Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep-Learning-Based Precipitation Nowcasting with Ground Weather Station Data and Radar Data

Recently, many deep-learning techniques have been applied to various weather-related prediction tasks, including precipitation nowcasting (i.e., predicting precipitation levels and locations in the near future). Most existing deep-learning-based approaches for precipitation nowcasting, however, consider only radar and/or satellite images as inputs, and meteorological observations collected from ground weather stations, which are sparsely located, are relatively unexplored. In this paper, we propose ASOC, a novel attentive method for effectively exploiting ground-based meteorological observations from multiple weather stations. ASOC is designed to capture temporal dynamics of the observations and also contextual relationships between them. ASOC is easily combined with existing image-based precipitation nowcasting models without changing their architectures. We show that such a combination improves the average critical success index (CSI) of predicting heavy (at least 10 mm/hr) and light (at least 1 mm/hr) rainfall events at 1–6 hr lead times by 5.7%, compared to the original image-based model, using the radar images and ground-based observations around South Korea collected from 2014 to 2020.
Generalized Likelihood Ratio Test With One-Class Classifiers

—One-class classiﬁcation (OCC) is the problem of deciding whether an observed sample belongs to a target class or not. We consider the problem of learning an OCC model when the dataset available at the learning stage contains only samples from the target class. We aim at obtaining a classiﬁer that performs as the generalized likelihood ratio test (GLRT), which is a well-known and provably optimal (under speciﬁc assumptions) classiﬁer, when the statistics of the target class is available. To this end, we consider both the multilayer perceptron neural network (NN) and the support vector machine (SVM) models. They are trained as two-class classiﬁers using an artiﬁcial dataset for the alternative class, obtained by generating random samples, uniformly over the domain of the target-class dataset. We prove that, under suitable assumptions, the models converge (with a large dataset) to the GLRT. Moreover, we show that the one-class least squares SVM (OCLSSVM) at convergence performs as the GLRT, with a suitable transformation function. Lastly, we compare the obtained solutions with the autoencoder (AE) classiﬁer, which does not in general provide the GLRT.
Feature Engineering and Classification Models for Partial Discharge in Power Transformers

To ensure reliability, power transformers are monitored for partial discharge (PD) events, which are symptoms of transformer failure. Since failures can have catastrophic cascading consequences, it is critical to preempt them as early as possi-ble. Our goal is to classify PDs as corona , floating , particle , or void , to gain an understanding of the failure location. Using phase resolved PD signal data, we create a small set of features, which can be used to classify PDs with high accuracy. This set of features consists of the total magnitude, the maximum magnitude, and the length of the longest empty band. These features represent the entire signal and not just a single phase, so the feature set has a fixed size and is easily comprehensible. With both Random Forest and SVM classification methods, we attain a 99% classification accuracy, which is significantly higher than classification using phase based feature sets such as phase magnitude. Furthermore, we develop a stacking ensemble to combine several classification models, resulting in a superior model that outperforms existing methods in both accuracy and variance.
Resolved Molecular Gas Observations of MaNGA Post-starbursts Reveal a Tumultuous Past

Post-starburst (PSB) galaxies have recently and rapidly quenched their star formation; thus, they are an important way to understand how galaxies transition from star-forming late types to quiescent early types. The recent discovery of large cold gas reservoirs in PSB galaxies calls into question the theory that galaxies must lose their gas to become quiescent. Optical Integral Field Spectroscopy (IFS) surveys have revealed two classes of PSB galaxies: central PSB (cPSB) galaxies with central quenching regions and ring PSB (rPSB) galaxies with quenching in their outskirts. We analyze a sample of 13 nearby (z < 0.1) PSB galaxies with spatially resolved optical IFS data from the Mapping Nearby Galaxies at Apache Point Observatory survey and matched resolution Atacama Large Millimeter/submillimeter Array observations of 12CO(1–0). Disturbed stellar kinematics in 7/13 of our PSB galaxies and centrally concentrated molecular gas is consistent with a recent merger for most of our sample. In galaxies without merger evidence, alternate processes may funnel gas inward and suppress star formation, which may include outflows, stellar bars, and minor mergers or interactions. The star formation efficiencies of the PSB regions in nearly half our galaxies are suppressed while the gas fractions are consistent with star-forming galaxies. Active galactic nucleus (AGN) feedback may drive this stabilization, and we observe AGN-consistent emission in the centers of 5/13 galaxies. Finally, our cPSB and rPSB galaxies have similar properties except the ionized and molecular gas in cPSB galaxies is more disturbed. Overall, the molecular gas in our PSB galaxies tends to be compact and highly disturbed, resulting in concentrated gas reservoirs unable to form stars efficiently.
Information-Transport-based Policy for Simultaneous Translation

Simultaneous translation (ST) outputs translation while receiving the source inputs, and hence requires a policy to determine whether to translate a target token or wait for the next source token. The major challenge of ST is that each target token can only be translated based on the current received source tokens, where the received source information will directly affect the translation quality. So naturally, how much source information is received for the translation of the current target token is supposed to be the pivotal evidence for the ST policy to decide between translating and waiting. In this paper, we treat the translation as information transport from source to target and accordingly propose an Information-Transport-based Simultaneous Translation (ITST). ITST quantifies the transported information weight from each source token to the current target token, and then decides whether to translate the target token according to its accumulated received information. Experiments on both text-to-text ST and speech-to-text ST (a.k.a., streaming speech translation) tasks show that ITST outperforms strong baselines and achieves state-of-the-art performance.
Learning Correlated Stackelberg Equilibrium in General-Sum Multi-Leader-Single-Follower Games

Many real-world strategic games involve interactions between multiple players. We study a hierarchical multi-player game structure, where players with asymmetric roles can be separated into leaders and followers, a setting often referred to as Stackelberg game or leader-follower game. In particular, we focus on a Stackelberg game scenario where there are multiple leaders and a single follower, called the Multi-Leader-Single-Follower (MLSF) game. We propose a novel asymmetric equilibrium concept for the MLSF game called Correlated Stackelberg Equilibrium (CSE). We design online learning algorithms that enable the players to interact in a distributed manner, and prove that it can achieve no-external Stackelberg-regret learning. This further translates to the convergence to approximate CSE via a reduction from no-external regret to no-swap regret. At the core of our works, we solve the intricate problem of how to learn equilibrium in leader-follower games with noisy bandit feedback by balancing exploration and exploitation in different learning structures.
Impact of droplets onto surfactant-laden thin liquid films

Abstract We study the effect of insoluble surfactants on the impact of surfactant-free droplets onto surfactant-laden thin liquid films via a fully three-dimensional direct numerical simulation approach that employs a hybrid interface-tracking/level-set method, and by taking into account surfactant-induced Marangoni stresses due to gradients in interfacial surfactant concentration. Our numerical predictions for the temporal evolution of the surfactant-free crown are validated against the experimental work by Che & Matar (Langmuir, vol. 33, 2017, pp. 12140–12148). We focus on the ‘crown-splash regime’, and we observe that the crown dynamics evolves through various stages: from the growth of linear modes (through a Rayleigh–Plateau instability) to the development of nonlinearities leading to primary and secondary breakup events (through droplet shedding modulated by an end-pinching mechanism). We show that the addition of surfactants does not affect the wave selection via the Rayleigh–Plateau instability. However, the presence of surfactants plays a key role in the late stages of the dynamics as soon as the ligaments are driven out from the rim. Surfactant-induced Marangoni stresses delay the end-pinching mechanisms to result in longer ligaments prior to their capillary singularity. Our results indicate that Marangoni stresses bridge the gap between adjacent protrusions promoting the adjacent protrusions' collision and the merging of ligaments. Finally, we demonstrate that the addition of surfactants leads to surface rigidification and consequently to the retardation of the flow dynamics.
Towards Pragmatic Production Strategies for Natural Language Generation Tasks

This position paper proposes a conceptual framework for the design of Natural Language Generation (NLG) systems that follow efficient and effective production strategies in order to achieve complex communicative goals. In this general framework, efficiency is characterised as the parsimonious regulation of production and comprehension costs while effectiveness is measured with respect to task-oriented and contextually grounded communicative goals. We provide concrete suggestions for the estimation of goals, costs, and utility via modern statistical methods, demonstrating applications of our framework to the classic pragmatic task of visually grounded referential games and to abstractive text summarisation, two popular generation tasks with real-world applications. In sum, we advocate for the development of NLG systems that learn to make pragmatic production decisions from experience, by reasoning about goals, costs, and utility in a human-like way.
Additive Interventions Yield Robust Multi-Domain Machine Translation Models

Additive interventions are a recently-proposed mechanism for controlling target-side attributes in neural machine translation by modulating the encoder’s representation of a source sequence as opposed to manipulating the raw source sequence as seen in most previous tag-based approaches. In this work we examine the role of additive interventions in a large-scale multi-domain machine translation setting and compare its performance in various inference scenarios. We find that while the performance difference is small between intervention-based systems and tag-based systems when the domain label matches the test domain, intervention-based systems are robust to label error, making them an attractive choice under label uncertainty. Further, we find that the superiority of single-domain fine-tuning comes under question when training data is scaled, contradicting previous findings.
Exact Recovery of Community Structures Using DeepWalk and Node2vec

—Random-walk based network embedding algorithms like DeepWalk and node2vec are widely used to obtain Euclidean representation of the nodes in a network prior to performing downstream inference tasks. However, despite their impressive empirical performance, there is a lack of theoretical results explaining their large-sample behavior. In this paper, we study node2vec and DeepWalk through the perspective of matrix factorization. In particular we analyze these algorithms in the setting of community detection for stochastic blockmodel graphs (and their degree-corrected variants). By exploiting the row-wise uniform perturbation bound for leading singular vectors, we derive high-probability error bounds between the matrix factorization-based node2vec/DeepWalk embeddings and their true counterparts, uniformly over all node embeddings. Based on strong concentration results, we further show the perfect membership recovery by node2vec/DeepWalk, followed by K -means/medians algorithms. Speciﬁcally, as the network becomes sparser, our results guarantee that with large enough window size and vertices number, applying K -means/medians on the matrix factorization-based node2vec embeddings can, with high probability, correctly recover the memberships of all vertices in a network generated from the stochastic blockmodel (or its degree-corrected variants). The theoretical justiﬁcations are mirrored in the numerical experiments and real data applications, for both the original node2vec and its matrix factorization variant. embedding,
High-Quality RGB-D Reconstruction via Multi-View Uncalibrated Photometric Stereo and Gradient-SDF

Fine-detailed reconstructions are in high demand in many applications. However, most of the existing RGB-D reconstruction methods rely on pre-calculated accurate camera poses to recover the detailed surface geometry, where the representation of a surface needs to be adapted when optimizing different quantities. In this paper, we present a novel multi-view RGB-D based reconstruction method that tackles camera pose, lighting, albedo, and surface normal estimation via the utilization of a gradient signed distance field (gradient-SDF). The proposed method formulates the image rendering process using specific physically-based model(s) and optimizes the surface’s quantities on the actual surface using its volumetric representation, as opposed to other works which estimate surface quantities only near the actual surface. To validate our method, we investigate two physically-based image formation models for natural light and point light source applications. The experimental results on synthetic and real-world datasets demonstrate that the proposed method can recover high-quality geometry of the surface more faithfully than the state-of-the-art and further improves the accuracy of estimated camera poses1.
Optimizing Bilingual Neural Transducer with Synthetic Code-switching Text Generation

Code-switching describes the practice of using more than one language in the same sentence. In this study, we investigate how to optimize a neural transducer based bilingual automatic speech recognition (ASR) model for code-switching speech. Focusing on the scenario where the ASR model is trained without supervised code-switching data, we found that semi-supervised training and synthetic code-switched data can improve the bilingual ASR system on code-switching speech. We analyze how each of the neural transducer's encoders contributes towards code-switching performance by measuring encoder-specific recall values, and evaluate our English/Mandarin system on the ASCEND data set. Our final system achieves 25% mixed error rate (MER) on the ASCEND English/Mandarin code-switching test set -- reducing the MER by 2.1% absolute compared to the previous literature -- while maintaining good accuracy on the monolingual test sets.
A New Distance to the Supernova Remnant DA 530 Based on H i Absorption of Polarized Emission

Supernova remnants (SNRs) are significant contributors of matter and energy to the interstellar medium. Understanding the impact and the mechanism of this contribution requires knowledge of the physical size, energy, and expansion rate of individual SNRs, which can only come if reliable distances can be obtained. We aim to determine the distance to the SNR DA 530 (G93.3+6.9), an object of low surface brightness. To achieve this, we used the Dominion Radio Astrophysical Observatory Synthesis Telescope and the National Radio Astronomy Observatory Very Large Array to observe the absorption by intervening H i of the polarized emission from DA 530. Significant absorption was detected at velocities −28 and −67 km s−1 (relative to the local standard of rest), corresponding to distances of 4.4 and 8.3 kpc, respectively. Based on the radio and X-ray characteristics of DA 530, we conclude that the minimum distance is 4.4−0.2+0.4 kpc. At this minimum distance, the diameter of the SNR is 34 −1+4 pc, and the elevation above the Galactic plane is 537 −32+40 pc. The −67 km s−1 absorption likely occurs in gas whose velocity is not determined by Galactic rotation. We present a new data processing method for combining Stokes Q and U observations of the emission from an SNR into a single H i absorption spectrum, which avoids the difficulties of the noise-bias subtraction required for the calculation of polarized intensity. The polarized absorption technique can be applied to determine the distances to many more SNRs.
STAR FORMATION IN ORION'S L1630 CLOUD: AN INFRARED AND MULTI-EPOCH X-RAY STUDY

X-ray emission is characteristic of young stellar objects (YSOs) and is known to be highly variable. We investigate, via an infrared and multi-epoch X-ray study of the L1630 dark cloud, whether and how X-ray variability in YSOs is related to protostellar evolutionary state. We have analyzed 11 Chandra X-Ray Observatory observations, obtained over the course of four years and totaling ∼240 ks exposure time, targeting the eruptive Class I YSO V1647 Ori in L1630. We used Two Micron All Sky Survey and Spitzer data to identify and classify IR counterparts to L1630 X-ray sources and identified a total of 52 X-ray-emitting YSOs with IR counterparts, including four Class I sources and one Class 0/I source. We have detected cool (<3 MK) plasma, possibly indicative of accretion shocks, in three classical T Tauri stars. A subsample of 27 X-ray-emitting YSOs were covered by 9 of the 11 Chandra observations targeting V1647 Ori and the vicinity. For these 27 YSOs, we have constructed X-ray light curves spanning approximately four years. These light curves highlight the variable nature of pre-main-sequence X-ray-emitting young stars; many of the L1630 YSOs vary by orders of magnitude in count rate between observations. We discuss possible scenarios to explain apparent trends between various X-ray spectral properties, X-ray variance, and YSO classification.
ComFact: A Benchmark for Linking Contextual Commonsense Knowledge

Understanding rich narratives, such as dialogues and stories, often requires natural language processing systems to access relevant knowledge from commonsense knowledge graphs. However, these systems typically retrieve facts from KGs using simple heuristics that disregard the complex challenges of identifying situationally-relevant commonsense knowledge (e.g., contextualization, implicitness, ambiguity). In this work, we propose the new task of commonsense fact linking, where models are given contexts and trained to identify situationally-relevant commonsense knowledge from KGs. Our novel benchmark, ComFact, contains ~293k in-context relevance annotations for commonsense triplets across four stylistically diverse dialogue and storytelling datasets. Experimental results confirm that heuristic fact linking approaches are imprecise knowledge extractors. Learned fact linking models demonstrate across-the-board performance improvements (~34.6% F1) over these heuristics. Furthermore, improved knowledge retrieval yielded average downstream improvements of 9.8% for a dialogue response generation task. However, fact linking models still significantly underperform humans, suggesting our benchmark is a promising testbed for research in commonsense augmentation of NLP systems.
The Schwarz alternating method for the seamless coupling of nonlinear reduced order models and full order models

Projection-based model order reduction allows for the parsimonious representation of full order models (FOMs), typically obtained through the discretization of certain partial differential equations (PDEs) using conventional techniques where the discretization may contain a very large number of degrees of freedom. As a result of this more compact representation, the resulting projection-based reduced order models (ROMs) can achieve considerable computational speedups, which are especially useful in real-time or multi-query analyses. One known deficiency of projection-based ROMs is that they can suffer from a lack of robustness, stability and accuracy, especially in the predictive regime, which ultimately limits their useful application. Another research gap that has prevented the widespread adoption of ROMs within the modeling and simulation community is the lack of theoretical and algorithmic foundations necessary for the"plug-and-play"integration of these models into existing multi-scale and multi-physics frameworks. This paper describes a new methodology that has the potential to address both of the aforementioned deficiencies by coupling projection-based ROMs with each other as well as with conventional FOMs by means of the Schwarz alternating method. Leveraging recent work that adapted the Schwarz alternating method to enable consistent and concurrent multi-scale coupling of finite element FOMs in solid mechanics, we present a new extension of the Schwarz formulation that enables ROM-FOM and ROM-ROM coupling in nonlinear solid mechanics. In order to maintain efficiency, we employ hyper-reduction via the Energy-Conserving Sampling and Weighting approach. We evaluate the proposed coupling approach in the reproductive as well as in the predictive regime on a canonical test case that involves the dynamic propagation of a traveling wave in a nonlinear hyper-elastic material.
AACHER: Assorted Actor-Critic Deep Reinforcement Learning with Hindsight Experience Replay

Actor learning and critic learning are two components of the outstanding and mostly used Deep Deterministic Policy Gradient (DDPG) reinforcement learning method. Since actor and critic learning plays a significant role in the overall robot's learning, the performance of the DDPG approach is relatively sensitive and unstable as a result. We propose a multi-actor-critic DDPG for reliable actor-critic learning to further enhance the performance and stability of DDPG. This multi-actor-critic DDPG is then integrated with Hindsight Experience Replay (HER) to form our new deep learning framework called AACHER. AACHER uses the average value of multiple actors or critics to substitute the single actor or critic in DDPG to increase resistance in the case when one actor or critic performs poorly. Numerous independent actors and critics can also gain knowledge from the environment more broadly. We implemented our proposed AACHER on goal-based environments: AuboReach, FetchReach-v1, FetchPush-v1, FetchSlide-v1, and FetchPickAndPlace-v1. For our experiments, we used various instances of actor/critic combinations, among which A10C10 and A20C20 were the best-performing combinations. Overall results show that AACHER outperforms the traditional algorithm (DDPG+HER) in all of the actor/critic number combinations that are used for evaluation. When used on FetchPickAndPlace-v1, the performance boost for A20C20 is as high as roughly 3.8 times the success rate in DDPG+HER.