Miracle-dz/UniFAR-FTUs huggingface dataset:
Present-Day Tectonic Deformation Partitioning Across South Tianshan From Satellite Geodetic Imaging

The Tianshan orogenic belt has intense crustal deformation and shortening in the near north-south direction. We processed ascending and descending Sentinel-1A/B 1synthetic aperture radar (SAR) data to obtain the first line-of-sight deformation field over South Tianshan (75–78 E), and combined with GPS velocity data to construct the current three dimensions surface velocity fields with high quality and high spatial resolution. The results show that: 1) deformation is not evenly distributed in the study area. The convergence across the Tianshan ranges is approximately 15–24 mm/yr; the deformation gradient in the junction area between South Tianshan and Pamir is the largest, and adjusts ∼68% of the total convergence deformation. South Tianshan is relatively stable without sharp gradients, and ∼58% of the deformation is distributed in the intermontane faults and basin systems in the north of South Tianshan. 2) Thrust slip of faults, uplift of folds, and slippage of décollement jointly play important roles in accommodating regional strain. The Maidan fault, which is the boundary between South Tianshan and Tarim Basin, has strike-slip and thrust characteristics, and is the root fault of the slip partitioning system. Our results can provide effective kinematic boundary constraints for the study of Asian crustal movement models.
Correlation Between Matrix Metalloproteinases With Coronary Artery Lesion Caused by Kawasaki Disease

This study was designed to clarify the role of matrix metalloproteinases (MMPs) in coronary artery lesions (CAL). Serum samples were acquired from healthy, febrile, and Kawasaki disease (KD) children with or without CAL. Standard blood parameters were examined and enzyme-linked immunosorbent assay (ELISA) was used to assess the levels of MMP-2 and MMP-9. Intravenous immunoglobulin (IVIG) therapy was conducted on the KD patients and the changes of MMPs before and after treatment were compared. The correlations between MMP levels and clinical parameters were also evaluated. Compared to febrile and healthy controls, KD patients demonstrated clinical signs characteristic of abnormal immunoregulation. However, the clinical parameters of KD patients with or without CAL were not significantly different. MMP-2 and MMP-9 levels, however, were significantly higher in KD patients with CAL than those without CAL. IVIG treatment effectively downregulated the levels of MMPs in KD patients, which was more prominent in those with CAL. Significant correlations were found between MMP levels and some clinical parameters of KD, such as fever time, white blood cell count, etc. The upregulation of MMPs significantly correlates with coronary artery aneurysms (CAAs) in KD patients, making it important biomarkers of CAL in KD patients.
Carrier Doping Physics of Rare Earth Perovskite Nickelates RENiO3

The family of rare earth (RE) nickelate perovskites RENiO3 has emerged over the past two decades as an important platform for quantum matter physics and advanced applications. The parent compounds from this family are strongly correlated insulators or metals, in most cases with long-range spin order. In the past few years, carrier doping has been achieved using different approaches and has been proven to be a powerful tuning parameter for the microscopic properties and collective macroscopic states in RENiO3 compounds. In particular, a series of recent studies has shown that carrier doping can be responsible for dramatic but reversible changes in the long-range electronic and magnetic properties, underscoring the potential for use of nickelates in advanced functional devices. In this review, we discuss the recent advancements in our description, understanding and application of electron-doped rare earth nickelates. We conclude with a discussion of the developments and outlook for harnessing the quantum functional properties of nickelates in novel devices for sensing and neuromorphic computation.
Online false discovery rate control for LORD++ and SAFFRON under positive, local dependence.

Online testing procedures assume that hypotheses are observed in sequence, and allow the significance thresholds for upcoming tests to depend on the test statistics observed so far. Some of the most popular online methods include alpha investing, LORD++, and SAFFRON. These three methods have been shown to provide online control of the "modified" false discovery rate (mFDR) under a condition known as CS. However, to our knowledge, LORD++ and SAFFRON have only been shown to control the traditional false discovery rate (FDR) under an independence condition on the test statistics. Our work bolsters these results by showing that SAFFRON and LORD++ additionally ensure online control of the FDR under a "local" form of nonnegative dependence. Further, FDR control is maintained under certain types of adaptive stopping rules, such as stopping after a certain number of rejections have been observed. Because alpha investing can be recovered as a special case of the SAFFRON framework, our results immediately apply to alpha investing as well. In the process of deriving these results, we also formally characterize how the conditional super-uniformity assumption implicitly limits the allowed p-value dependencies. This implicit limitation is important not only to our proposed FDR result, but also to many existing mFDR results.
The Solar Neighborhood. XLIX. New Discoveries and Orbits of M-dwarf Multiples with Speckle Interferometry at SOAR

We present the first results of a multiyear program to map the orbits of M-dwarf multiples within 25 pc. The observations were conducted primarily during 2019–2020 using speckle interferometry at the Southern Astrophysical Research Telescope in Chile, using the High-Resolution Camera mounted on the adaptive optics module (HRCam+SAM). The sample of nearby M dwarfs is drawn from three sources: multiples from the RECONS long-term astrometric monitoring program at the SMARTS 0.9 m; known multiples, for which these new observations will enable or improve orbit fits; and candidate multiples flagged by their astrometric fits in Gaia Data Release 2 (DR2). We surveyed 333 of our 338 M dwarfs via 830 speckle observations, detecting companions for 63% of the stars. Most notably, this includes new companions for 76% of the subset selected from Gaia DR2. In all, we report the first direct detections of 97 new stellar companions to the observed M dwarfs. Here we present the properties of those detections, the limits of each nondetection, and five orbits with periods 0.67–29 yr already observed as part of this program. Companions detected have projected separations of 0. ″024–2. ″0 (0.25–66 au) from their primaries and have ΔI ≲ 5.0 mag. This multiyear campaign will ultimately map complete orbits for nearby M dwarfs with periods up to 3 yr, and provide key epochs to stretch orbital determinations for binaries to 30 yr.
Augmenting Neural Networks with Priors on Function Values

The need for function estimation in label-limited settings is common in the natural sciences. At the same time, prior knowledge of function values is often available in these domains. For example, data-free biophysics-based models can be informative on protein properties, while quantum-based computations can be informative on small molecule properties. How can we coherently leverage such prior knowledge to help improve a neural network model that is quite accurate in some regions of input space -- typically near the training data -- but wildly wrong in other regions? Bayesian neural networks (BNN) enable the user to specify prior information only on the neural network weights, not directly on the function values. Moreover, there is in general no clear mapping between these. Herein, we tackle this problem by developing an approach to augment BNNs with prior information on the function values themselves. Our probabilistic approach yields predictions that rely more heavily on the prior information when the epistemic uncertainty is large, and more heavily on the neural network when the epistemic uncertainty is small.
Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap

Simulator-based models are models for which the likelihood is intractable but simulation of synthetic data is possible. They are often used to describe complex real-world phenomena, and as such can often be misspecified in practice. Unfortunately, existing Bayesian approaches for simulators are known to perform poorly in those cases. In this paper, we propose a novel algorithm based on the posterior bootstrap and maximum mean discrepancy estimators. This leads to a highly-parallelisable Bayesian inference algorithm with strong robustness properties. This is demonstrated through an in-depth theoretical study which includes generalisation bounds and proofs of frequentist consistency and robustness of our posterior. The approach is then assessed on a range of examples including a g-and-k distribution and a toggle-switch model.
Understanding Twitters behavior during the pandemic: Fake News and Fear

The outbreak of the SARS-CoV-2 novel coronavirus (COVID-19) has been accompanied by a large amount of misleading and false information about the virus, especially on social media. During the pandemic social media gained special interest as it went on to become an important medium of communication. This made the information being relayed on these platforms especially critical. In our work, we aim to explore the percentage of fake news being spread on Twitter as well as measure the sentiment of the public at the same time. We further study how the sentiment of fear is present among the public. In addition to that we compare the rate of spread of the virus per day with the rate of spread of fake news on Twitter. Our study is useful in establishing the role of Twitter, and social media, during a crisis, and more specifically during crisis management.
Royalflush Speaker Diarization System for ICASSP 2022 Multi-channel Multi-party Meeting Transcription Challenge

This paper describes the Royalflush speaker diarization system submitted to the Multi-channel Multi-party Meeting Transcription Challenge(M2MeT). Our system comprises speech enhancement, overlapped speech detection, speaker embedding extraction, speaker clustering, speech separation and system fusion. In this system, we made three contributions. First, we propose an architecture of combining the multi-channel and U-Net-based models, aiming at utilizing the benefits of these two individual architectures, for far-field overlapped speech detection. Second, in order to use overlapped speech detection model to help speaker diarization, a speech separation based overlapped speech handling approach, in which the speaker verification technique is further applied, is proposed. Third, we explore three speaker embedding methods, and obtained the state-of-the-art performance on the CNCeleb-E test set. With these proposals, our best individual system significantly reduces DER from 15.25% to 6.40%, and the fusion of four systems finally achieves a DER of 6.30% on the far-field Alimeeting evaluation set.
Designing Closed Human-in-the-loop Deferral Pipelines

In hybrid human-machine deferral frameworks, a classifier can defer uncertain cases to human decision-makers (who are often themselves fallible). Prior work on simultaneous training of such classifier and deferral models has typically assumed access to an oracle during training to obtain true class labels for training samples, but in practice there often is no such oracle. In contrast, we consider a"closed"decision-making pipeline in which the same fallible human decision-makers used in deferral also provide training labels. How can imperfect and biased human expert labels be used to train a fair and accurate deferral framework? Our key insight is that by exploiting weak prior information, we can match experts to input examples to ensure fairness and accuracy of the resulting deferral framework, even when imperfect and biased experts are used in place of ground truth labels. The efficacy of our approach is shown both by theoretical analysis and by evaluation on two tasks.
Revisiting the evolved hypergiants in the Magellanic Clouds

The massive stars that survive the phase of red supergiants (RSGs) spend the rest of their life in extremity. Their unstable atmospheres facilitate the formation and episodic ejection of shells that alter the stellar appearance and surroundings. In the present study, we revise the evolutionary state of eight hypergiants in the Magellanic Clouds, four of early-A type and four of FG type, and complement the short list of the eruptive post-RSGs termed as yellow hypergiants (YHGs). We refine the outdated temperatures and luminosities of the stars by means of high-resolution spectroscopy with FEROS. The A-type stars are suggested to be in their early, post-main sequence phase, showing spectrophotometric characteristics of redward evolving supergiants. On the other hand, the FG-type stars manifest themselves through the enhanced atmospheric activity that is traced by emission filling in Hα and the dynamical modulation of the low-excitation Ba ii line. Of these stars, the dusty HD269723 is suggested to have recently departed from a cool phase. We identify double-peaked emission in the FEROS data of HD269953 that emerges from an orbiting disk-hosting companion. The highlight of the study is an episode of enhanced mass loss of HD271182 that manifests as a dimming event in the lightcurve and renders the star “modest” analogue to ρ Cas. The luminosity log (L/L⊙) = 5.6 of HD271182 can serve as an updated threshold for the luminosity of stars exhibiting a post-RSG evolution in the Large Magellanic Cloud.
AA-TransUNet: Attention Augmented TransUNet For Nowcasting Tasks

Data driven modeling based approaches have recently gained a lot of attention in many challenging meteoro-logical applications including weather element forecasting. This paper introduces a novel data-driven predictive model based on TransUNet for precipitation nowcasting task. The TransUNet model which combines the Transformer and U-Net models has been previously successfully applied in medical segmentation tasks. Here, TransUnet is used as a core model and is further equipped with Convolutional Block Attention Modules (CBAM) and Depthwise-separable Convolution (DSC). The proposed Attention Augmented TransUNet (AA- TransUNet) model is evaluated on two distinct datasets: the Dutch precipitation map dataset and the French cloud cover dataset. The obtained results show that the proposed model outperforms other examined models on both tested datasets. Furthermore, the uncertainty analysis of the proposed AA-TransUNet is provided to give additional insights on its predictions.
Thermodynamic formalism for expanding measures

In this paper we study the thermodynamic formalism of strongly transitive endomorphisms $f$, focusing on the set all expanding measures. In case $f$ is a non-flat $C^{1+}$ map defined on a Riemannian manifold, these are invariant probability measures with all its Lyapunov exponents positive. Given a H\"older continuous potential $\varphi$ we prove the uniqueness of the equilibrium state among the space of expanding measures. Moreover, we show that the existence of an expanding measure $\mu$ maximizing the entropy on the the space of expanding measures implies the existence and uniqueness of equilibrium state $\mu_{\varphi}$ on the space of expanding measures for any H\"older continuous potential $\varphi$ with a small oscillation $\text{osc }\varphi=\sup\varphi-\inf\varphi$. As some applications, we prove that Collet-Eckmann quadratic maps does not admit phase transition for H\"older potential, and show that for Viana maps and every H\"older continuous potential of sufficiently small oscillation has a unique equilibrium state.
F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization

Neural network quantization is a promising compression technique to reduce memory footprint and save energy consumption, potentially leading to real-time inference. However, there is a performance gap between quantized and full-precision models. To reduce it, existing quantization approaches require high-precision INT32 or full-precision multiplication during inference for scaling or dequantization. This introduces a noticeable cost in terms of memory, speed, and required energy. To tackle these issues, we present F8Net, a novel quantization framework consisting of only fixed-point 8-bit multiplication. To derive our method, we first discuss the advantages of fixed-point multiplication with different formats of fixed-point numbers and study the statistical behavior of the associated fixed-point numbers. Second, based on the statistical and algorithmic analysis, we apply different fixed-point formats for weights and activations of different layers. We introduce a novel algorithm to automatically determine the right format for each layer during training. Third, we analyze a previous quantization algorithm -- parameterized clipping activation (PACT) -- and reformulate it using fixed-point arithmetic. Finally, we unify the recently proposed method for quantization fine-tuning and our fixed-point approach to show the potential of our method. We verify F8Net on ImageNet for MobileNet V1/V2 and ResNet18/50. Our approach achieves comparable and better performance, when compared not only to existing quantization techniques with INT32 multiplication or floating-point arithmetic, but also to the full-precision counterparts, achieving state-of-the-art performance.
B EST A RM I DENTIFICATION WITH A F IXED B UDGET UNDER A S MALL G AP

We consider the fixed-budget best arm identification problem in the multi-armed bandit problem. One of the main interests in this field is to derive a tight lower bound on the probability of misidentifying the best arm and to develop a strategy whose performance guarantee matches the lower bound. However, it has long been an open problem when the optimal allocation ratio of arm draws is unknown. In this paper, we provide an answer for this problem under which the gap between the expected rewards is small. First, we derive a tight problem-dependent lower bound, which characterizes the optimal allocation ratio that depends on the gap of the expected rewards and the Fisher information of the bandit model. Then, we propose the “RS-AIPW” strategy, which consists of the randomized sampling (RS) rule using the estimated optimal allocation ratio and the recommendation rule using the augmented inverse probability weighting (AIPW) estimator. Our proposed strategy is optimal in the sense that the performance guarantee achieves the derived lower bound under a small gap. In the course of the analysis, we present a novel large deviation bound for martingales.
Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks

Dealing with missing values and incomplete time series is a labor-intensive, tedious, inevitable task when handling data coming from real-world applications. Effective spatio-temporal representations would allow imputation methods to reconstruct missing temporal data by exploiting information coming from sensors at different locations. However, standard methods fall short in capturing the nonlinear time and space dependencies existing within networks of interconnected sensors and do not take full advantage of the available - and often strong - relational information. Notably, most state-of-the-art imputation methods based on deep learning do not explicitly model relational aspects and, in any case, do not exploit processing frameworks able to adequately represent structured spatio-temporal data. Conversely, graph neural networks have recently surged in popularity as both expressive and scalable tools for processing sequential data with relational inductive biases. In this work, we present the first assessment of graph neural networks in the context of multivariate time series imputation. In particular, we introduce a novel graph neural network architecture, named GRIN, which aims at reconstructing missing data in the different channels of a multivariate time series by learning spatio-temporal representations through message passing. Empirical results show that our model outperforms state-of-the-art methods in the imputation task on relevant real-world benchmarks with mean absolute error improvements often higher than 20%.
Cooperative Online Learning with Feedback Graphs

We study the interplay between feedback and communication in a cooperative online learning setting where a network of agents solves a task in which the learners' feedback is determined by an arbitrary graph. We characterize regret in terms of the independence number of the strong product between the feedback graph and the communication network. Our analysis recovers as special cases many previously known bounds for distributed online learning with either expert or bandit feedback. A more detailed version of our results also captures the dependence of the regret on the delay caused by the time the information takes to traverse each graph. Experiments run on synthetic data show that the empirical behavior of our algorithm is consistent with the theoretical results.
A Directional Equispaced interpolation-based Fast Multipole Method for oscillatory kernels

Fast Multipole Methods (FMMs) based on the oscillatory Helmholtz kernel can reduce the cost of solving N-body problems arising from Boundary Integral Equations (BIEs) in acoustic or electromagnetics. However, their cost strongly increases in the high-frequency regime. This paper introduces a new directional FMM for oscillatory kernels (defmm - directional equispaced interpolation-based fmm), whose precomputation and application are FFT-accelerated due to polynomial interpolations on equispaced grids. We demonstrate the consistency of our FFT approach, and show how symmetries can be exploited in the Fourier domain. We also describe the algorithmic design of defmm, well-suited for the BIE non-uniform particle distributions, and present performance optimizations on one CPU core. Finally, we exhibit important performance gains on all test cases for defmm over a state-of-the-art FMM library for oscillatory kernels.
PINs: Progressive Implicit Networks for Multi-Scale Neural Representations

Multi-layer perceptrons (MLP) have proven to be effective scene encoders when combined with higher-dimensional projections of the input, commonly referred to as \textit{positional encoding}. However, scenes with a wide frequency spectrum remain a challenge: choosing high frequencies for positional encoding introduces noise in low structure areas, while low frequencies result in poor fitting of detailed regions. To address this, we propose a progressive positional encoding, exposing a hierarchical MLP structure to incremental sets of frequency encodings. Our model accurately reconstructs scenes with wide frequency bands and learns a scene representation at progressive level of detail \textit{without explicit per-level supervision}. The architecture is modular: each level encodes a continuous implicit representation that can be leveraged separately for its respective resolution, meaning a smaller network for coarser reconstructions. Experiments on several 2D and 3D datasets show improvements in reconstruction accuracy, representational capacity and training speed compared to baselines.