bluuebunny/crossref_metadata_2025 huggingface dataset:
RR-PU: A Synergistic Two-Stage Positive and Unlabeled Learning Framework for Robust Tax Evasion Detection
Authors: Cao Shuzhi , Ruan Jianfei , Dong Bo , Shi Bin , Zheng Qinghua
Published: March 2024

Tax evasion, an unlawful practice in which taxpayers deliberately conceal information to avoid paying tax liabilities, poses significant challenges for tax authorities. Effective tax evasion detection is critical for assisting tax authorities in mitigating tax revenue loss. Recently, machine-learning-based methods, particularly those employing positive and unlabeled (PU) learning, have been adopted for tax evasion detection, achieving notable success. However, these methods exhibit two major practical limitations. First, their success heavily relies on the strong assumption that the label frequency (the fraction of identified taxpayers among tax evaders) is known in advance. Second, although some methods attempt to estimate label frequency using approaches like Mixture Proportion Estimation (MPE) without making any assumptions, they subsequently construct a classifier based on the error-prone label frequency obtained from the previous estimation. This two-stage approach may not be optimal, as it neglects error accumulation in classifier training resulting from the estimation bias in the first stage. To address these limitations, we propose a novel PU learning-based tax evasion detection framework called RR-PU, which can revise the bias in a two-stage synergistic manner. Specifically, RR-PU refines the label frequency initialization by leveraging a regrouping technique to fortify the MPE perspective. Subsequently, we integrate a trainable slack variable to fine-tune the initial label frequency, concurrently optimizing this variable and the classifier to eliminate latent bias in the initial stage. Experimental results on three real-world tax datasets demonstrate that RR-PU outperforms state-of-the-art methods in tax evasion detection tasks.
Distributional Off-Policy Evaluation for Slate Recommendations
Authors: Chaudhari Shreyas , Arbour David , Theocharous Georgios , Vlassis Nikos
Published: March 2024

Recommendation strategies are typically evaluated by using previously logged data, employing off-policy evaluation methods to estimate their expected performance. However, for strategies that present users with slates of multiple items, the resulting combinatorial action space renders many of these methods impractical. Prior work has developed estimators that leverage the structure in slates to estimate the expected off-policy performance, but the estimation of the entire performance distribution remains elusive. Estimating the complete distribution allows for a more comprehensive evaluation of recommendation strategies, particularly along the axes of risk and fairness that employ metrics computable from the distribution. In this paper, we propose an estimator for the complete off-policy performance distribution for slates and establish conditions under which the estimator is unbiased and consistent. This builds upon prior work on off-policy evaluation for slates and off-policy distribution estimation in reinforcement learning. We validate the efficacy of our method empirically on synthetic data as well as on a slate recommendation simulator constructed from real-world data (MovieLens-20M). Our results show a significant reduction in estimation variance and improved sample efficiency over prior work across a range of slate structures.
Uncertainty-Aware Yield Prediction with Multimodal Molecular Features
Authors: Chen Jiayuan , Guo Kehan , Liu Zhen , Isayev Olexandr , Zhang Xiangliang
Published: March 2024

Predicting chemical reaction yields is pivotal for efficient chemical synthesis, an area that focuses on the creation of novel compounds for diverse uses.
Yield prediction demands accurate representations of reactions for forecasting practical transformation rates. Yet, the uncertainty issues broadcasting in real-world situations prohibit current models to excel in this task owing to the high sensitivity of yield activities and the uncertainty in yield measurements. Existing models often utilize single-modal feature representations, such as molecular fingerprints, SMILES sequences, or molecular graphs, which is not sufficient to capture the complex interactions and dynamic behavior of molecules in reactions. In this paper, we present an advanced Uncertainty-Aware Multimodal model (UAM) to tackle these challenges. Our approach seamlessly integrates data sources from multiple modalities by encompassing sequence representations, molecular graphs, and expert-defined chemical reaction features for a comprehensive representation of reactions. Additionally, we address both the model and data-based uncertainty, refining the model's predictive capability. Extensive experiments on three datasets, including two high throughput experiment (HTE) datasets and one chemist-constructed Amide coupling reaction dataset, demonstrate that UAM outperforms the state-of-the-art methods. The code and used datasets are available at https://github.com/jychen229/Multimodal-reaction-yield-prediction.
Dense Projection for Anomaly Detection
Authors: Fu Dazhi , Zhang Zhao , Fan Jicong
Published: March 2024

This work presents a novel method called dense projection for unsupervised anomaly detection (DPAD). The main idea is maximizing the local density of (normal) training data and then determining whether a test data is anomalous or not by evaluating its density. Specifically, DPAD uses a deep neural network to learn locally dense representations of normal data. Since density estimation is computationally expensive, we minimize the local distances of the representations in an iteratively reweighting manner, where the weights are updated adaptively and the parameters are regularized to avoid model collapse (all representations collapse to a single point). Compared with many state-of-the-art methods of anomaly detection, our DPAD does not rely on any assumption about the distribution or spatial structure of the normal data and representations. Moreover, we provide theoretical guarantees for the effectiveness of DPAD. The experiments show that our method DPAD is effective not only in traditional one-class classification problems but also in scenarios with complex normal data composed of multiple classes.
D3: A Methodological Exploration of Domain Division, Modeling, and Balance in Multi-Domain Recommendations
Authors: Jia Pengyue , Wang Yichao , Lin Shanru , Li Xiaopeng , Zhao Xiangyu , Guo Huifeng , Tang Ruiming
Published: March 2024

To enhance the efficacy of multi-scenario services in industrial recommendation systems, the emergence of multi-domain recommendation has become prominent, which entails simultaneous modeling of all domains through a unified model, effectively capturing commonalities and differences among them. However, current methods rely on manual domain partitioning, which overlook the intricate domain relationships and the heterogeneity of different domains during joint optimization, hindering the integration of domain commonalities and differences. To address these challenges, this paper proposes a universal and flexible framework D3 aimed at optimizing the multi-domain recommendation pipeline from three key aspects. Firstly, an attention-based domain adaptation module is introduced to automatically identify and incorporate domain-sensitive features during training. Secondly, we propose a fusion gate module that enables the seamless integration of commonalities and diversities among domains, allowing for implicit characterization of intricate domain relationships. Lastly, we tackle the issue of joint optimization by deriving loss weights from two complementary viewpoints: domain complexity and domain specificity, alleviating inconsistencies among different domains during the training phase. Experiments on three public datasets demonstrate the effectiveness and superiority of our proposed framework. In addition, D3 has been implemented on a real-life, high-traffic internet platform catering to millions of users daily.
MESED: A Multi-Modal Entity Set Expansion Dataset with Fine-Grained Semantic Classes and Hard Negative Entities
Authors: Li Yangning , Lu Tingwei , Zheng Hai-Tao , Li Yinghui , Huang Shulin , Yu Tianyu , Yuan Jun , Zhang Rui
Published: March 2024

The Entity Set Expansion (ESE) task aims to expand a handful of seed entities with new entities belonging to the same semantic class. Conventional ESE methods are based on mono-modality (i.e., literal modality), which struggle to deal with complex entities in the real world such as (1) Negative entities with fine-grained semantic differences. (2) Synonymous entities. (3) Polysemous entities. (4) Long-tailed entities. These challenges prompt us to propose novel Multi-modal Entity Set Expansion (MESE), where models integrate information from multiple modalities to represent entities. Intuitively, the benefits of multi-modal information for ESE are threefold: (1) Different modalities can provide complementary information. (2) Multi-modal information provides a unified signal via common visual properties for the same semantic class or entity. (3) Multi-modal information offers robust alignment signals for synonymous entities. To assess model performance in MESE, we constructed the MESED dataset which is the first multi-modal dataset for ESE with large-scale and elaborate manual calibration. A powerful multi-modal model MultiExpan is proposed which is pre-trained on four multimodal pre-training tasks. The extensive experiments and analyses on MESED demonstrate the high quality of the dataset and the effectiveness of our MultiExpan, as well as pointing the direction for future research. The benchmark and code are public at https://github.com/THUKElab/MESED.
Learning to Rank in Generative Retrieval
Authors: Li Yongqi , Yang Nan , Wang Liang , Wei Furu , Li Wenjie
Published: March 2024

Generative retrieval stands out as a promising new paradigm in text retrieval that aims to generate identifier strings of relevant passages as the retrieval target. This generative paradigm taps into powerful generative language models, distinct from traditional sparse or dense retrieval methods. However, only learning to generate is insufficient for generative retrieval. Generative retrieval learns to generate identifiers of relevant passages as an intermediate goal and then converts predicted identifiers into the final passage rank list. The disconnect between the learning objective of autoregressive models and the desired passage ranking target leads to a learning gap. To bridge this gap, we propose a learning-to-rank framework for generative retrieval, dubbed LTRGR. LTRGR enables generative retrieval to learn to rank passages directly, optimizing the autoregressive model toward the final passage ranking target via a rank loss. This framework only requires an additional learning-to-rank training phase to enhance current generative retrieval systems and does not add any burden to the inference stage. We conducted experiments on three public benchmarks, and the results demonstrate that LTRGR achieves state-of-the-art performance among generative retrieval methods. The code and checkpoints are released at https://github.com/liyongqi67/LTRGR.
Temporally and Distributionally Robust Optimization for Cold-Start Recommendation
Authors: Lin Xinyu , Wang Wenjie , Zhao Jujia , Li Yongqi , Feng Fuli , Chua Tat-Seng
Published: March 2024

Collaborative Filtering (CF) recommender models highly depend on user-item interactions to learn CF representations, thus falling short of recommending cold-start items. To address this issue, prior studies mainly introduce item features (e.g., thumbnails) for cold-start item recommendation. They learn a feature extractor on warm-start items to align feature representations with interactions, and then leverage the feature extractor to extract the feature representations of cold-start items for interaction prediction. Unfortunately, the features of cold-start items, especially the popular ones, tend to diverge from those of warm-start ones due to temporal feature shifts, preventing the feature extractor from accurately learning feature representations of cold-start items.
To alleviate the impact of temporal feature shifts, we consider using Distributionally Robust Optimization (DRO) to enhance the generation ability of the feature extractor. Nonetheless, existing DRO methods face an inconsistency issue: the worse-case warm-start items emphasized during DRO training might not align well with the cold-start item distribution. To capture the temporal feature shifts and combat this inconsistency issue, we propose a novel temporal DRO with new optimization objectives, namely, 1) to integrate a worst-case factor to improve the worst-case performance, and 2) to devise a shifting factor to capture the shifting trend of item features and enhance the optimization of the potentially popular groups in cold-start items. Substantial experiments on three real-world datasets validate the superiority of our temporal DRO in enhancing the generalization ability of cold-start recommender models.
Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks
Authors: Qiu Chenyang , Nan Guoshun , Xiong Tianyu , Deng Wendi , Wang Di , Teng Zhiyang , Sun Lijuan , Cui Qimei , Tao Xiaofeng
Published: March 2024

Graph convolution networks (GCNs) are extensively utilized in various graph tasks to mine knowledge from spatial data. Our study marks the pioneering attempt to quantitatively investigate the GCN robustness over omnipresent heterophilic graphs for node classification. We uncover that the predominant vulnerability is caused by the structural out-of-distribution (OOD) issue. This finding motivates us to present a novel method that aims to harden GCNs by automatically learning Latent Homophilic Structures over heterophilic graphs. We term such a methodology as LHS. To elaborate, our initial step involves learning a latent structure by employing a novel self-expressive technique based on multi-node interactions. Subsequently, the structure is refined using a pairwisely constrained dual-view contrastive learning approach. We iteratively perform the above procedure, enabling a GCN model to aggregate information in a homophilic way on heterophilic graphs. Armed with such an adaptable structure, we can properly mitigate the structural OOD threats over heterophilic graphs. Experiments on various benchmarks show the effectiveness of the proposed LHS approach for robust GCNs.
STEM: Unleashing the Power of Embeddings for Multi-Task Recommendation
Authors: Su Liangcai , Pan Junwei , Wang Ximei , Xiao Xi , Quan Shijie , Chen Xihua , Jiang Jie
Published: March 2024

Multi-task learning (MTL) has gained significant popularity in recommender systems as it enables simultaneous optimization of multiple objectives. A key challenge in MTL is negative transfer, but existing studies explored negative transfer on all samples, overlooking the inherent complexities within them. We split the samples according to the relative amount of positive feedback among tasks. Surprisingly, negative transfer still occurs in existing MTL methods on samples that receive comparable feedback across tasks. Existing work commonly employs a shared-embedding paradigm, limiting the ability of modeling diverse user preferences on different tasks. In this paper, we introduce a novel Shared and Task-specific EMbeddings (STEM) paradigm that aims to incorporate both shared and task-specific embeddings to effectively capture task-specific user preferences. Under this paradigm, we propose a simple model STEM-Net, which is equipped with an All Forward Task-specific Backward gating network to facilitate the learning of task-specific embeddings and direct knowledge transfer across tasks. Remarkably, STEM-Net demonstrates exceptional performance on comparable samples, achieving positive transfer. Comprehensive evaluation on three public MTL recommendation datasets demonstrates that STEM-Net outperforms state-of-the-art models by a substantial margin. Our code is released at https://github.com/LiangcaiSu/STEM.
Towards Dynamic Spatial-Temporal Graph Learning: A Decoupled Perspective
Authors: Wang Binwu , Wang Pengkun , Zhang Yudong , Wang Xu , Zhou Zhengyang , Bai Lei , Wang Yang
Published: March 2024

With the progress of urban transportation systems, a significant amount of high-quality traffic data is continuously collected through streaming manners, which has propelled the prosperity of the field of spatial-temporal graph prediction. In this paper, rather than solely focusing on designing powerful models for static graphs, we shift our focus to spatial-temporal graph prediction in the dynamic scenario, which involves a continuously expanding and evolving underlying graph. To address inherent challenges, a decoupled learning framework (DLF) is proposed in this paper, which consists of a spatial-temporal graph learning network (DSTG) with a specialized decoupling training strategy. Incorporating inductive biases of time-series structures, DSTG can interpret time dependencies into latent trend and seasonal terms. To enable prompt adaptation to the evolving distribution of the dynamic graph, our decoupling training strategy is devised to iteratively update these two types of patterns. Specifically, for learning seasonal patterns, we conduct thorough training for the model using a long time series (e.g., three months of data). To enhance the learning ability of the model, we also introduce the masked auto-encoding mechanism. During this period, we frequently update trend patterns to expand new information from dynamic graphs. Considering both effectiveness and efficiency, we develop a subnet sampling strategy to select a few representative nodes for fine-tuning the weights of the model. These sampled nodes cover unseen patterns and previously learned patterns. Experiments on dynamic spatial-temporal graph datasets further demonstrate the competitive performance, superior efficiency, and strong scalability of the proposed framework.
UNEX-RL: Reinforcing Long-Term Rewards in Multi-Stage Recommender Systems with UNidirectional EXecution
Authors: Zhang Gengrui , Wang Yao , Chen Xiaoshuang , Qian Hongyi , Zhan Kaiqiao , Wang Ben
Published: March 2024

In recent years, there has been a growing interest in utilizing reinforcement learning (RL) to optimize long-term rewards in recommender systems. Since industrial recommender systems are typically designed as multi-stage systems, RL methods with a single agent face challenges when optimizing multiple stages simultaneously. The reason is that different stages have different observation spaces, and thus cannot be modeled by a single agent. To address this issue, we propose a novel UNidirectional-EXecution-based multi-agent Reinforcement Learning (UNEX-RL) framework to reinforce the long-term rewards in multi-stage recommender systems.
We show that the unidirectional execution is a key feature of multi-stage recommender systems, bringing new challenges to the applications of multi-agent reinforcement learning (MARL), namely the observation dependency and the cascading effect. To tackle these challenges, we provide a cascading information chain (CIC) method to separate the independent observations from action-dependent observations and use CIC to train UNEX-RL effectively. We also discuss practical variance reduction techniques for UNEX-RL. Finally, we show the effectiveness of UNEX-RL on both public datasets and an online recommender system with over 100 million users. Specifically, UNEX-RL reveals a 0.558% increase in users' usage time compared with single-agent RL algorithms in online A/B experiments, highlighting the effectiveness of UNEX-RL in industrial recommender systems.
M3D: Dataset Condensation by Minimizing Maximum Mean Discrepancy
Authors: Zhang Hansong , Li Shikun , Wang Pengju , Zeng Dan , Ge Shiming
Published: March 2024

Training state-of-the-art (SOTA) deep models often requires extensive data, resulting in substantial training and storage costs. To address these challenges, dataset condensation has been developed to learn a small synthetic set that preserves essential information from the original large-scale dataset. Nowadays, optimization-oriented methods have been the primary method in the field of dataset condensation for achieving SOTA results. However, the bi-level optimization process hinders the practical application of such methods to realistic and larger datasets. To enhance condensation efficiency, previous works proposed Distribution-Matching (DM) as an alternative, which significantly reduces the condensation cost. Nonetheless, current DM-based methods still yield less comparable results to SOTA optimization-oriented methods. In this paper, we argue that existing DM-based methods overlook the higher-order alignment of the distributions, which may lead to sub-optimal matching results. Inspired by this, we present a novel DM-based method named M3D for dataset condensation by Minimizing the Maximum Mean Discrepancy between feature representations of the synthetic and real images. By embedding their distributions in a reproducing kernel Hilbert space, we align all orders of moments of the distributions of real and synthetic images, resulting in a more generalized condensed set. Notably, our method even surpasses the SOTA optimization-oriented method IDC on the high-resolution ImageNet dataset. Extensive analysis is conducted to verify the effectiveness of the proposed method. Source codes are available at https://github.com/Hansong-Zhang/M3D.
Influential Exemplar Replay for Incremental Learning in Recommender Systems
Authors: Zhang Xinni , Chen Yankai , Ma Chenhao , Fang Yixiang , King Irwin
Published: March 2024

Personalized recommender systems have found widespread applications for effective information filtering. Conventional models engage in knowledge mining within the static setting to reconstruct singular historical data. Nonetheless, the dynamics of real-world environments are in a constant state of flux, rendering acquired model knowledge inadequate for accommodating emergent trends and thus leading to notable recommendation performance decline. Given the typically prohibitive cost of exhaustive model retraining, it has emerged to study incremental learning for recommender systems with ever-growing data. In this paper, we propose an effective model-agnostic framework, namely INFluential Exemplar Replay (INFER). INFER facilitates recommender models in retaining the earlier assimilated knowledge, e.g., users' enduring preferences, while concurrently accommodating evolving trends manifested in users' new interaction behaviors. We commence with a vanilla implementation that centers on identifying the most representative data samples for effective consolidation of early knowledge. Subsequently, we propose an advanced solution, namely INFERONCE, to optimize the computational overhead associated with the vanilla implementation. Extensive experiments on four prototypical backbone models, two classic recommendation tasks, and four widely used benchmarks consistently demonstrate the effectiveness of our method as well as its compatibility for extending to several incremental recommender models.
Efficient Learning in Polyhedral Games via Best-Response Oracles
Authors: Chakrabarti Darshan , Farina Gabriele , Kroer Christian
Published: March 2024

We study online learning and equilibrium computation in games with polyhedral decision sets, a property shared by normal-form games (NFGs) and extensive-form games (EFGs), when the learning agent is restricted to utilizing a best-response oracle. We show how to achieve constant regret in zero-sum games and O(T^0.25) regret in general-sum games while using only O(log t) best-response queries at a given iteration t, thus improving over the best prior result, which required O(T) queries per iteration. Moreover, our framework yields the first last-iterate convergence guarantees for self-play with best-response oracles in zero-sum games. This convergence occurs at a linear rate, though with a condition-number dependence. We go on to show a O(T^(-0.5)) best-iterate convergence rate without such a dependence. Our results build on linear-rate convergence results for variants of the Frank-Wolfe (FW) algorithm for strongly convex and smooth minimization problems over polyhedral domains. These FW results depend on a condition number of the polytope, known as facial distance. In order to enable application to settings such as EFGs, we show two broad new results: 1) the facial distance for polytopes in standard form is at least γ/k where γ is the minimum value of a nonzero coordinate of a vertex of the polytope and k≤n is the number of tight inequality constraints in the optimal face, and 2) the facial distance for polytopes of the form Ax=b, Cx≤d, x≥0 where x∈R^n, C≥0 is a nonzero integral matrix, and d≥0, is at least 1/(c√n), where c is the infinity norm of C. This yields the first such results for several problems such as sequence-form polytopes, flow polytopes, and matching polytopes.
Independence of Irrelevant Alternatives under the Lens of Pairwise Distortion
Authors: Delemazure Théo , Lang Jérôme , Pierczyński Grzegorz
Published: March 2024

We give a quantitative analysis of the independence of irrelevant alternatives (IIA) axiom. IIA says that the society's preference between x and y should depend only on individual preferences between x and y: we show that, in several contexts, if the individuals express their preferences about additional (``irrelevant'') alternatives, this information helps to estimate better which of x and y has higher social welfare.
Our contribution is threefold: (1) we provide a new tool to measure the impact of IIA on social welfare (pairwise distortion), based on the well-established notion of voting distortion, (2) we study the average impact of IIA in both general and metric settings, with experiments on synthetic and real data and (3) we study the worst-case impact of IIA in the 1D-Euclidean metric space.
Cost Minimization for Equilibrium Transition
Authors: Huang Haoqiang , Wang Zihe , Wei Zhide , Zhang Jie
Published: March 2024

In this paper, we delve into the problem of using monetary incentives to encourage players to shift from an initial Nash equilibrium to a more favorable one within a game. Our main focus revolves around computing the minimum reward required to facilitate this equilibrium transition. The game involves a single row player who possesses m strategies and k column players, each endowed with n strategies. Our findings reveal that determining whether the minimum reward is zero is NP-complete, and computing the minimum reward becomes APX-hard. Nonetheless, we bring some positive news, as this problem can be efficiently handled if either k or n is a fixed constant. Furthermore, we have devised an approximation algorithm with an additive error that runs in polynomial time. Lastly, we explore a specific case wherein the utility functions exhibit single-peaked characteristics, and we successfully demonstrate that the optimal reward can be computed in polynomial time.
Maximizing Nash Social Welfare under Two-Sided Preferences
Authors: Jain Pallavi , Vaish Rohit
Published: March 2024

The maximum Nash social welfare (NSW)---which maximizes the geometric mean of agents' utilities---is a fundamental solution concept with remarkable fairness and efficiency guarantees. The computational aspects of NSW have been extensively studied for *one-sided* preferences where a set of agents have preferences over a set of resources. Our work deviates from this trend and studies NSW maximization for *two-sided* preferences, wherein a set of workers and firms, each having a cardinal valuation function, are matched with each other. We provide a systematic study of the computational complexity of maximizing NSW for many-to-one matchings under two-sided preferences. Our main negative result is that maximizing NSW is NP-hard even in a highly restricted setting where each firm has capacity 2, all valuations are in the range {0,1,2}, and each agent positively values at most three other agents. In search of positive results, we develop approximation algorithms as well as parameterized algorithms in terms of natural parameters such as the number of workers, the number of firms, and the firms' capacities. We also provide algorithms for restricted domains such as symmetric binary valuations and bounded degree instances.
Pay to (Not) Play: Monetizing Impatience in Mobile Games
Authors: Lundy Taylor , Raman Narun , Fu Hu , Leyton-Brown Kevin
Published: March 2024

Mobile gaming is a rapidly growing and incredibly profitable sector; having grown seven-fold over the past 10 years, it now grosses over $100 billion annually. This growth was due in large part to a shift in monetization strategies: rather than charging players an upfront cost ("pay-to-play"), games often request optional microtransactions throughout gameplay ("free-to-play"). We focus on a common scenario in which games include wait times---gating either items or game progression---that players can pay to skip. Game designers typically say that they optimize for player happiness rather than revenue; however, prices for skips are typically set at levels that few players are willing to pay, leading to low purchase rates. Under a traditional analysis, it would seem that game designers fail at their stated goal if few players buy what they are selling. We argue that an alternate model can better explain this dynamic: players value tasks more highly as they are perceived to be more difficult. While skips can increase players' utilities by providing instant gratification, pricing skips too cheaply can lower players' utilities by decreasing the perceived amount of work needed to complete a task. We show that high revenue, high player utility, and low purchase rates can all coexist under this model, particularly under a realistic distribution of players having few buyers but a few big-spending "whales." We also investigate how a game designer should optimize prices under our model. An appendix of the paper with proofs, more comprehensive results and visualizations can be found at https://arxiv.org/abs/2312.10205.
Weighted Envy-Freeness for Submodular Valuations
Authors: Montanari Luisa , Schmidt-Kraepelin Ulrike , Suksompong Warut , Teh Nicholas
Published: March 2024

We investigate the fair allocation of indivisible goods to agents with possibly different entitlements represented by weights. Previous work has shown that guarantees for additive valuations with existing envy-based notions cannot be extended to the case where agents have matroid-rank (i.e., binary submodular) valuations. We propose two families of envy-based notions for matroid-rank and general submodular valuations, one based on the idea of transferability and the other on marginal values. We show that our notions can be satisfied via generalizations of rules such as picking sequences and maximum weighted Nash welfare. In addition, we introduce welfare measures based on harmonic numbers, and show that variants of maximum weighted harmonic welfare offer stronger fairness guarantees than maximum weighted Nash welfare under matroid-rank valuations.
Learning Optimal Advantage from Preferences and Mistaking It for Reward
Authors: Knox W. Bradley , Hatgis-Kessell Stephane , Adalgeirsson Sigurdur Orn , Booth Serena , Dragan Anca , Stone Peter , Niekum Scott
Published: March 2024

We consider algorithms for learning reward functions from human preferences over pairs of trajectory segments, as used in reinforcement learning from human feedback (RLHF).
Most recent work assumes that human preferences are generated based only upon the reward accrued within those segments, or their partial return. Recent work casts doubt on the validity of this assumption, proposing an alternative preference model based upon regret. We investigate the consequences of assuming preferences are based upon partial return when they actually arise from regret. We argue that the learned function is an approximation of the optimal advantage function, not a reward function. We find that if a specific pitfall is addressed, this incorrect assumption is not particularly harmful, resulting in a highly shaped reward function. Nonetheless, this incorrect usage of the approximation of the optimal advantage function is less desirable than the appropriate and simpler approach of greedy maximization of it. From the perspective of the regret preference model, we also provide a clearer interpretation of fine tuning contemporary large language models with RLHF. This paper overall provides insight regarding why learning under the partial return preference model tends to work so well in practice, despite it conforming poorly to how humans give preferences.
When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programming
Authors: Mozannar Hussein , Bansal Gagan , Fourney Adam , Horvitz Eric
Published: March 2024

AI powered code-recommendation systems, such as Copilot and CodeWhisperer, provide code suggestions inside a programmer's environment (e.g., an IDE) with the aim of improving productivity. We pursue mechanisms for leveraging signals about programmers' acceptance and rejection of code suggestions to guide recommendations. We harness data drawn from interactions with GitHub Copilot, a system used by millions of programmers, to develop interventions that can save time for programmers. We introduce a utility-theoretic framework to drive decisions about suggestions to display versus withhold. The approach, conditional suggestion display from human feedback (CDHF), relies on a cascade of models that provide the likelihood that recommended code will be accepted. These likelihoods are used to selectively hide suggestions, reducing both latency and programmer verification time. Using data from 535 programmers, we perform a retrospective evaluation of CDHF and show that we can avoid displaying a significant fraction of suggestions that would have been rejected. We further demonstrate the importance of incorporating the programmer's latent unobserved state in decisions about when to display suggestions through an ablation study. Finally, we showcase how using suggestion acceptance as a reward signal for guiding the display of suggestions can lead to suggestions of reduced quality, indicating an unexpected pitfall.
CTO-SLAM: Contour Tracking for Object-Level Robust 4D SLAM
Authors: Li Xiaohan , Liu Dong , Wu Jun
Published: March 2024

The demand for 4D ( 3D+time ) SLAM system is increasingly urgent, especially for decision-making and scene understanding. However, most of the existing simultaneous localization and mapping ( SLAM ) systems primarily assume static environments. They fail to represent dynamic scenarios due to the challenge of establishing robust long-term spatiotemporal associations in dynamic object tracking. We address this limitation and propose CTO-SLAM, a monocular and RGB-D object-level 4D SLAM system to track moving objects and estimate their motion simultaneously. In this paper, we propose contour tracking, which introduces contour features to enhance the keypoint representation of dynamic objects and coupled with pixel tracking to achieve long-term robust object tracking. Based on contour tracking, we propose a novel sampling-based object pose initialization algorithm and the following adapted bundle adjustment ( BA ) optimization algorithm to estimate dynamic object poses with high accuracy. The CTO-SLAM system is verified on both KITTI and VKITTI datasets. The experimental results demonstrate that our system effectively addresses cumulative errors in long-term spatiotemporal association and hence obtains substantial improvements over the state-of-the-art systems. The source code is available at https://github.com/realXiaohan/CTO-SLAM.
On the Expressivity of Recurrent Neural Cascades
Authors: Knorozova Nadezda Alexandrovna , Ronca Alessandro
Published: March 2024

Recurrent Neural Cascades (RNCs) are the recurrent neural networks with no cyclic dependencies among recurrent neurons. This class of recurrent networks has received a lot of attention in practice. Besides training methods for a fixed architecture such as backpropagation, the cascade architecture naturally allows for constructive learning methods, where recurrent nodes are added incrementally one at a time, often yielding smaller networks. Furthermore, acyclicity amounts to a structural prior that even for the same number of neurons yields a more favourable sample complexity compared to a fully-connected architecture.
A central question is whether the advantages of the cascade architecture come at the cost of a reduced expressivity. We provide new insights into this question. We show that the regular languages captured by RNCs with sign and tanh activation with positive recurrent weights are the star-free regular languages. In order to establish our results we developed a novel framework where capabilities of RNCs are assessed by analysing which semigroups and groups a single neuron is able to implement. A notable implication of our framework is that RNCs can achieve the expressivity of all regular languages by introducing neurons that can implement groups.
Abstraction of Situation Calculus Concurrent Game Structures
Authors: Lesperance Yves , De Giacomo Giuseppe , Rostamigiv Maryam , Khan Shakil M.
Published: March 2024

We present a general framework for abstracting agent behavior in multi-agent synchronous games in the situation calculus, which provides a first-order representation of the state and allows us to model how plays depend on the data and objects involved. We represent such games as action theories of a special form called situation calculus synchronous game structures (SCSGSs), in which we have a single action "tick" whose effects depend on the combination of moves selected by the players. In our framework, one specifies both an abstract SCSGS and a concrete SCSGS, as well as a refinement mapping that specifies how each abstract move is implemented by a Golog program defined over the concrete SCSGS. We define notions of sound and complete abstraction with respect to a mapping over such SCSGS. To express strategic properties on the abstract and concrete games we adopt a first-order variant of alternating-time mu-calculus mu-ATL-FO. We show that we can exploit abstraction in verifying mu-ATL-FO properties of SCSGSs under the assumption that agents can always execute abstract moves to completion even if not fully controlling their outcomes.
Relational Programming with Foundational Models
Authors: Li Ziyang , Huang Jiani , Liu Jason , Zhu Felix , Zhao Eric , Dodds William , Velingker Neelay , Alur Rajeev , Naik Mayur
Published: March 2024

Foundation models have vast potential to enable diverse AI applications. The powerful yet incomplete nature of these models has spurred a wide range of mechanisms to augment them with capabilities such as in-context learning, information retrieval, and code interpreting. We propose Vieira, a declarative framework that unifies these mechanisms in a general solution for programming with foundation models. Vieira follows a probabilistic relational paradigm and treats foundation models as stateless functions with relational inputs and outputs. It supports neuro-symbolic applications by enabling the seamless combination of such models with logic programs, as well as complex, multi-modal applications by streamlining the composition of diverse sub-models. We implement Vieira by extending the Scallop compiler with a foreign interface that supports foundation models as plugins. We implement plugins for 12 foundation models including GPT, CLIP, and SAM. We evaluate Vieira on 9 challenging tasks that span language, vision, and structured and vector databases. Our evaluation shows that programs in Vieira are concise, can incorporate modern foundation models, and have comparable or better accuracy than competitive baselines.
Scalable Enumeration of Trap Spaces in Boolean Networks via Answer Set Programming
Authors: Trinh Giang , Benhamou Belaid , Pastva Samuel , Soliman Sylvain
Published: March 2024

Boolean Networks (BNs) are widely used as a modeling formalism in several domains, notably systems biology and computer science. A fundamental problem in BN analysis is the enumeration of trap spaces, which are hypercubes in the state space that cannot be escaped once entered. Several methods have been proposed for enumerating trap spaces, however they often suffer from scalability and efficiency issues, particularly for large and complex models. To our knowledge, the most efficient and recent methods for the trap space enumeration all rely on Answer Set Programming (ASP), which has been widely applied to the analysis of BNs. Motivated by these considerations, our work proposes a new method for enumerating trap spaces in BNs using ASP. We evaluate the method on a mix of 250+ real-world and 400+ randomly generated BNs, showing that it enables analysis of models beyond the capabilities of existing tools (namely pyboolnet, mpbn, trappist, and trapmvn).
Use of shells for the mechanical stabilisation of sediments: a valuable geomechanical perspective?
Authors: Petti Rossella , Vitone Claudia , Marchi Maurizio Iler , Plötze Michael , Puzrin Alexander
Published: March 2024

The present study contributes to developing a novel eco-friendly solution for the mechanical stabilisation of dredged marine sediments by using mussel shells – another high-impact waste of marine origin – in the partial replacement of cements. What are the underlying chemo-mechanical interactions affecting the evolution of such mixtures? Can mussel shells replace cement without compromising useful geomechanical and geochemical properties of the stabilised sediments? Can such mixtures still be modelled as soils when it comes to their geotechnical design and analysis? The paper answers these questions by assessing the mechanical performance of mixtures formed by sediments stabilised with three types of cement and a mussel shell powder and comparing them with those of the same sediments treated with cement only. Multiple beneficial effects of the use of mussel shell powder, as a peculiar source of calcium carbonate from its biogenic origin, have been demonstrated: it acts as a void filler; enhances the electrolytic exchanges between sediment and cement, and increases the contact area between the mineral particles promoting the chemical hydration reactions. As a result, for fixed replacement ratios, the original mixtures still exhibit soil-like behaviour consistent with traditional geomechanics and even better performance than the control mixtures.
Dynamic stress analysis of rock joints under railway loading
Authors: Cecilio Jr Marlisio Oliveira , Indraratna Buddhima , Rujikiatkamjorn Cholachat , Malisetty Rakesh Sai
Published: March 2024

The proper estimation of stresses generated by the passage of a train is fundamentally important to the serviceability and longevity of railways, and yet very limited knowledge is available where the track substructure is built on a jointed rock mass. The present study introduces an analytical solution for estimating the ground stresses arising from moving wheel loads, causing a change in the three-dimensional stress state in the track formation, in relation to the stress variation with depth and along the longitudinal track section – that is, the direction of train passage. Based on 21 case histories, an array of field measurements and numerical simulations covering a wide range of freight tonnage, train speeds and different formation conditions were considered to validate the proposed analytical solution. The proposed methodology (analytical solution) was then applied to a jointed rock subgrade to determine the normal and shear stresses acting along a specific discontinuity plane. The main analytical outcome demonstrates that the orthogonal vertical and shear stresses present different and phase-shifted history plots for homogeneous ground conditions with principal stresses rotation. However, conversely for a jointed subgrade, the normal and shear stresses along the discontinuity have the same history plot pattern and are in phase. As a practical guide, the results from this study would help to define which cyclic loads should be applied in laboratory tests to simulate realistic traffic patterns of trains travelling over a jointed rock subgrade.
Analysis of risk factors associated with pre-myopia among primary school students in the Mianyang Science City Area
Authors: Wang Xiaoyin , Deng Yi-bin    , Xiao Li-ge , Wang  Hui-Min , Zhao Guo-Zhong , Ye Lian , Men Da-Wei , Yan Mei
Published: March 2024

Objectives To find out the prevalence rate of pre-myopia among primary school students in the Mianyang Science City Area, analyze its related risk factors, and thus provide a reference for local authorities to formulate policies on the prevention and control of myopia for primary school students. Methods　 October 2022, Cluster sampling was adopted by our research group to obtain the vision levels of 2278 primary school students employing a diopter test in the Science City Area. In addition, questionnaires were distributed to help us find the risk factors associated with pre-myopia. Results The prevalence rate of pre-myopia among primary school students in the Science  City Area was 45.27%, of which 43.82% were boys and 46.92% were girls, with no statistically significant difference in the prevalence rate of myopia between boys and girls (c2 =2.171, P=0.141). Multiple logistic regression analysis demonstrated that the main risk factors for pre-myopia were having at least one parent with severe myopia, spending less than 2 hours a day outdoors, lack of sleep looking at electronic screens for more than 1 hours, and having an improper reading and writing posture. Outdoor activity time less than 2 hours per day, lack of sleep, looking at the electronic screen for more than 1 hour per day, and incorrect reading and writing posture were all positively associated with the pre-myopia (P values < 0.05). Conclusion The Science City Area has a high prevalence rate of pre-myopia among primary school students. It is proposed that students, schools, families, and local authorities work together to increase the time spent outdoors, get adequate sleep, reduce the time spent staring at digital screens and develop scientific use of eye habits.
Use of Integrative Medicine Practices by Pregnant Women with Restless Legs Syndrome
Authors: Kaplan Özlem , Başer Mürüvvet , Çağli Fulya
Published: March 2024

Objective: To determine the use of integrative medicine practices in pregnant women with restless legs syndrome.

Methods: This cross-sectional descriptive study was conducted in the perinatology service and outpatient clinic of a university hospital between 2 February-11 May 2022. The study included 148 pregnant women with restless legs syndrome who met the inclusion criteria. Data were collected using the Restless Legs Syndrome Diagnostic Criteria Questionnaire, the Restless Legs Syndrome Severity Rating Scale, a Personal Information Form, and an Integrative Medicine Use Form.

Results: The pregnant women of the 61.4% were in their last trimester, and 75.7% had no information about restless legs syndrome. Symptoms started at an average of 20.29 ± 8.65 weeks of gestation and 81.1% of the pregnant women did not consult a physician. Of those who did consult a physician, 57.7% consulted a gynecologist and 81.0% were recommended vitamins/minerals as a treatment. For syndrome symptoms, 50.7% of pregnant women used integrative medicine practices. The four most commonly used methods were taking vitamins (55.9%), prayer (51.9%), brisk walking (45.2%) and massage (33.3%). There was no statistically significant difference between the use of integrative medicine practices and the severity of restless legs syndrome (P> .05).

Conclusion: Restless legs syndrome is a condition that is not widely recognized by pregnant women. About half of pregnant women try to manage the condition by using integrative medical practices for syndrome. Nurses and other health professionals should provide information to pregnant women to raise awareness of the disease and provide effective treatment options.