Reviewerly/exHarmony huggingface dataset:
GranCATs: Cross-Lingual Enhancement through Granularity-Specific Contrastive Adapters

Multilingual language models (MLLMs) have demonstrated remarkable success in various cross-lingual downstream tasks, facilitating the transfer of knowledge across numerous languages, whereas this transfer is not universally effective. Our study reveals that while existing MLLMs like mBERT can capturephrase-level alignments across the language families, they struggle to effectively capturesentence-level andparagraph-level alignments. To address this limitation, we propose GranCATs, Granularity-specific Contrastive AdapTers. We collect a new dataset that observes each sample at three distinct levels of granularity and employ contrastive learning as a pre-training task to train GranCATs on this dataset. Our objective is to enhance MLLMs' adaptation to a broader range of cross-lingual tasks by equipping them with improved capabilities to capture global information at different levels of granularity. Extensive experiments show that MLLMs with GranCATs yield significant performance advancements across various language tasks with different text granularities, including entity alignment, relation extraction, sentence classification and retrieval, and question-answering. These results validate the effectiveness of our proposed GranCATs in enhancing cross-lingual alignments across various text granularities and effectively transferring this knowledge to downstream tasks.
Incremental Graph Classification by Class Prototype Construction and Augmentation

Graph neural networks (GNNs) are prone to catastrophic forgetting of past experience in continuous learning scenarios. In this work, we propose a novel method for class-incremental graph learning (CGL) by class prototype construction and augmentation, which can effectively overcome catastrophic forgetting and requires no storage of exemplars (i.e., data-free). Concretely, on the one hand, we construct class prototypes in the embedding space that contain rich topological information of nodes or graphs to represent past data, which are then used for future learning. On the other hand, to boost the adaptability of the model to new classes, we employ class prototype augmentation (PA) to create virtual classes by combining current prototypes. Theoretically, we show that PA can promote the model's adaptation to new data and reduce the inconsistency of old prototypes in the embedding space, therefore further mitigate catastrophic forgetting. Extensive experiments on both node and graph classification datasets show that our method significantly outperforms the existing methods in reducing catastrophic forgetting, and beats the existing methods in most cases in terms of classification accuracy.
ContributionSum: Generating Disentangled Contributions for Scientific Papers

Contributions are essentially the core of every scientific research, highlighting their key values to the academic community. Systems that are capable of identifying the contributions from scientific papers precisely and organizing them into well-structured summaries can facilitate both text processing and human comprehension. In this paper, we present ContributionSum, a dataset consisting of 24K computer science papers with contributions explicitly listed by the authors, which are further classified into different contribution types based on a newly-proposed annotation scheme. In addition, we study the task of generating disentangled contributions that summarize the values of scientific papers into key points. We propose a fine-grained post-training strategy tailored to our task and leverage salient information of different contribution types in the papers. To assess the coherency and coverage of each contribution aspect, we perform summary-level and contribution-level evaluations for our task. Experimental results show that our method improves upon mainstream baselines.
Can Knowledge Graphs Simplify Text?

Knowledge Graph (KG)-to-Text Generation has seen recent improvements in generating fluent and informative sentences which describe a given KG. As KGs are widespread across multiple domains and contain important entity-relation information, and as text simplification aims to reduce the complexity of a text while preserving the meaning of the original text, we propose KGSimple, a novel approach to unsupervised text simplification which infuses KG-established techniques in order to construct a simplified KG path and generate a concise text which preserves the original input's meaning. Through an iterative and sampling KG-first approach, our model is capable of simplifying text when starting from a KG by learning to keep important information while harnessing KG-to-text generation to output fluent and descriptive sentences. We evaluate various settings of the KGSimple model on currently-available KG-to-text datasets, demonstrating its effectiveness compared to unsupervised text simplification models which start with a given complex text. Our code is available on GitHub.
Personalized Differentially Private Federated Learning without Exposing Privacy Budgets

The meteoric rise of cross-silo Federated Learning (FL) is due to its ability to mitigate data breaches during collaborative training. To further provide rigorous privacy protection with consideration of the varying privacy requirements across different clients, a privacy-enhanced line of work on personalized differentially private federated learning (PDP-FL) has been proposed. However, the existing solution for PDP-FL [20] assumes the raw privacy budgets of all clients should be collected by the server. These values are then directly utilized to improve the model utility via facilitating the privacy preferences partitioning (i.e., partitioning all clients into multiple privacy groups). It is however non-realistic because the raw privacy budgets can be quite informative and sensitive.
Tight-Sketch: A High-Performance Sketch for Heavy Item-Oriented Data Stream Mining with Limited Memory Size

Accurate and fast data stream mining is critical and fundamental to many tasks, including time series database handling, big data management and machine learning. Different heavy-based detection tasks, such as heavy hitter, heavy changer, persistent item and significant item detection, have drawn much attention from both the industry and academia. Unfortunately, due to the growing data stream speeds and limited memory (L1 cache) available for real-time processing, existing schemes face challenges in simultaneously achieving high detection accuracy, high memory efficiency, and fast update throughput, as we reveal. To tackle this conundrum, we propose a versatile and elegant sketch framework named Tight-Sketch, which supports a spectrum of heavy-based detection tasks. Considering that most items are cold (non-heavy/persistent/significant) in practice, we employ different eviction treatments for different types of items to discard these potentially cold ones as soon as possible, and offer more protection to those that are hot (heavy/persistent/significant). In addition, we propose an eviction method that follows a stochastic decay strategy, enabling Tight-Sketch to only bear small one-sided errors (no overestimation). We present a theoretical analysis of the error bounds and conduct extensive experiments on diverse detection tasks to demonstrate that Tight-Sketch significantly outperforms existing methods in terms of accuracy and update speed. Lastly, we accelerate Tight-Sketch's update throughput by up to 36% with Single Instruction Multiple Data (SIMD) instructions.
Highly-Optimized Forgetting for Creating Signature-Based Views of Ontologies

Uniform interpolation (UI) is a non-standard reasoning service that seeks to project an ontology down to its sub-signature --- given an ontology taking a certain signature, and a subset Σ of "relevant names'' of that signature, compute a new ontology, called a uniform interpolant, that uses only the relevant names while preserving the semantics of the relevant names in the uniform interpolant. UI is of great potential importance since it may be used in a variety of applications where suitable views of ontologies need to be computed. However, this potential can only be fully realized if a highly optimized method for computing such views exists. Previous research has shown that computing uniform interpolants of ELH-ontologies is a computationally extremely hard problem --- a finite uniform interpolant does not always exist for ELH, and if it exists, then there exists one of at most triple exponential size in terms of the original ontology, and that, in the worst case, no shorter interpolant exists. Despite the inherent difficulty of the problem, in this paper, we present a highly optimized forgetting method for computing uniform interpolants of ELH-ontologies, and show however that, with good reduction and inference strategies, such uniform interpolants can be efficiently computed. The method is an improvement of the one presented in our previous work. What sets it apart is its flexibility to treat concept names of different types differently, effectively cutting down on the inferences involved. This treatment is primarily driven by the polarities of the concept names within an ontology. A comprehensive evaluation with a prototypical implementation of the method shows >95% average success rates over two popular benchmark datasets and demonstrates a clear computational advantage over state-of-the-art systems.
Exploiting Sequential Music Preferences via Optimisation-Based Sequencing

Users in music streaming platforms typically consume tracks sequentially in sessions by interacting with personalised playlists. To satisfy users, music platforms usually rely on recommender systems that learn users' preferences over individual tracks and rank the tracks within each playlist according to the learned preferences. However, such rankings often do not fully exploit the sequential nature of the users' consumption, which may result in a lower within-a-session consumption. In this paper, we model the sequential within-a-session preferences of users and propose an optimisation-based sequencing approach that allows for optimally incorporating such preferences into the rankings. To this end, we rely on interaction data of a major music streaming service to identify two most common aspects of the users' sequential preferences: (1) Position-Aware preferences, and (2) Local-Sequential preferences. We propose a sequencing model that can leverage each of these aspects optimally to maximise the expected total consumption from the session. We further perform an extensive offline and off-policy evaluation of our model, and carry out a large scale online randomised control trial with 7M users across 80 countries. Our findings confirm that we can effectively incorporate sequential preferences of users into our sequencer to make users complete more and skip less tracks within their listening sessions.
Extracting Methodology Components from AI Research Papers: A Data-driven Factored Sequence Labeling Approach

Extraction of methodology component names from scientific articles is a challenging task due to the diversified contexts around the occurrences of these entities, and the different levels of granularity and containment relationships exhibited by these entities. We hypothesize that standard sequence labeling approaches may not adequately model the dependence of methodology name mentions with their contexts, due to the problems of their large, fast evolving, and domain-specific vocabulary. As a solution, we propose a factored approach, where the mention-context dependencies are represented in a more fine-grained manner, thus allowing the model parameters to better adjust to the different characteristic patterns inherent within the data. In particular, we experiment with two variants of this factored approach - one that uses the per-entity category information derived from an ontology, and the other that makes use of the topology of the sentence embedding space to infer a category for each entity constituting that sentence. We demonstrate that both these factored variants of SciBERT outperform their non-factored counterpart, a state-of-the-art model for scientific concept extraction.
Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph

Knowledge graphs (KGs) are commonly used as side information to enhance collaborative signals and improve recommendation quality. In the context of knowledge-aware recommendation (KGR), graph neural networks (GNNs) have emerged as promising solutions for modeling factual and semantic information in KGs. However, the long-tail distribution of entities leads to sparsity in supervision signals, which weakens the quality of item representation when utilizing KG enhancement. Additionally, the binary relation representation of KGs simplifies hyper-relational facts, making it challenging to model complex real-world information. Furthermore, the over-smoothing phenomenon results in indistinguishable representations and information loss.
MC-DRE: Multi-Aspect Cross Integration for Drug Event/Entity Extraction

Extracting meaningful drug-related information chunks, such as adverse drug events (ADE), is crucial for preventing morbidity and saving many lives. Most ADEs are reported via an unstructured conversation with the medical context, so applying a general entity recognition approach is not sufficient enough. In this paper, we propose a new multi-aspect cross-integration framework for drug entity/event detection by capturing and aligning different context/language/knowledge properties from drug-related documents. We first construct multi-aspect encoders to describe semantic, syntactic, and medical document contextual information by conducting those slot tagging tasks, main drug entity/event detection, part-of-speech tagging, and general medical named entity recognition. Then, each encoder conducts cross-integration with other contextual information in three ways: the key-value cross, attention cross, and feedforward cross, so the multi-encoders are integrated in depth. Our model outperforms all SOTA on two widely used tasks, flat entity detection and discontinuous event extraction.
Quantum evolution speed and its limit in the driven double‐well system

Abstract We investigate quantum evolution speed in the driven double‐well system using the entangled trajectory molecular dynamics method. We emphasize not only the evolution speed of the quantum state but also its limit according to different definitions. The Wasserstein 1‐distance is used to quantify the distance between distinguishable quantum states in the phase space, the quantum speed limit based on the geometry has been shown to be the strictest one. The single trajectory's contribution to the quantum speed limit is discussed, which is related to both the time evolution of the trajectory and its position in the total Wigner function. The resonance and chaos strongly enhance the evolution speed and its limit in the driven double‐well system. The resonance effect makes a large proportion of representative points pass through the well as a whole, nevertheless, the chaos makes the Wigner function disperse in the phase space.
Closed-form Machine Unlearning for Matrix Factorization

Matrix factorization (MF) is a fundamental model in data mining and machine learning, which finds wide applications in diverse application areas, including recommendation systems with user-item rating matrices, phenotype extraction from electronic health records, and spatial-temporal data analysis for check-in records. The "right to be forgotten" has become an indispensable privacy consideration due to the widely enforced data protection regulations, which allow personal users having contributed their data for model training to revoke their data through a data deletion request. Consequently, it gives rise to the emerging task of machine unlearning for the MF model, which removes the influence of the matrix rows/columns from the trained MF factors upon receiving the deletion requests from the data owners of these rows/columns. The central goal is to effectively remove the influence of the rows/columns to be forgotten, while avoiding the computationally prohibitive baseline approach of retraining from scratch. Existing machine unlearning methods are either designed for single-variable models and not compatible with MF that has two factors as coupled model variables, or require alternative updates that are not efficient enough. In this paper, we propose a closed-form machine unlearning method. In particular, we explicitly capture the implicit dependency between the two factors, which yields the total Hessian-based Newton step as the closed-form unlearning update. In addition, we further introduce a series of efficiency-enhancement strategies by exploiting the structural properties of the total Hessian. Extensive experiments on five real-world datasets from three application areas as well as synthetic datasets validate the efficiency, effectiveness, and utility of the proposed method.
CORD: A Three-Stage Coarse-to-Fine Framework for Relation Detection in Knowledge Base Question Answering

As a fundamental subtask of Knowledge Base Question Answering (KBQA), Relation Detection (KBQA-RD) plays a crucial role to detect the KB relations between entities or variables in natural language questions. It remains, however, a challenging task, particularly for significant large-scale relations and in the presence of easily confused relations. Recent state-of-the-art methods not only struggle with such scenarios, but often take into account only one facet and fail to incorporate the subtle discrepancy among the relations. In this paper, we propose a simple and efficient three-stage framework to exploit the coarse-to-fine paradigm. Specifically, we employ a natural clustering over all KB relations and perform a coarse-to-fine relation recognition process based on the relation clustering. In this way, our framework (i.e., CORD) refines the detection of relations, so as to scale well with large-scale relations. Experiments on both single-relation (i.e., SimpleQuestions (SQ)) and multi-relation (i.e., WebQSP (WQ)) benchmarks show that CORD not only achieves the outstanding relation detection performance in KBQA-RD subtask; but more importantly, further improves the accuracy of KBQA systems.
A Joint Training-Calibration Framework for Test-Time Personalization with Label Shift in Federated Learning

The data heterogeneity has been a challenging issue in federated learning in both training and inference stages, which motivates a variety of approaches to learn either personalized models for participating clients or test-time adaptations for unseen clients. One such approach is employing a shared feature representation and a customized classifier head for each client. However, previous works either neglect the global head with rich knowledge or assume the new clients have enough labeled data, which significantly limit their broader practicality. In this work, we propose a lightweight framework to tackle with the label shift issue during the model deployment by test priors estimation and model prediction calibration. We also demonstrate the importance of training a balanced global model in FL so as to guarantee the general effectiveness of prior estimation approaches. Evaluation results on benchmark datasets demonstrate the superiority of our framework for model adaptation in unseen clients with unknown label shifts.
Geometric Matrix Completion via Sylvester Multi-Graph Neural Network

Despite the success of the Sylvester equation empowered methods on various graph mining applications, such as semi-supervised label learning and network alignment, there also exists several limitations. The Sylvester equation's inability of modeling non-linear relations and the inflexibility of tuning towards different tasks restrict its performance. In this paper, we propose an end-to-end neural framework, SYMGNN, which consists of a multi-network neural aggregation module and a prior multi-network association incorporation learning module. The proposed framework inherits the key ideas of the Sylvester equation, and meanwhile generalizes it to overcome aforementioned limitations. Empirical evaluations on real-world datasets show that the instantiations of SYMGNN overall outperform the baselines in geometric matrix completion task, and its low-rank instantiation could further reduce the memory consumption by 16.98% on average.
Attention Calibration for Transformer-based Sequential Recommendation

Transformer-based sequential recommendation (SR) has been booming in recent years, with the self-attention mechanism as its key component. Self-attention has been widely believed to be able to effectively select those informative and relevant items from a sequence of interacted items for next-item prediction via learning larger attention weights for these items. However, this may not always be true in reality. Our empirical analysis of some representative Transformer-based SR models reveals that it is not uncommon for large attention weights to be assigned to less relevant items, which can result in inaccurate recommendations. Through further in-depth analysis, we find two factors that may contribute to such inaccurate assignment of attention weights:sub-optimal position encoding andnoisy input. To this end, in this paper, we aim to address this significant yet challenging gap in existing works. To be specific, we propose a simple yet effective framework called Attention Calibration for Transformer-based Sequential Recommendation (AC-TSR). In AC-TSR, a novel spatial calibrator and adversarial calibrator are designed respectively to directly calibrates those incorrectly assigned attention weights. The former is devised to explicitly capture the spatial relationships (i.e., order and distance) among items for more precise calculation of attention weights. The latter aims to redistribute the attention weights based on each item's contribution to the next-item prediction. AC-TSR is readily adaptable and can be seamlessly integrated into various existing transformer-based SR models. Extensive experimental results on four benchmark real-world datasets demonstrate the superiority of our proposed AC-TSR via significant recommendation performance enhancements. The source code is available at https://github.com/AIM-SE/AC-TSR.
Rule-based Knowledge Graph Completion with Canonical Models

Rule-based approaches have proven to be an efficient and explainable method for knowledge base completion. Their predictive quality is on par with classic knowledge graph embedding models such as TransE or ComplEx, however, they cannot achieve the results of neural models proposed recently. The performance of a rule-based approach depends crucially on the solution of the rule aggregation problem, which is concerned with the computation of a score for a prediction that is generated by several rules. Within this paper, we propose a supervised approach to learn a reweighted confidence value for each rule to get an optimal explanation for the training set given a specific aggregation function. In particular, we apply our approach to two aggregation functions: We learn weights for a noisy-or multiplication and apply logistic regression, which computes the score of a prediction as a sum of these weights. Due to the simplicity of both models the final score is fully explainable. Our experimental results show that we can significantly improve the predictive quality of a rule-based approach. We compare our method with current state-of-the-art latent models that lack explainability, and achieve promising results.