals of the same signal class, achieving state of the art performance in numerical experiments. Our approach also extends to structured acquisition, where instead of isolated measurements, blocks of measurements are taken.
Radio Emission from UV Cet: Auroral Emission from a Stellar Magnetosphere

The archetypical flare star UV Cet was observed by MeerKAT on 2021 October 5–6. A large radio outburst with a duration of ∼2 hr was observed between 886 and 1682 MHz, with a time resolution of 8 s and a frequency resolution of 0.84 MHz, enabling sensitive dynamic spectra to be formed. The emission is characterized by three peaks containing a multitude of broadband arcs or partial arcs in the time-frequency domain. In general, the arcs are highly right-hand circularly polarized. At the end of the third peak, brief bursts occur that are significantly elliptically polarized. We present a simple model that appears to be broadly consistent with the characteristics of the radio emission from UV Cet. Briefly, the stellar magnetic field is modeled as a dipole aligned with the rotational axis of the star. The radio emission mechanism is assumed to be due to the cyclotron maser instability, where x-mode radiation near the electron gyrofrequency is amplified. While the elliptically polarized bursts may be intrinsic to the source, rather stringent limits are imposed on the plasma density in the source and along the propagation path. We suggest that the elliptically polarized radiation may instead be the result of reflection on an overdense plasma structure at some distance from the source. The radio emission from UV Cet shares both stellar and planetary attributes.
BAGEL: A Benchmark for Assessing Graph Neural Network Explanations

The problem of interpreting the decisions of machine learning is a well-researched and important. We are interested in a specific type of machine learning model that deals with graph data called graph neural networks. Evaluating interpretability approaches for graph neural networks (GNN) specifically are known to be challenging due to the lack of a commonly accepted benchmark. Given a GNN model, several interpretability approaches exist to explain GNN models with diverse (sometimes conflicting) evaluation methodologies. In this paper, we propose a benchmark for evaluating the explainability approaches for GNNs called Bagel. In Bagel, we firstly propose four diverse GNN explanation evaluation regimes -- 1) faithfulness, 2) sparsity, 3) correctness. and 4) plausibility. We reconcile multiple evaluation metrics in the existing literature and cover diverse notions for a holistic evaluation. Our graph datasets range from citation networks, document graphs, to graphs from molecules and proteins. We conduct an extensive empirical study on four GNN models and nine post-hoc explanation approaches for node and graph classification tasks. We open both the benchmarks and reference implementations and make them available at https://github.com/Mandeep-Rathee/Bagel-benchmark.
Graph Condensation via Receptive Field Distribution Matching

Graph neural networks (GNNs) enable the analysis of graphs using deep learning, with promising results in capturing structured information in graphs. This paper focuses on creating a small graph to represent the original graph, so that GNNs trained on the size-reduced graph can make accurate predictions. We view the original graph as a distribution of receptive fields and aim to synthesize a small graph whose receptive fields share a similar distribution. Thus, we propose Graph Condesation via Receptive Field Distribution Matching (GCDM), which is accomplished by optimizing the synthetic graph through the use of a distribution matching loss quantified by maximum mean discrepancy (MMD). Additionally, we demonstrate that the synthetic graph generated by GCDM is highly generalizable to a variety of models in evaluation phase and that the condensing speed is significantly improved using this framework.
A VLA View of the Flared, Asymmetric Disk around the Class 0 Protostar L1527 IRS

We present high-resolution Karl G. Jansky Very Large Array (VLA) observations of the protostar L1527 IRS at 7 mm, 1.3 cm, and 2 cm wavelengths. We detect the edge-on dust disk at all three wavelengths and find that it is asymmetric, with the southern side of the disk brighter than the northern side. We confirm this asymmetry through analytic modeling and also find that the disk is flared at 7 mm. We test the data against models including gap features in the intensity profile, and though we cannot rule such models out, they do not provide a statistically significant improvement in the quality of fit to the data. From these fits, we can, however, place constraints on allowed properties of any gaps that could be present in the true, underlying intensity profile. The physical nature of the asymmetry is difficult to associate with physical features owing to the edge-on nature of the disk, but it could be related to spiral arms or asymmetries seen in other imaging of more face-on disks.