Miracle-dz/UniFAR-FTUs huggingface dataset:
RGVisNet: A Hybrid Retrieval-Generation Neural Framework Towards Automatic Data Visualization Generation

Recent years have witnessed the burgeoning of data visualization (DV) systems in both the research and the industrial communities since they provide vivid and powerful tools to convey the insights behind the massive data. A necessary step to visualize data is through creating suitable specifications in some declarative visualization languages (DVLs, e.g., Vega-Lite, ECharts). Due to the steep learning curve of mastering DVLs, automatically generating DVs via natural language questions, or text-to-vis, has been proposed and received great attention. However, existing neural network-based text-to-vis models, such as Seq2Vis or ncNet, usually generate DVs from scratch, limiting their performance due to the complex nature of this problem. Inspired by how developers reuse previously validated source code snippets from code search engines or a large-scale codebase when they conduct software development, we provide a novel hybrid retrieval-generation framework named RGVisNet for text-to-vis. It retrieves the most relevant DV query candidate as a prototype from the DV query codebase, and then revises the prototype to generate the desired DV query. Specifically, the DV query retrieval model is a neural ranking model which employs a schema-aware encoder for the NL question, and a GNN-based DV query encoder to capture the structure information of a DV query. At the same time, the DV query revision model shares the same structure and parameters of the encoders, and employs a DV grammar-aware decoder to reuse the retrieved prototype. Experimental evaluation on the public NVBench dataset validates that RGVisNet can significantly outperform existing generative text-to-vis models such as ncNet, by up to 74.28% relative improvement in terms of overall accuracy. To the best of our knowledge, RGVisNet is the first framework that seamlessly integrates the retrieval- with the generative-based approach for the text-to-vis task.
A Nearly-Linear Time Algorithm for Minimizing Risk of Conflict in Social Networks

Concomitant with the tremendous prevalence of online social media platforms, the interactions among individuals are unprecedentedly enhanced. People are free to interact with acquaintances, express and exchange their own opinions through commenting, liking, retweeting on online social media, leading to resistance, controversy and other important phenomena over controversial social issues, which have been the subject of many recent works. In this paper, we study the problem of minimizing risk of conflict in social networks by modifying the initial opinions of a small number of nodes. We show that the objective function of the combinatorial optimization problem is monotone and supermodular. We then propose a naive greedy algorithm with a (1-1/e) approximation ratio that solves the problem in cubic time. To overcome the computation challenge for large networks, we further integrate several effective approximation strategies to provide a nearly linear time algorithm with a (1-1/e-ε) approximation ratio for any error parameter ε>0. Extensive experiments on various real-world datasets demonstrate both the efficiency and effectiveness of our algorithms. In particular, the fast one scales to large networks with more than two million nodes, and achieves up to 20x speed-up over the state-of-the-art algorithm.
Graph Neural Networks in Life Sciences: Opportunities and Solutions

Graphs (or networks) are ubiquitous representation in life sciences and medicine, from molecular interactions maps, signaling transduction pathways, to graphs of scientific knowledge and patient- disease-intervention relationships derived from population studies and/or real-world data, such as electronic health records and insurance claims. Recent advance in graph machine learning (ML) approaches such as graph neural networks (GNNs) has transformed a diverse set of problems relying on biomedical networks that traditionally depend on descriptive topological data analyses. Small- and macro- molecules that were not modeled as graphs also saw a bloom in GNN-based algorithms improving the state-of-the-art performance for learning their properties. Comparing to graph ML applications from other domains, life sciences offer many unique problems and nuances ranging from graph construction to graph- level, and bi-graph-level supervision tasks. The objective of this tutorial is twofold. First, it will provide a comprehensive overview of the types of biomedical graphs/networks, the underlying biological and medical problems, and the applications of graph ML algorithms for solving those problems. Second, it will showcase four concrete GNN solutions in life sciences with hands-on experience for the attendees. These hands-on sessions will cover: 1) training and fine-tuning GNN models for small-molecule property prediction on atomic graphs, 2) macro-molecule property and function prediction on residue graphs, 3) bi-graph based bind- ing affinity prediction for protein-ligand pairs, and 4) organizing and generating new knowledge for drug discovery and repurposing with knowledge graphs. This tutorial will also instruct the attendees to develop in two extensions of the software library Deep Graph Library (DGL), including DGL-lifesci and DGL-KE, so that they could jumpstart their own graph ML journey to advance life science research and development.
GBPNet: Universal Geometric Representation Learning on Protein Structures

Representation learning of protein 3D structures is challenging and essential for applications, e.g., computational protein design or protein engineering. Recently, geometric deep learning has achieved great success in non-Euclidean domains. Although protein can be represented as a graph naturally, it remains under-explored mainly due to the significant challenges in modeling the complex representations and capturing the inherent correlation in the 3D structure modeling. Several challenges include: 1) It is challenging to extract and preserve multi-level rotation and translation equivariant information during learning. 2) Difficulty in developing appropriate tools to effectively leverage the input spatial representations to capture complex geometries across the spatial dimension. 3) Difficulty in incorporating various geometric features and preserving the inherent structural relations. In this work, we introduce geometric bottleneck perceptron, and a general SO(3)-equivariant message passing neural network built on top of it for protein structure representation learning. The proposed geometric bottleneck perceptron can be incorporated into diverse network architecture backbones to process geometric data in different domains. This research shed new light on geometric deep learning in 3D structure studies. Empirically, we demonstrate the strength of our proposed approach on three core downstream tasks, where our model achieves significant improvements and outperforms existing benchmarks. The implementation is available at https://github.com/sarpaykent/GBPNet.
Applying Deep Learning Based Probabilistic Forecasting to Food Preparation Time for On-Demand Delivery Service

On-demand food delivery service has widely served people's daily demands worldwide, e.g., customers place over 40 million online orders in Meituan food delivery platform per day in Q3 of 2021. Predicting the food preparation time (FPT) of each order accurately is very significant for the courier and customer experience over the platform. However, there are two challenges, namely incomplete label and huge uncertainty in FPT data, to make the prediction of FPT in practice. In this paper, we apply probabilistic forecasting to FPT for the first time and propose a non-parametric method based on deep learning. Apart from the data with precise label of FPT, we make full use of the lower/upper bound of orders without precise label, during feature extraction and model construction. A number of categories of meaningful features are extracted based on the detailed data analysis to produce sharp probability distribution. For probabilistic forecasting, we propose S-QL and prove its relationship with S-CRPS for interval-censored data for the first time, which serves the quantile discretization of S-CRPS and optimization for the constructed neural network model. Extensive offline experiments over the large-scale real-world dataset, and online A/B test both demonstrate the effectiveness of our proposed method.
Selective Cross-City Transfer Learning for Traffic Prediction via Source City Region Re-Weighting

Deep learning models have been demonstrated powerful in modeling complex spatio-temporal data for traffic prediction. In practice, effective deep traffic prediction models rely on large-scale traffic data, which is not always available in real-world scenarios. To alleviate the data scarcity issue, a promising way is to use cross-city transfer learning methods to fine-tune well-trained models from source cities with abundant data. However, existing approaches overlook the divergence between source and target cities, and thus, the trained model from source cities may contain noise or even harmful source knowledge. To address the problem, we propose CrossTReS, a selective transfer learning framework for traffic prediction that adaptively re-weights source regions to assist target fine-tuning. As a general framework for fine-tuning-based cross-city transfer learning, CrossTReS consists of a feature network, a weighting network, and a prediction model. We train the feature network with node- and edge-level domain adaptation techniques to learn generalizable spatial features for both source and target cities. We further train the weighting network via source-target joint meta-learning such that source regions helpful to target fine-tuning are assigned high weights. Finally, the prediction model is selectively trained on the source city with the learned weights to initialize target fine-tuning. We evaluate CrossTReS using real-world taxi and bike data, where under the same settings, CrossTReS outperforms state-of-the-art baselines by up to 8%. Moreover, the learned region weights offer interpretable visualization.
Counteracting User Attention Bias in Music Streaming Recommendation via Reward Modification

In streaming media applications, like music Apps, songs are recommended in a continuous way in users' daily life. The recommended songs are played automatically although users may not pay any attention to them, posing a challenge of user attention bias in training recommendation models, i.e., the training instances contain a large number of false-positive labels (users' feedback). Existing approaches either directly use the auto-feedbacks or heuristically delete the potential false-positive labels. Both of the approaches lead to biased results because the false-positive labels cause the shift of training data distribution, hurting the accuracy of the recommendation models. In this paper, we propose a learning-based counterfactual approach to adjusting the user auto-feedbacks and learning the recommendation models using Neural Dueling Bandit algorithm, called NDB. Specifically, NDB maintains two neural networks: a user attention network for computing the importance weights that are used for modifying the original rewards, and another random network trained with dueling bandit for conducting online recommendations based on the modified rewards. Theoretical analysis showed that the modified rewards are statistically unbiased, and the learned bandit policy enjoys a sub-linear regret bound. Experimental results demonstrated that NDB can significantly outperform the state-of-the-art baselines.
Quantum Circuit for Calculating Mean Values Via Grover-like Algorithm

In this paper, we give a quantum circuit for calculating the mean value of a function $A(x^n)\in \mathbb{C}$, where $x^n\in \{0,1\}^n$. Known classical algorithms for calculating the mean value of a structureless function $A(x^n)$ take ${\cal O}(2^n)$ steps. Our quantum algorithm is based on a Grover-like algorithm and it takes ${\cal O}(\sqrt{2^n})$ steps. Our algorithm differs significantly from previously proposed quantum algorithms for calculating the mean value of a function via Grover's algorithm.
New Frontiers of Scientific Text Mining: Tasks, Data, and Tools

Exploring the vast amount of rapidly growing scientific text data is highly beneficial for real-world scientific discovery. However, scientific text mining is particularly challenging due to the lack of specialized domain knowledge in natural language context, complex sentence structures in scientific writing, and multi-modal representations of scientific knowledge. This tutorial presents a comprehensive overview of recent research and development on scientific text mining, focusing on the biomedical and chemistry domains. First, we introduce the motivation and unique challenges of scientific text mining. Then we discuss a set of methods that perform effective scientific information extraction, such as named entity recognition, relation extraction, and event extraction. We also introduce real-world applications such as textual evidence retrieval, scientific topic contrasting for drug discovery, and molecule representation learning for reaction prediction. Finally, we conclude our tutorial by demonstrating, on real-world datasets (COVID-19 and organic chemistry literature), how the information can be extracted and retrieved, and how they can assist further scientific discovery. We also discuss the emerging research problems and future directions for scientific text mining.
Addressing Unmeasured Confounder for Recommendation with Sensitivity Analysis

Recommender systems should answer the intervention question "if recommending an item to a user, what would the feedback be", calling for estimating the causal effect of a recommendation on user feedback. Generally, this requires blocking the effect of confounders that simultaneously affect the recommendation and feedback. To mitigate the confounding bias, a strategy is incorporating propensity into model learning. However, existing methods forgo possible unmeasured confounders (e.g., user financial status), which can result in biased propensities and hurt recommendation performance. This work combats the risk of unmeasured confounders in recommender systems. Towards this end, we propose Robust Deconfounder (RD) that accounts for the effect of unmeasured confounders on propensities, under the mild assumption that the effect is bounded. It estimates the bound with sensitivity analysis, learning a recommender model robust to unmeasured confounders within the bound by adversarial learning. However, pursuing robustness within a bound may restrict model accuracy. To avoid the trade-off between robustness and accuracy, we further propose Benchmarked RD (BRD) that incorporates a pre-trained model into the learning as the benchmark. Theoretical analyses prove the stronger robustness of our methods compared to existing propensity-based deconfounders, and also prove the no-harm property of BRD. Our methods are applicable to any propensity-based estimators, where we select three representative ones: IPS, Doubly Robust, and AutoDebias. We conduct experiments on three real-world datasets to demonstrate the effectiveness of our methods.
Electromagnetic waves on dilatonic star backgrounds

We study the propagation of classical electromagnetic waves on the simplest four-dimensional spherically symmetric metric with a (massless) dilaton background field. Solutions to the relevant equations are obtained perturbatively in a parameter which measures the strength of the dilaton field (hence parameterizes the departure from Schwarzschild geometry). The loss of energy from outgoing modes is estimated as a back-scattering process against the dilaton background, which would affect the luminosity of stars with a dilaton field. The radiation emitted by a freely falling point-like source on such a background is also studied by analytical and numerical methods.
PARSRec: Explainable Personalized Attention-fused Recurrent Sequential Recommendation Using Session Partial Actions

The emerging meta- and multi-verse landscape is yet another step towards the more prevalent use of already ubiquitous online markets. In such markets, recommender systems play critical roles by offering items of interest to the users, thereby narrowing down a vast search space that comprises hundreds of thousands of products. Recommender systems are usually designed to learn common user behaviors and rely on them for inference. This approach, while effective, is oblivious to subtle idiosyncrasies that differentiate humans from each other. Focusing on this observation, we propose an architecture that relies on common patterns as well as individual behaviors to tailor its recommendations for each person. Simulations under a controlled environment show that our proposed model learns interpretable personalized user behaviors. Our empirical results on Nielsen Consumer Panel dataset indicate that the proposed approach achieves up to 27.9% performance improvement compared to the state-of-the-art.
Activity Trajectory Generation via Modeling Spatiotemporal Dynamics

Human daily activities, such as working, eating out, and traveling, play an essential role in contact tracing and modeling the diffusion patterns of the COVID-19 pandemic. However, individual-level activity data collected from real scenarios are highly limited due to privacy issues and commercial concerns. In this paper, we present a novel framework based on generative adversarial imitation learning, to generate artificial activity trajectories that retain both the fidelity and utility of the real-world data. To tackle the inherent randomness and sparsity of irregular-sampled activities, we innovatively capture the spatiotemporal dynamics underlying trajectories by leveraging neural differential equations. We incorporate the dynamics of continuous flow between consecutive activities and instantaneous updates at observed activity points in temporal evolution and spatial transformation. Extensive experiments on two real-world datasets show that our proposed framework achieves superior performance over state-of-the-art baselines in terms of improving the data fidelity and data utility in facilitating practical applications. Moreover, we apply the synthetic data to model the COVID-19 spreading, and it achieves better performance by reducing the simulation MAPE over the baseline by more than 50%. The source code is available online: https://github.com/tsinghua-fib-lab/Activity-Trajectory-Generation.
Interpreting Trajectories from Multiple Views: A Hierarchical Self-Attention Network for Estimating the Time of Arrival

Estimating the time of arrival is a crucial task in intelligent transportation systems. Although considerable efforts have been made to solve this problem, most of them decompose a trajectory into several segments and then compute the travel time by integrating the attributes from all segments. The segment view, though being able to depict the local traffic conditions straightforwardly, is insufficient to embody the intrinsic structure of trajectories on the road network. To overcome the limitation, this study proposes multi-view trajectory representation that comprehensively interprets a trajectory from the segment-, link-, and intersection-views. To fulfill the purpose, we design a hierarchical self-attention network (HierETA) that accurately models the local traffic conditions and the underlying trajectory structure. Specifically, a segment encoder is developed to capture the spatio-temporal dependencies at a fine granularity, within which an adaptive self-attention module is designed to boost performance. Further, a joint link-intersection encoder is developed to characterize the natural trajectory structure consisting of alternatively arranged links and intersections. Afterward, a hierarchy-aware attention decoder is designed to realize a tradeoff between the multi-view spatio-temporal features. The hierarchical encoders and the attentive decoder are simultaneously learned to achieve an overall optimality. Experiments on two large-scale practical datasets show the superiority of HierETA over the state-of-the-arts.
Uncovering the Heterogeneous Effects of Preference Diversity on User Activeness: A Dynamic Mixture Model

Preference diversity arouses much research attention in recent years, as it is believed to be closely related to many profound problems such as user activeness in social media or recommendation systems. However, due to the lack of large-scale data with comprehensive user behavior log and accurate content labels, the real quantitative effect of preference diversity on user activeness is still largely unknown. This paper studies the heterogeneous effect of preference diversity on user activeness in social media. We examine large-scale real-world datasets collected from two of the most popular video-sharing social platforms in China, including the behavior logs of more than 787 thousand users and 1.95 million videos, with accurate content category information. We investigate the distribution and evolution of preference diversity, and find rich heterogeneity in the effect of preference diversity on the dynamic activeness. Furthermore, we discover the divergence of preference diversity mechanisms for the same user under different usage scenarios, such as active (where users actively seek information) and passive (where users passively receive information) modes. Unlike existing qualitative studies, we propose a universal mixture model with the capability of accurately fitting dynamic activeness curves while reflecting the heterogeneous patterns of preference diversity. To our best knowledge, this is the first quantitative model that incorporates the effect of preference diversity on user activeness. With the modeling parameters, we are able to make accurate churn and activeness predictions and provide decision support for increasing user activity through the intervention of diversity. Our findings and model comprehensively reveal the significance of preference diversity and provide potential implications for the design of future recommendation systems and social media.
Multi-Agent Graph Convolutional Reinforcement Learning for Dynamic Electric Vehicle Charging Pricing

Electric Vehicles (EVs) have been emerging as a promising low-carbon transport target. While a large number of public charging stations are available, the use of these stations is often imbalanced, causing many problems to Charging Station Operators (CSOs). To this end, in this paper, we propose a Multi-Agent Graph Convolutional Reinforcement Learning (MAGC) framework to enable CSOs to achieve more effective use of these stations by providing dynamic pricing for each of the continuously arising charging requests with optimizing multiple long-term commercial goals. Specifically, we first formulate this charging station request-specific dynamic pricing problem as a mixed competitive-cooperative multi-agent reinforcement learning task, where each charging station is regarded as an agent. Moreover, by modeling the whole charging market as a dynamic heterogeneous graph, we devise a multi-view heterogeneous graph attention networks to integrate complex interplay between agents induced by their diversified relationships. Then, we propose a shared meta generator to generate individual customized dynamic pricing policies for large-scale yet diverse agents based on the extracted meta characteristics. Finally, we design a contrastive heterogeneous graph pooling representation module to learn a condensed yet effective state action representation to facilitate policy learning of large-scale agents. Extensive experiments on two real-world datasets demonstrate the effectiveness of MAGC and empirically show that the overall use of stations can be improved if all the charging stations in a charging market embrace our dynamic pricing policy.