Miracle-dz/UniFAR-FTUs huggingface dataset:
Rationale Aware Contrastive Learning Based Approach to Classify and Summarize Crisis-Related Microblogs

Recent fashion of information propagation on Twitter makes the platform a crucial conduit for tactical data and emergency responses during disasters. However, the real-time information about crises is immersed in a large volume of emotional and irrelevant posts. It brings the necessity to develop an automatic tool to identify disaster-related messages and summarize the information for data consumption and situation planning. Besides, explainability of the methods is crucial in determining their applicability in real-life scenarios. Recent studies also highlight the importance of learning a good latent representation of tweets for several downstream tasks. In this paper, we take advantage of state-of-the-art methods, such as transformers and contrastive learning to build an interpretable classifier. Our proposed model classifies Twitter messages into different humanitarian categories and also extracts rationale snippets as supporting evidence for output decisions. The contrastive learning framework helps to learn better representations of tweets by bringing the related tweets closer in the embedding space. Furthermore, we employ classification labels and rationales to efficiently generate summaries of crisis events. Extensive experiments over different crisis datasets show that (i). our classifier obtains the best performance-interpretability trade-off, (ii). the proposed summarizer shows superior performance (1.4%-22% improvement) with significantly less computation cost than baseline models.
Multi-Channel Attention Selection GAN With Cascaded Semantic Guidance for Cross-View Image Translation

Cross-view image translation is challenging because it involves images with drastically different views and severe deformation. In this paper, we propose a novel approach named Multi-Channel Attention SelectionGAN (SelectionGAN) that makes it possible to generate images of natural scenes in arbitrary viewpoints, based on an image of the scene and a novel semantic map. The proposed SelectionGAN explicitly utilizes the semantic information and consists of two stages. In the first stage, the condition image and the target semantic map are fed into a cycled semantic-guided generation network to produce initial coarse results. In the second stage, we refine the initial results by using a multi-channel attention selection mechanism. Moreover, uncertainty maps automatically learned from attentions are used to guide the pixel loss for better network optimization. Extensive experiments on Dayton, CVUSA and Ego2Top datasets show that our model is able to generate significantly better results than the state-of-the-art methods. The source code, data and trained models are available at https://github.com/Ha0Tang/SelectionGAN.
One Rating to Rule Them All?: Evidence of Multidimensionality in Human Assessment of Topic Labeling Quality

Two general approaches are common for evaluating automatically generated labels in topic modeling: direct human assessment; or performance metrics that can be calculated without, but still correlate with, human assessment. However, both approaches implicitly assume that the quality of a topic label is single-dimensional. In contrast, this paper provides evidence that human assessments about the quality of topic labels consist of multiple latent dimensions. This evidence comes from human assessments of four simple labeling techniques. For each label, study participants responded to several items asking them to assess each label according to a variety of different criteria. Exploratory factor analysis shows that these human assessments of labeling quality have a two-factor latent structure. Subsequent analysis demonstrates that this multi-item, two-factor assessment can reveal nuances that would be missed using either a single-item human assessment of perceived label quality or established performance metrics. The paper concludes by suggesting future directions for the development of human-centered approaches to evaluating NLP and ML systems more broadly.
Parallel Skyline Processing Using Space Pruning on GPU

Skyline computation is an essential database operation that has many applications in multi-criteria decision making scenarios such as recommender systems. Existing algorithms have focused on checking point domination, which lack efficiency over large datasets. We propose a grid-based structure that enables grid cell domination checks. We show that only a small constant number of cells need to be checked which is independent from the number of data points. Our structure also enables parallel processing. We thus obtain a highly efficient parallel skyline algorithm named SkyCell, taking advantage of the parallelization power of graphics processing units. Experimental results confirm the effectiveness and efficiency of SkyCell -- it outperforms state-of-the-art algorithms consistently and by up to over two orders of magnitude in the computation time.
Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Models

Deep learning techniques have been applied widely in industrial recommendation systems. However, far less attention has been paid on the overfitting problem of models in recommendation systems, which, on the contrary, is recognized as a critical issue for deep neural networks. In the context of Click-Through Rate (CTR) prediction, we observe an interesting one-epoch overfitting problem: the model performance exhibits a dramatic degradation at the beginning of the second epoch. Such a phenomenon has been witnessed widely in real-world applications of CTR models. Thereby, the best performance is usually achieved by training with only one epoch. To understand the underlying factors behind the one-epoch phenomenon, we conduct extensive experiments on the production data set collected from the display advertising system of Alibaba. The results show that the model structure, the optimization algorithm with a fast convergence rate, and the feature sparsity are closely related to the one-epoch phenomenon. We also provide a likely hypothesis for explaining such a phenomenon and conduct a set of proof-of-concept experiments. We hope this work can shed light on the future research on training more epochs for better performance.
Social Graph Transformer Networks for Pedestrian Trajectory Prediction in Complex Social Scenarios

Pedestrian trajectory prediction is essential for many modern applications, such as abnormal motion analysis and collision avoidance for improved traffic safety. Previous studies still face challenges in embracing high social interaction, dynamics, and multi-modality for achieving high accuracy with long-time predictions. We propose Social Graph Transformer Networks for multi-modal prediction of pedestrian trajectories, where we combine Graph Convolutional Network and Transformer Network by generating stable resolution pseudo-images from Spatio-temporal graphs through a designed stacking and interception method. Specifically, we adopt adjacency matrices to obtain Spatio-temporal features and Transformer for long-time trajectory predictions. As such, we retrain the advantages of both, i.e., the ability to aggregate information over an arbitrary number of neighbors and to conduct complex time-dependent data processing. Our experimental results show that our model reduces the final displacement error and achieves state-of-the-art in multiple metrics. The module's effectiveness is demonstrated through ablation experiments.
A Dataset for Burned Area Delineation and Severity Estimation from Satellite Imagery

The ability to correctly identify areas damaged by forest wildfires is essential to plan and monitor the restoration process and estimate the environmental damages after such catastrophic events. The wide availability of satellite data, combined with the recent development of machine learning and deep learning methodologies applied to the computer vision field, makes it extremely interesting to apply the aforementioned techniques to the field of automatic burned area detection. One of the main issues in such a context is the limited amount of labeled data, especially in the context of semantic segmentation. In this paper, we introduce a publicly available dataset for the burned area detection problem for semantic segmentation. The dataset contains 73 satellite images of different forests damaged by wildfires across Europe with a resolution of up to 10m per pixel. Data were collected from the Sentinel-2 L2A satellite mission and the target labels were generated from the Copernicus Emergency Management Service (EMS) annotations, with five different severity levels, ranging from undamaged to completely destroyed. Finally, we report the benchmark values obtained by applying a Convolutional Neural Network on the proposed dataset to address the burned area identification problem.
Visual Exploration of Literature with Argo Scholar

Discovering and making sense of relevant literature is fundamental in any scientific field. Visualization can aid this process; however, existing tools' adoption and impact are often constrained, such as by their reliance on small, curated paper datasets that quickly become outdated or a lack of support for personalized exploration. We introduce Argo Scholar, an open-source visualization tool designed for interactive exploration of literature and easy sharing of exploration results. Argo Scholar is tightly integrated with Semantic Scholar's live data of over 200 million papers, enabling users to generate personalized literature exploration results in real-time through flexible, incremental exploration, a common and effective method for researchers to discover relevant work. A large-scale user study of 122 participants from diverse backgrounds and experiences showed that Argo Scholar is effective at helping users find related work and understand paper connections, and incremental graph-based exploration is effective across diverse disciplines. Developed using modern web technologies, our tool is widely available at https://poloclub.github. io/argo-scholar and allows users to easily share their literature search results as a URL.
ADVANCED BURNING STAGES AND FATE OF 8–10 M☉ STARS

The stellar mass range 8 ≲ M/M☉ ≲ 12 corresponds to the most massive asymptotic giant branch (AGB) stars and the most numerous massive stars. It is host to a variety of supernova (SN) progenitors and is therefore very important for galactic chemical evolution and stellar population studies. In this paper, we study the transition from super-AGB (SAGB) star to massive star and find that a propagating neon–oxygen-burning shell is common to both the most massive electron capture supernova (EC-SN) progenitors and the lowest mass iron-core-collapse supernova (FeCCSN) progenitors. Of the models that ignite neon-burning off-center, the 9.5 M☉ star would evolve to an FeCCSN after the neon-burning shell propagates to the center, as in previous studies. The neon-burning shell in the 8.8 M☉ model, however, fails to reach the center as the URCA process and an extended (0.6 M☉) region of low Ye (0.48) in the outer part of the core begin to dominate the late evolution; the model evolves to an EC-SN. This is the first study to follow the most massive EC-SN progenitors to collapse, representing an evolutionary path to EC-SN in addition to that from SAGB stars undergoing thermal pulses (TPs). We also present models of an 8.75 M☉ SAGB star through its entire TP phase until electron captures on 20Ne begin at its center and of a 12 M☉ star up to the iron core collapse. We discuss key uncertainties and how the different pathways to collapse affect the pre-SN structure. Finally, we compare our results to the observed neutron star mass distribution.
SPIN VECTOR AND SHAPE OF (6070) RHEINLAND AND THEIR IMPLICATIONS

Main belt asteroids (6070) Rheinland and (54827) 2001 NQ8 belong to a small population of couples of bodies that reside in very similar heliocentric orbits. Vokrouhlický & Nesvorný promoted the term “asteroid pairs,” pointing out their common origin within the past tens to hundreds of kyr. Previous attempts to reconstruct the initial configuration of Rheinland and 2001 NQ8 at the time of their separation have led to the prediction that Rheinland's rotation should be retrograde. Here, we report extensive photometric observations of this asteroid and use the light curve inversion technique to directly determine its rotation state and shape. We confirm the retrograde sense of rotation of Rheinland, with obliquity value constrained to be ⩾140°. The ecliptic longitude of the pole position is not well constrained as yet. The asymmetric behavior of Rheinland's light curve reflects a sharp, near-planar edge in our convex shape representation of this asteroid. Our calibrated observations in the red filter also allow us to determine HR = 13.68 ± 0.05 and G = 0.31 ± 0.05 values of the H–G system. With the characteristic color index V − R = 0.49 ± 0.05 for S-type asteroids, we thus obtain H = 14.17 ± 0.07 for the absolute magnitude of (6070) Rheinland. This is a significantly larger value than previously obtained from analysis of astrometric survey observations. We next use the obliquity constraint for Rheinland to eliminate some degree of uncertainty in the past propagation of its orbit. This is because the sign of the past secular change of its semimajor axis due to the Yarkovsky effect is now constrained. The determination of the rotation state of the secondary component, asteroid (54827) 2001 NQ8, is the key element in further constraining the age of the pair and its formation process.
Efficient Trajectory Similarity Computation with Contrastive Learning

The ubiquity of mobile devices and the accompanying deployment of sensing technologies have resulted in a massive amount of trajectory data. One important fundamental task is trajectory similarity computation, which is to determine how similar two trajectories are. To enable effective and efficient trajectory similarity computation, we propose a novel robust model, namely Contrastive Learning based Trajectory Similarity Computation (CL-TSim). Specifically, we employ a contrastive learning mechanism to learn the latent representations of trajectories and then calculate the dissimilarity between trajectories based on these representations. Compared with sequential auto-encoders that are the mainstream deep learning architectures for trajectory similarity computation, CL-TSim does not require a decoder and step-by-step reconstruction, thus improving the training efficiency significantly. Moreover, considering the non-uniform sampling rate and noisy points in trajectories, we adopt two type of augmentations, i.e., point dowm-sampling and point distorting, to enhance the robustness of the proposed model. Extensive experiments are conducted on two widely-used real-world datasets, i.e., Porto and ChengDu, which demonstrate the superior effectiveness and efficiency of the proposed model.
TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis

Time series anomaly detection is a challenging problem due to the complex temporal dependencies and the limited label data. Although some algorithms including both traditional and deep models have been proposed, most of them mainly focus on time-domain modeling, and do not fully utilize the information in the frequency domain of the time series data. In this paper, we propose a Time-Frequency analysis based time series Anomaly Detection model, or TFAD for short, to exploit both time and frequency domains for performance improvement. Besides, we incorporate time series decomposition and data augmentation mechanisms in the designed time-frequency architecture to further boost the abilities of performance and interpretability. Empirical studies on widely used benchmark datasets show that our approach obtains state-of-the-art performance in univariate and multivariate time series anomaly detection tasks.
Extracting Drug-drug Interactions from Biomedical Texts using Knowledge Graph Embeddings and Multi-focal Loss

The field of Drug-drug interaction (DDI) aims to detect descriptions of interactions between drugs from biomedical texts. Currently, researchers have extracted DDIs using pre-trained language models such as BERT, which often misclassify two kinds of DDI types, "Effect" and "Int", on the DDIExtraction 2013 corpus because of highly similar expressions. The use of knowledge graphs can alleviate this problem by incorporating different relationships for each, thus allowing them to be distinguished. Thus, we propose a novel framework to integrate the neural network with a knowledge graph, where the features from these components are complementary. Specifically, we take text features at different levels into account in the neural network part. This is done by firstly obtaining a word-level position feature using PubMedBERT together with a convolution neural network, secondly, getting a phrase-level key path feature using a dependency parsing tree, thirdly, using PubMedBERT with an attention mechanism to obtain a sentence-level language feature, and finally, fusing these three kinds of representation into a synthesized feature. We also extract a knowledge feature from a drug knowledge graph which takes just a few minutes to construct, then concatenate the synthesized feature with the knowledge feature, feed the result into a multi-layer perceptron and obtain the result by a softmax classifier. In order to achieve a good integration of the synthesized feature and the knowledge feature, we train the model using a novel multifocal loss function, KGE-MFL, which is based on a knowledge graph embedding. Finally we attain state-of-the-art results on the DDIExtraction 2013 dataset (micro F-score 86.24%) and on the ChemProt dataset (micro F-score 77.75%), which proves our framework to be effective for biomedical relation extraction tasks. In particular, we fill the performance gap (more than 5.57%) between methods that rely on and do not rely on knowledge graph embedding on the DDIExtraction 2013 corpus, when predicting the "Int" type. The implementation code is available at https://github.com/NWU-IPMI/DDIE-KGE-MFL.
Probing the Robustness of Pre-trained Language Models for Entity Matching

The paradigm of fine-tuning Pre-trained Language Models (PLMs) has been successful in Entity Matching (EM). Despite their remarkable performance, PLMs exhibit tendency to learn spurious correlations from training data. In this work, we aim at investigating whether PLM-based entity matching models can be trusted in real-world applications where data distribution is different from that of training. To this end, we design an evaluation benchmark to assess the robustness of EM models to facilitate their deployment in the real-world settings. Our assessments reveal that data imbalance in the training data is a key problem for robustness. We also find that data augmentation alone is not sufficient to make a model robust. As a remedy, we prescribe simple modifications that can improve the robustness of PLM-based EM models. Our experiments show that while yielding superior results for in-domain generalization, our proposed model significantly improves the model robustness, compared to state-of-the-art EM models.
PEMP: Leveraging Physics Properties to Enhance Molecular Property Prediction

Molecular property prediction is essential for drug discovery. In recent years, deep learning methods have been introduced to this area and achieved state-of-the-art performances. However, most of existing methods ignore the intrinsic relations between molecular properties which can be utilized to improve the performances of corresponding prediction tasks. In this paper, we propose a new approach, namely Physics properties Enhanced Molecular Property prediction (PEMP), to utilize relations between molecular properties revealed by previous physics theory and physical chemistry studies. Specifically, we enhance the training of the chemical and physiological property predictors with related physics property prediction tasks. We design two different methods for PEMP, respectively based on multi-task learning and transfer learning. Both methods include a model-agnostic molecule representation module and a property prediction module. In our implementation, we adopt both the state-of-the-art molecule embedding models under the supervised learning paradigm and the pretraining paradigm as the molecule representation module of PEMP, respectively. Experimental results on public benchmark MoleculeNet show that the proposed methods have the ability to outperform corresponding state-of-the-art models.
BLAZAR ANTI-SEQUENCE OF SPECTRAL VARIATION WITHIN INDIVIDUAL BLAZARS: CASES FOR MRK 501 AND 3C 279

The jet properties of Mrk 501 and 3C 279 are derived by fitting broadband spectral energy distributions (SEDs) with lepton models. The derived γb (the break Lorenz factor of the electron distribution) is 104–106 for Mrk 501 and 200 ∼ 600 for 3C 279. The magnetic field strength (B) of Mrk 501 is usually one order of magnitude lower than that of 3C 279, but their Doppler factors (δ) are comparable. A spectral variation feature where the peak luminosity is correlated with the peak frequency, which is opposite from the blazar sequence, is observed in the two sources. We find that (1) the peak luminosities of the two bumps in the SEDs for Mrk 501 depend on γb in both the observer and co-moving frames, but they are not correlated with B and δ and (2) the luminosity variation of 3C 279 is dominated by the external Compton (EC) peak and its peak luminosity is correlated with γb and δ, but anti-correlated with B. These results suggest that γb may govern the spectral variation of Mrk 501 and δ and B would be responsible for the spectral variation of 3C 279. The narrow distribution of γb and the correlation of γb and B in 3C 279 would be due to the cooling from the EC process and the strong magnetic field. Based on our brief discussion, we propose that this spectral variation feature may originate from the instability of the corona but not from the variation of the accretion rate as does the blazar sequence.
℘-MinHash Algorithm for Continuous Probability Measures: Theory and Application to Machine Learning

This paper studies the scale-invariant "probability Jaccard'' (ProbJ), noted as ℐ℘, which is another variant of weighted Jaccard similarity. The standard and commonly used Jaccard index is not invariant of data scaling. Thus, the probability Jaccard can be a potentially useful extension to probability distributions. Before our paper, the problem of hashing the ℐ℘ for continuous probability measures is an open problem, where rigorous definitions and analysis are still absent in literature. In our work, we solve this problem systematically and completely. Specifically, we formalize the definition of ℐ℘ in continuous measure space, and propose a general ℘-MinHash sampling algorithm which generates samples following any target distribution, and preserves ℐ℘ between two distributions by the hash collision. In addition, a refined early stopping rule is proposed under a practical boundedness assumption. We validate the theory through simulation and experiments, and demonstrate the application of our method in machine learning problems.