Miracle-dz/UniFAR-FTUs huggingface dataset:
Minimizing Spectral Radius of Non-Backtracking Matrix by Edge Removal

The spectral radius of the non-backtracking matrix for an undirected graph plays an important role in various dynamic processes running on the graph. For example, its reciprocal provides an excellent approximation of epidemic and edge percolation thresholds. In this paper, we study the problem of minimizing the spectral radius of the non-backtracking matrix of a graph with n nodes and m edges, by deleting k selected edges. We show that the objective function of this combinatorial optimization problem is not submodular, although it is monotone. Since any straightforward approach to solving the optimization problem is computationally infeasible, we present an effective, scalable approximation algorithm with complexity O (n+km). Extensive experiment results for a large set of real-world networks verify the effectiveness and efficiency of our algorithm, and demonstrate that our algorithm outperforms several baseline schemes.
Explore, Filter and Distill: Distilled Reinforcement Learning in Recommendation

Reinforcement learning (RL) has been verified in real-world list-wise recommendation. However, RL-based recommendation suffers from huge memory and computation costs due to its large-scale models. Knowledge distillation (KD) is an effective approach for model compression widely used in practice. However, RL-based models strongly rely on sufficient explorations on the enormous user-item space due to the data sparsity issue, which multiplies the challenges of KD with RL models. What the teacher should teach and how much the student should learn from each lesson need to be carefully designed. In this work, we propose a novel Distilled reinforcement learning framework for recommendation (DRL-Rec), which aims to improve both effectiveness and efficiency in list-wise recommendation. Specifically, we propose an Exploring and filtering module before the distillation, which decides what lessons the teacher should teach from both teachers' and students' aspects. We also conduct a Confidence-guided distillation at both output and intermediate levels with a list-wise KL divergence loss and a Hint loss, which aims to understand how much the student should learn for each lesson. We achieve significant improvements on both offline and online evaluations in a well-known recommendation system. DRL-Rec has been deployed on WeChat Top Stories for more than six months, affecting millions of users. The source codes are released in https://github.com/modriczhang/DRL-Rec.
Understanding the Property of Long Term Memory for the LSTM with Attention Mechanism

Recent trends of incorporating LSTM network with different attention mechanisms in time series forecasting have led researchers to consider the attention module as an essential component. While existing studies revealed the effectiveness of attention mechanism with some visualization experiments, the underlying rationale behind their outstanding performance on learning long-term dependencies remains hitherto obscure. In this paper, we aim to elaborate on this fundamental question by conducting a thorough investigation of the memory property for LSTM network with attention mechanism. We present a theoretical analysis of LSTM integrated with attention mechanism, and demonstrate that it is capable of generating an adaptive decay rate which dynamically controls the memory decay according to the obtained attention score. In particular, our theory shows that attention mechanism brings significantly slower decays than the exponential decay rate of a standard LSTM. Experimental results on four real-world time series datasets demonstrate the superiority of the attention mechanism for maintaining long-term memory when compared to the state-of-the-art methods, and further corroborate our theoretical analysis.
WG4Rec: Modeling Textual Content with Word Graph for News Recommendation

News recommendation plays an indispensable role in acquiring daily news for users. Previous studies make great efforts to model high-order feature interactions between users and items, where various neural models are applied (e.g., RNN, GNN). However, we find that seldom efforts are made to get better representations for news. Most previous methods simply adopt pre-trained word embeddings to represent news and also suffer from cold-start users. In this work, we propose a new textual content representation method by building a word graph for recommendation, which is named WG4Rec. Three types of word associations are adopted in WG4Rec for content representation and user preference modeling, namely: 1)semantically-similar according to pre-trained word vectors, 2)co-occurrence in documents, and 3)co-click by users across documents. As extra information can be unified by adding nodes/edges to the word graph easily, WG4Rec is flexible to make use of cross-platform and cross-domain context for recommendation to alleviate the cold-start issue. To the best of our knowledge, it is the first attempt that using these relationships for news recommendation to better model textual content and adopt cross-platform information. Experimental results on two large-scale real-world datasets show that WG4Rec significantly outperforms state-of-the-art algorithms, especially for cold users in the online environment. Besides, WG4Rec achieves better performances when cross-platform information is utilized.
Online Advertising Incrementality Testing: Practical Lessons And Emerging Challenges

Online advertising has historically been approached as an ad-to-user matching problem within sophisticated optimization algorithms. As the research and ad-tech industries have progressed, advertisers have increasingly emphasized the causal effect estimation of their ads (incrementality) using controlled experiments (A/B testing). With low lift effects and sparse conversion, the development of incrementality testing platforms at scale suggests tremendous engineering challenges in measurement precision. Similarly, the correct interpretation of results addressing a business goal requires significant data science and experimentation research expertise. We propose a practical tutorial in the incrementality testing landscape, including: The business need; Literature solutions and industry practices; Designs in the development of testing platforms; The testing cycle, case studies, and recommendations. We provide first-hand lessons based on the development of such a platform in a major combined DSP and ad network, and after running several tests for up to two months each over recent years.
GAKG: A Multimodal Geoscience Academic Knowledge Graph

The research of geoscience plays a strong role in helping people gain a better understanding of the Earth. To effectively represent the knowledge (KG) from enormous geoscience research papers, knowledge graphs can be a powerful means. In the face of enormous geoscience research papers, knowledge graphs can be a powerful means to manage the relationships of data and integrate knowledge extracted from them. However, the existing geoscience KGs mainly focus on the external connection between concepts, whereas the potential abundant information contained in the internal multimodal data of the paper is largely overlooked for more fine-grained knowledge mining. To this end, we propose GAKG, a large-scale multimodal academic KG based on 1.12 million papers published in various geoscience-related journals. In addition to the bibliometrics elements, we also extracted the internal illustrations, tables, and text information of the articles, and dig out the knowledge entities of the papers and the era and spatial attributes of the articles, coupling multimodal academic data and features. Specifically, GAKG realizes knowledge entity extraction under our proposed Human-In-the-Loop framework, the novelty of which is to combine the techniques of machine reading and information retrieval with manual annotation of geoscientists in the loop. Considering the fact that literature of geoscience often contains more abundant illustrations and time scale information compared with that of other disciplines, we extract all the geographical information and era from the geoscience papers' text and illustrations, mapping papers to the atlas and chronology. Based on GAKG, we build several knowledge discovery benchmarks for finding geoscience communities and predicting potential links. GAKG and its services have been made publicly available and user-friendly.
HORNET: Enriching Pre-trained Language Representations with Heterogeneous Knowledge Sources

Knowledge-Enhanced Pre-trained Language Models (KEPLMs) improve the language understanding abilities of deep language models by leveraging the rich semantic knowledge from knowledge graphs, other than plain pre-training texts. However, previous efforts mostly use homogeneous knowledge (especially structured relation triples in knowledge graphs) to enhance the context-aware representations of entity mentions, whose performance may be limited by the coverage of knowledge graphs. Also, it is unclear whether these KEPLMs truly understand the injected semantic knowledge due to the "black-box'' training mechanism. In this paper, we propose a novel KEPLM named HORNET, which integrates Heterogeneous knowledge from various structured and unstructured sources into the Roberta NETwork and hence takes full advantage of both linguistic and factual knowledge simultaneously. Specifically, we design a hybrid attention heterogeneous graph convolution network (HaHGCN) to learn heterogeneous knowledge representations based on the structured relation triplets from knowledge graphs and the unstructured entity description texts. Meanwhile, we propose the explicit dual knowledge understanding tasks to help induce a more effective infusion of the heterogeneous knowledge, promoting our model for learning the complicated mappings from the knowledge graph embedding space to the deep context-aware embedding space and vice versa. Experiments show that our HORNET model outperforms various KEPLM baselines on knowledge-aware tasks including knowledge probing, entity typing and relation extraction. Our model also achieves substantial improvement over several GLUE benchmark datasets, compared to other KEPLMs.
CoPE: Modeling Continuous Propagation and Evolution on Interaction Graph

Human interactions with items are being constantly logged, which enables advanced representation learning and facilitates various tasks. Instead of generating static embeddings at the end of training, several temporal embedding methods were recently proposed to learn user and item embeddings as functions of time, where each entity has a trajectory of embedding vectors aiming to encode the full dynamics. However, these methods may not be optimal to encode the dynamical behaviors on the interaction graphs in that they can not generate "fully''-temporal embeddings and do not consider information propagation. In this paper, we tackle the issues and propose CoPE (Co ntinuous P ropagation and E volution). We use an ordinary differential equation based graph neural network to model information propagation and more sophisticated evolution patterns. We train CoPE on sequences of interactions with the help of meta-learning to ensure fast adaptation to the most recent interactions. We evaluate CoPE on three tasks and prove its effectiveness.
When Hardness Makes a Difference: Multi-Hop Knowledge Graph Reasoning over Few-Shot Relations

Knowledge graph (KG) reasoning is a significant method for KG completion. To enhance the explainability of KG reasoning, some studies adopt reinforcement learning (RL) to complete the multi-hop reasoning. However, RL-based reasoning methods are severely limited by few-shot relations (only contain few triplets). To tackle the problem, recent studies introduce meta-learning into RL-based methods to improve reasoning performance. However, the generalization abilities of their models are limited due to the problem of low reasoning accuracies over hard relations (e.g., language and title). To overcome this problem, we propose a novel model called THML (Two-level Hardness-aware Meta-reinforcement Learning). Specifically, the model contains the following two components: (1) A hardness-aware meta-reinforcement learning method is proposed to predict the missing element by training hardness-aware batches. (2) A two-level hardness-aware sampling is proposed to effectively generate new hardness-aware batches from relation level and relation-cluster level. The generalization ability of our model is significantly improved by repeating the process of these two components in an alternate way. The experimental results demonstrate that THML notably outperforms the state-of-the-art approaches in few-shot scenarios.
Dynamic Early Exit Scheduling for Deep Neural Network Inference through Contextual Bandits

Recent advances in Deep Neural Networks (DNNs) have dramatically improved the accuracy of DNN inference, but also introduce larger latency. In this paper, we investigate how to utilize early exit, a novel method that allows inference to exit at earlier exit points at the cost of an acceptable amount of accuracy. Scheduling the optimal exit point on a per-instance basis is challenging because the realized performance (i.e., confidence and latency) of each exit point is random and the statistics vary in different scenarios. Moreover, the performance has dependencies among the exit points, further complicating the problem. Therefore, the optimal exit scheduling decision cannot be known in advance but should be learned in an online fashion. To this end, we propose Dynamic Early Exit (DEE), a real-time online learning algorithm based on contextual bandit analysis. DEE observes the performance at each exit point as context and decides whether to exit or keep processing. Unlike standard contextual bandit analyses, the rewards of the decisions in our problem are temporally dependent. Furthermore, the performances of the earlier exit points are inevitably explored more compared to the later ones, which poses an unbalance exploration-exploitation trade-off. DEE addresses the aforementioned challenges, where its regret per inference asymptotically approaches zero. We compare DEE with four benchmark schemes in the real-world experiment. The experiment result shows that DEE can improve the overall performance by up to 98.1% compared to the best benchmark scheme.
Open Benchmarking for Click-Through Rate Prediction

Click-through rate (CTR) prediction is a critical task for many applications, as its accuracy has a direct impact on user experience and platform revenue. In recent years, CTR prediction has been widely studied in both academia and industry, resulting in a wide variety of CTR prediction models. Unfortunately, there is still a lack of standardized benchmarks and uniform evaluation protocols for CTR prediction research. This leads to non-reproducible or even inconsistent experimental results among existing studies, which largely limit the practical value and potential impact of their research. In this work, we aim to perform open benchmarking for CTR prediction and present a rigorous comparison of different models in a reproducible manner. To this end, we ran over 7,000 experiments for more than 12,000 GPU hours in total to re-evaluate 24 existing models on multiple dataset settings. Surprisingly, our experiments show that with sufficient hyper-parameter search and model tuning, many deep models have smaller differences than expected. The results also reveal that making real progress on the modeling of CTR prediction is indeed a very challenging research task. We believe that our benchmarking work could not only allow researchers to gauge the effectiveness of new models conveniently but also make them fairly compare with the state of the arts. We have publicly released the benchmarking tools, evaluation protocols, and experimental settings of our work to promote reproducible research in this field.
50 Ways to Bake a Cookie: Mapping the Landscape of Procedural Texts

The web is full of guidance on a wide variety of tasks, from changing the oil in your car to baking an apple pie. However, as content is created independently, a single task could have thousands of corresponding procedural texts. This makes it difficult for users to view the bigger picture and understand the multiple ways the task could be accomplished. In this work we propose an unsupervised learning approach for summarizing multiple procedural texts into an intuitive graph representation, allowing users to easily explore commonalities and differences. We demonstrate our approach on recipes, a prominent example of procedural texts. User studies show that our representation is intuitive and coherent and that it has the potential to help users with several sensemaking tasks, including adapting recipes for a novice cook and finding creative ways to spice up a dish.
Sparse Shield: Social Network Immunization vs. Harmful Speech

With the rise of social media users and the general shift of communication from traditional media to online platforms, the spread of harmful content (e.g., hate speech, misinformation, fake news) has been exacerbated. Harmful content in the form of hate speech causes a person distress or harm, having a negative impact on the individual mental health, with even more detrimental effects on the psychology of children and teenagers. In this paper, we propose an end-to-end solution with real-time capabilities to detect harmful content in real-time and mitigate its spread over the network. Our main contribution is Sparse Shield, a novel method that out-scales existing state-of-the-art methods for network immunization. We also propose a novel architecture for harmful speech mitigation that maximizes the impact of immunization. Our solution aims to identify a set of users for which to move harmful content at the bottom of the user feed, rather than censoring users. By immunizing certain network nodes in this manner, we minimize the negative impact on the network and minimize the interference with and limitation of individual freedoms: the information is not hidden but rather not as easy to reach without an explicit search. Our analysis is based on graphs built on real-world data collected from Twitter; these graphs reflect real user behavior. We perform extensive scalability experiments to prove the superiority of our method over existing state-of-the-art network immunization techniques. We also perform extensive experiments to showcase that Sparse Shield outperforms existing techniques on the task of harmful speech mitigation on a real-world dataset.
Multi-Property Molecular Optimization using an Integrated Poly-Cycle Architecture

Molecular lead optimization is an important task of drug discovery focusing on generating molecules similar to a drug candidate but with enhanced properties. Most prior works focused on optimizing a single property. However, in real settings, we wish to find molecules that satisfy multiple constraints, e.g., potency and safety. Simultaneously optimizing these constraints was shown to be difficult, mostly due to the lack of training examples satisfying all constraints. In this work, we present a novel approach for multi-property optimization. Unlike prior approaches, that require a large training set of pairs of a lead molecule and an enhanced molecule, our approach is unpaired. Our architecture learns a transformation for each property optimization separately, while constraining the latent embedding space between all transformations. This allows generating a molecule which optimizes multiple properties simultaneously. We present a novel adaptive loss which balances the separate transformations and stabilizes the optimization process. We evaluate our method on optimizing for two properties: dopamine receptor (DRD2) and drug likeness (QED), and show our method outperforms previous state-of-the-art, especially when training examples satisfying all constraints are sparse.
Cycle or Minkowski: Which is More Appropriate for Knowledge Graph Embedding?

Knowledge graph (KG) embedding aims to encode entities and relations into low-dimensional vector spaces, in turn, can support various machine learning models on KG related tasks with good performance. However, existing methods for knowledge graph embedding fail to consider the influence of the embedding space, which makes them still unsatisfactory in practical applications. In this study, we try to improve the expressiveness of the embedding space from the perspective of the metric. Specifically, we first point out the implications of Minkowski metric used in KG embedding and then make a quantitative analysis. To solve the limitations, we introduce a new metric, named Cycle metric, based on the oscillation property of the periodic function. Furthermore, we find that the function period has a significant influence on the expressiveness of the embedding space. Given a fully trained model, the smaller the period, the better the expressive ability. Finally, to validate the findings, we propose a new model, named CyclE by combining Cycle Metric and the popular KG embeddings models. Comprehensive experimental results show that Cycle is more appropriate than Minkowski for KG embedding.
SCI: Subspace Learning Based Counterfactual Inference for Individual Treatment Effect Estimation

Inferring causal effect from observational data has attracted much attention from various domains. Under the potential outcome framework, the estimation of counterfactuals is crucial for the investigation of causal effect at the individual level. Existing representation learning approaches focus on learning one balanced feature space, which ignores certain information predictive to the outcomes. To fully utilize the predictive information, we propose a Subspace learning based Counterfactual Inference (SCI) method to estimate causal effect at the individual level. Different from existing work, SCI learns both a common subspace, which preserves the information across all the treatment groups, and treatment-specific subspaces, which retain the information associated with each specific treatment. Learning from two kinds of subspaces helps SCI obtain better causal effect estimations than state-of-the-art methods, demonstrated by a series of experiments on synthetic and real-world datasets.
Hydrogen bond interactions of dopamine hydrochloride with urea

ABSTRACT The interactions of dopamine hydrochloride (DH) with urea were investigated by electrochemical cyclic voltammetry, density functional theory (DFT) and quantum theory of atoms in molecules (QTAIM) analysis. Changes of the oxidation/reduction peak potentials of DH in DH-urea-H2O systems indicated that the hydrogen bonds were formed between DH and urea. The DFT and QTAIM results confirmed that the hydrogen and oxygen atoms on phenolic hydroxyl groups, the hydrogen atoms on NH3+ and the aromatic ring of DH can form hydrogen bond with the oxygen and hydrogen atoms of urea. Moreover, the solution pH has an effect on the electrochemical behaviour of DH in the system.