Miracle-dz/UniFAR-FTUs huggingface dataset:
Motion-aware Self-supervised Video Representation Learning via Foreground-background Merging

In light of the success of contrastive learning in the image domain, current self-supervised video representation learning methods usually employ contrastive loss to facilitate video representation learning. When naively pulling two augmented views of a video closer, the model however tends to learn the common static background as a shortcut but fails to capture the motion information, a phenomenon dubbed as background bias. This bias makes the model suffer from weak generalization ability, leading to worse performance on downstream tasks such as action recognition. To alleviate such bias, we propose F oreground-b a ckground Me rging (FAME) to deliberately compose the foreground region of the selected video onto the background of others. Speciﬁ-cally, without any off-the-shelf detector, we extract the foreground and background regions via the frame difference and color statistics, and shufﬂe the background regions among the videos. By leveraging the semantic consistency between the original clips and the fused ones, the model focuses more on the foreground motion pattern and is thus more robust to the background context. Extensive experiments demonstrate that FAME can signiﬁcantly boost the performance in different downstream tasks with various backbones. When integrated with MoCo, FAME reaches 84.8% and 53.5% accuracy on UCF101 and HMDB51, respectively, achieving the state-of-the-art performance.
Causal Matrix Completion

Matrix completion is the study of recovering an underlying matrix from a sparse subset of noisy observations. Traditionally, it is assumed that the entries of the matrix are"missing completely at random"(MCAR), i.e., each entry is revealed at random, independent of everything else, with uniform probability. This is likely unrealistic due to the presence of"latent confounders", i.e., unobserved factors that determine both the entries of the underlying matrix and the missingness pattern in the observed matrix. For example, in the context of movie recommender systems -- a canonical application for matrix completion -- a user who vehemently dislikes horror films is unlikely to ever watch horror films. In general, these confounders yield"missing not at random"(MNAR) data, which can severely impact any inference procedure that does not correct for this bias. We develop a formal causal model for matrix completion through the language of potential outcomes, and provide novel identification arguments for a variety of causal estimands of interest. We design a procedure, which we call"synthetic nearest neighbors"(SNN), to estimate these causal estimands. We prove finite-sample consistency and asymptotic normality of our estimator. Our analysis also leads to new theoretical results for the matrix completion literature. In particular, we establish entry-wise, i.e., max-norm, finite-sample consistency and asymptotic normality results for matrix completion with MNAR data. As a special case, this also provides entry-wise bounds for matrix completion with MCAR data. Across simulated and real data, we demonstrate the efficacy of our proposed estimator.
Born Again Neural Rankers

We introduce Born Again neural Rankers (BAR) in the Learning to Rank (LTR) setting, where student rankers, trained in the Knowledge Distillation (KD) framework, are parameterized identically to their teachers. Unlike the existing ranking distillation work which pursues a good trade-oﬀ between performance and eﬃciency, BAR adapts the idea of Born Again Networks (BAN) to ranking problems and signiﬁcantly improves ranking performance of students over the teacher rankers without increasing model capacity. The key diﬀerences between BAR and common distillation techniques for classi-ﬁcation are: (1) an appropriate teacher score transformation function, and (2) a novel listwise distillation framework. Both techniques are speciﬁcally designed for ranking problems and are rarely studied in the knowledge distillation literature. Using the state-of-the-art neural ranking structure, BAR is able to push the limits of neural rankers above a recent rigorous benchmark study and signiﬁcantly outperforms traditionally strong gradient boosted decision tree based models on 7 out of 9 key metrics, the ﬁrst time in the literature. In addition to the strong empirical results, we give theoretical explanations on why listwise distillation is eﬀective for neural rankers.
Bursting Bubbles: Feedback from Clustered Supernovae and the Trade-off Between Turbulence and Outflows

We present an analytic model for clustered supernovae (SNe) feedback in galaxy disks, incorporating the dynamical evolution of superbubbles formed from spatially overlapping SNe remnants. We propose two realistic outcomes for the evolution of superbubbles in galactic disks: (1) the expansion velocity of the shock front falls below the turbulent velocity dispersion of the interstellar medium in the galaxy disk, whereupon the superbubble stalls and fragments, depositing its momentum entirely within the galaxy disk; or (2) the superbubble grows in size to reach the gas scale height, breaking out of the galaxy disk and driving galactic outflows/fountains. In either case, we find that superbubble breakup/breakout almost always occurs before the last Type II SN (≲40 Myr) in the recently formed star cluster, assuming a standard high-end initial mass function slope, and scalings between stellar lifetimes and masses. The threshold between these two cases implies a break in the effective strength of feedback in driving turbulence within galaxies, and a resulting change in the scalings of, for example, star formation rates with gas surface density (the Kennicutt–Schmidt relation) and the star formation efficiency in galaxy disks.
LIFE: Learning Individual Features for Multivariate Time Series Prediction with Missing Values

Multivariate time series (MTS) prediction is ubiquitous in real-world fields, but MTS data often contains missing values. In recent years, there has been an increasing interest in using end-to-end models to handle MTS with missing values. To generate features for prediction, existing methods either merge all input dimensions of MTS or tackle each input dimension independently. However, both approaches are hard to perform well because the former usually produce many unreliable features and the latter lacks correlated information. In this paper, we propose a Learning Individual Features (LIFE) framework, which provides a new paradigm for MTS prediction with missing values. LIFE generates reliable features for prediction by using the correlated dimensions as auxiliary information and suppressing the interference from uncorrelated dimensions with missing values. Experiments on two real-world data sets verify the superiority of LIFE to existing state-of-the-art models. The full version of this work can refer to arXiv (2109.14844).
Syntactic Persistence in Language Models: Priming as a Window into Abstract Language Representations

We investigate the extent to which modern, neural language models are susceptible to syntactic priming, the phenomenon where the syntactic structure of a sentence makes the same structure more probable in a follow-up sentence. We explore how priming can be used to study the nature of the syntactic knowledge acquired by these models. We introduce a novel metric and release P RIME - LM, a large corpus where we control for various linguistic factors which interact with priming strength. We ﬁnd that recent large Transformer models indeed show evidence of syntactic priming, but also that the syntactic generalisations learned by these models are to some extent modulated by semantic information. We report surprisingly strong priming effects when priming with multiple sentences, each with different words and meaning but with identical syntactic structure. We conclude that the syntactic priming paradigm is a highly useful, additional tool for gaining insights into the capacities of language models.
Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook

The deep learning revolution is touching all scientific disciplines and corners of our lives as a means of harnessing the power of big data. Marine ecology is no exception. These new methods provide analysis of data from sensors, cameras, and acoustic recorders, even in real time, in ways that are reproducible and rapid. Off-the-shelf algorithms can find, count, and classify species from digital images or video and detect cryptic patterns in noisy data. Using these opportunities requires collaboration across ecological and data science disciplines, which can be challenging to initiate. To facilitate these collaborations and promote the use of deep learning towards ecosystem-based management of the sea, this paper aims to bridge the gap between marine ecologists and computer scientists. We provide insight into popular deep learning approaches for ecological data analysis in plain language, focusing on the techniques of supervised learning with deep neural networks, and illustrate challenges and opportunities through established and emerging applications of deep learning to marine ecology. We use established and future-looking case studies on plankton, fishes, marine mammals, pollution, and nutrient cycling that involve object detection, classification, tracking, and segmentation of visualized data. We conclude with a broad outlook of the field's opportunities and challenges, including potential technological advances and issues with managing complex data sets.
Physical Characterization of Main-belt Comet (248370) 2005 QN173

We report results from new and archival observations of the newly discovered active asteroid (248370) 2005 QN173 (also now designated Comet 433P), which has been determined to be a likely main-belt comet based on a subsequent discovery that it is recurrently active near perihelion. From archival data analysis, we estimate g′ -, r′ -, i′ -, and z′ -band absolute magnitudes for the nucleus of H g = 16.62 ± 0.13, H r = 16.12 ± 0.10, H i = 16.05 ± 0.11, and H z = 15.93 ± 0.08, corresponding to nucleus colors of g′−r′=0.50±0.16 , r′−i′=0.07±0.15 , and i′−z′=0.12±0.14 ; an equivalent V-band absolute magnitude of H V = 16.32 ± 0.08; and a nucleus radius of r n = 1.6 ± 0.2 km (using a V-band albedo of p V = 0.054 ± 0.012). Meanwhile, we find mean near-nucleus coma colors when 248370 is active of g′−r′=0.47±0.03 , r′−i′=0.10±0.04 , and i′−z′=0.05±0.05 and similar mean dust tail colors, suggesting that no significant gas coma is present. We find approximate ratios between the scattering cross sections of near-nucleus dust (within 5000 km of the nucleus) and the nucleus of A d /A n = 0.7 ± 0.3 on 2016 July 22 and 1.8 < A d /A n < 2.9 in 2021 July and August. During the 2021 observation period, the coma declined in intrinsic brightness by ∼0.35 mag (or ∼25%) in 37 days, while the surface brightness of the dust tail remained effectively constant over the same period. Constraints derived from the sunward extent of the coma and width of the tail as measured perpendicular to the orbit plane suggest that the terminal velocities of ejected dust grains are extremely slow (∼1 m s−1 for 1 μm particles), suggesting that the observed dust emission may be aided by rapid rotation of the nucleus lowering the effective escape velocity.
Stability Constrained Reinforcement Learning for Real-Time Voltage Control

Deep reinforcement learning (RL) has been recognized as a promising tool to address the challenges in real-time control of power systems. However, its deployment in real-world power systems has been hindered by a lack of formal stability and safety guarantees. In this paper, we propose a stability constrained reinforcement learning method for real-time voltage control in distribution grids and we prove that the proposed approach provides a formal voltage stability guarantee. The key idea underlying our approach is an explicitly constructed Lyapunov function that certifies stability. We demonstrate the effectiveness of the approach in case studies, where the proposed method can reduce the transient control cost by more than 30% and shorten the response time by a third compared to a widely used linear policy, while always achieving voltage stability. In contrast, standard RL methods often fail to achieve voltage stability. 1
The NANOGrav 12.5-year Data Set: Search for Non-Einsteinian Polarization Modes in the Gravitational-wave Background

We search NANOGrav’s 12.5 yr data set for evidence of a gravitational-wave background (GWB) with all the spatial correlations allowed by general metric theories of gravity. We find no substantial evidence in favor of the existence of such correlations in our data. We find that scalar-transverse (ST) correlations yield signal-to-noise ratios and Bayes factors that are higher than quadrupolar (tensor-transverse, TT) correlations. Specifically, we find ST correlations with a signal-to-noise ratio of 2.8 that are preferred over TT correlations (Hellings and Downs correlations) with Bayesian odds of about 20:1. However, the significance of ST correlations is reduced dramatically when we include modeling of the solar system ephemeris systematics and/or remove pulsar J0030+0451 entirely from consideration. Even taking the nominal signal-to-noise ratios at face value, analyses of simulated data sets show that such values are not extremely unlikely to be observed in cases where only the usual TT modes are present in the GWB. In the absence of a detection of any polarization mode of gravity, we place upper limits on their amplitudes for a spectral index of γ = 5 and a reference frequency of f yr = 1 yr−1. Among the upper limits for eight general families of metric theories of gravity, we find the values of ATT95%=(9.7±0.4)×10−16 and AST95%=(1.4±0.03)×10−15 for the family of metric spacetime theories that contain both TT and ST modes.
Classifying Tweet Sentiment Using the Hidden State and Attention Matrix of a Fine-tuned BERTweet Model

This paper introduces a study on tweet sentiment classification. Our task is to classify a tweet as either positive or negative. We approach the problem in two steps, namely embedding and classifying. Our baseline methods include several combinations of traditional embedding methods and classification algorithms. Furthermore, we explore the current state-of-the-art tweet analysis model, BERTweet, and propose a novel approach in which features are engineered from the hidden states and attention matrices of the model, inspired by empirical study of the tweets. Using a multi-layer perceptron trained with a high dropout rate for classification, our proposed approach achieves a validation accuracy of 0.9111.
Self-Supervised Out-of-Distribution Detection and Localization with Natural Synthetic Anomalies (NSA)

We introduce a new self-supervised task, NSA, for training an end-to-end model for anomaly detection and localization using only normal data. NSA uses Poisson image editing to seamlessly blend scaled patches of various sizes from separate images. This creates a wide range of synthetic anomalies which are more similar to natural sub-image irregularities than previous data-augmentation strategies for self-supervised anomaly detection. We evaluate the proposed method using natural and medical images. Our experiments with the MVTec AD dataset show that a model trained to localize NSA anomalies generalizes well to detecting real-world a priori unknown types of manufacturing defects. Our method achieves an overall detection AUROC of 97.2 outperforming all previous methods that learn from scratch without pre-training datasets.
HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning

Zero-shot learning (ZSL) tackles the unseen class recognition problem, transferring semantic knowledge from seen classes to unseen ones. Typically, to guarantee desirable knowledge transfer, a common (latent) space is adopted for associating the visual and semantic domains in ZSL. However, existing common space learning methods align the semantic and visual domains by merely mitigating distribution disagreement through one-step adaptation. This strategy is usually ineffective due to the heterogeneous nature of the feature representations in the two domains, which intrinsically contain both distribution and structure variations. To address this and advance ZSL, we propose a novel hierarchical semantic-visual adaptation (HSVA) framework. Specifically, HSVA aligns the semantic and visual domains by adopting a hierarchical two-step adaptation, i.e., structure adaptation and distribution adaptation. In the structure adaptation step, we take two task-specific encoders to encode the source data (visual domain) and the target data (semantic domain) into a structure-aligned common space. To this end, a supervised adversarial discrepancy (SAD) module is proposed to adversarially minimize the discrepancy between the predictions of two task-specific classifiers, thus making the visual and semantic feature manifolds more closely aligned. In the distribution adaptation step, we directly minimize the Wasserstein distance between the latent multivariate Gaussian distributions to align the visual and semantic distributions using a common encoder. Finally, the structure and distribution adaptation are derived in a unified framework under two partially-aligned variational autoencoders. Extensive experiments on four benchmark datasets demonstrate that HSVA achieves superior performance on both conventional and generalized ZSL. The code is available at \url{https://github.com/shiming-chen/HSVA} .
Multilingual AMR Parsing with Noisy Knowledge Distillation

We study multilingual AMR parsing from the perspective of knowledge distillation, where the aim is to learn and improve a multilingual AMR parser by using an existing English parser as its teacher. We constrain our exploration in a strict multilingual setting: there is but one model to parse all different languages including English. We identify that noisy input and precise output are the key to successful distillation. Together with extensive pre-training, we obtain an AMR parser whose performances surpass all previously published results on four different foreign languages, including German, Spanish, Italian, and Chinese, by large margins (up to 18.8 \textsc{Smatch} points on Chinese and on average 11.3 \textsc{Smatch} points). Our parser also achieves comparable performance on English to the latest state-of-the-art English-only parser.
Kinetic study of phenol removal from wastewater over a 0.5% pt/ .-al2o3 catalyst in a trickle bed reactor

Catalytic wet oxidation (CWO) of phenol was studied in the present work. The effect of liquid hourly space velocity (LHSV), superficial gas velocity, reactor pressure, temperature and initial phenol concentration on phenols removal was investigated. Results showed that phenol conversion increased with increasing reaction temperature, reactor pressure, and superficial gas velocity of oxygen, yet it decreased with increasing (LHSV). The results exhibited that the highest phenol conversion of (67.47%) was obtained over 0.5% Pt/. -Al2O3 at the studied reaction conditions (i.e., operating pressure = 0.6 MPa, operating temperature = 140oC, LHSV = 4 h-1, and gas superficial velocity = 0.169 m/s), with phenol concentration = 900 mg/L. According to the kinetic results, the reaction behavior was first order with respect to phenol concentration, (0.69) order with respect to oxygen and the activation energy was equal to (29.299) kJ/mol. Selectivity maps for concentration of intermediate compounds in reactor effluent were introduced which would be helpful for monitoring the reaction pathways of phenol oxidation.
Vision-Aided Beam Tracking: Explore the Proper Use of Camera Images with Deep Learning

We investigate the problem of wireless beam tracking on mmWave bands with the assistance of camera images. In particular, based on the user’ beam indices used and camera images taken in the trajectory, we predict the optimal beam indices in the next few time spots. To resolve this problem, we first reformulate the “ViWi” dataset in [1] to get rid of the image repetition problem. Then we develop a deep learning approach and investigate various model components to achieve the best performance. Finally, we explore whether, when, and how to use the image for better beam prediction. To answer this question, we split the dataset into three clusters - (LOS, light NLOS, serious NLOS)-like - based on the standard deviation of the beam sequence. With experiments we demonstrate that using the image indeed helps beam tracking especially when the user is in serious NLOS, and the solution relies on carefully-designed dataset for training a model. Generally speaking, including NLOS-like data for training a model does not benefit beam tracking of the user in LOS, but including light NLOS-like data for training a model benefits beam tracking of the user in serious NLOS.
PP-LCNet: A Lightweight CPU Convolutional Neural Network

We propose a lightweight CPU network based on the MKLDNN acceleration strategy, named PP-LCNet, which improves the performance of lightweight models on multiple tasks. This paper lists technologies which can improve network accuracy while the latency is almost constant. With these improvements, the accuracy of PP-LCNet can greatly surpass the previous network structure with the same inference time for classification. As shown in Figure 1, it outperforms the most state-of-the-art models. And for downstream tasks of computer vision, it also performs very well, such as object detection, semantic segmentation, etc. All our experiments are implemented based on PaddlePaddle. Code and pretrained models are available at PaddleClas.
Measurement placement in electric power transmission and distribution grids: Review of concepts, methods, and research needs

Sensing and measurement systems are quintessential to the safe and reliable operation of electric power grids. Their strategic placement is of ultimate importance because it is not economically viable to install measurement systems on every node and branch of a power grid, though they need to be monitored. An overwhelming number of strategies have been developed to meet oftentimes multiple conflicting objectives. The prime challenge in formulating the problem lies in developing a heuristic or an optimization model that, though mathematically tractable and constrained in cost, leads to trustworthy technical solutions. Further, large-scale, long-term deployments pose additional challenges because the boundary conditions change as technologies evolve. For instance, the advent of new technologies in sensing and measurement, as well as in communications and networking, might impact the cost and performance of available solutions and shift initially set conditions. Also, the placement strategies developed for transmission grids might not be suitable for distribution grids, and vice versa, because of unique characteristics; therefore, the strategies need to be flexible, to a certain extent, because no two power grids are alike. Despite the extensive literature on the present topic, the focus of published works tends to be on a specific subject, such as the optimal placement of measurements to ensure observability in transmission grids. There is a dearth of work providing a comprehensive picture for developing optimal placement strategies. Because of the ongoing efforts on the modernization of electric power grids, there is a need to consolidate the status quo while exposing its limitations to inform policymakers, industry stakeholders, and researchers on the research-and-development needs to push the boundaries for innovation.