bluuebunny/crossref_metadata_2025 huggingface dataset:
Mining Gaze for Contrastive Learning toward Computer-Assisted Diagnosis
Authors: Zhao Zihao , Wang Sheng , Wang Qian , Shen Dinggang
Published: March 2024

Obtaining large-scale radiology reports can be difficult for medical images due to ethical concerns, limiting the effectiveness of contrastive pre-training in the medical image domain and underscoring the need for alternative methods. In this paper, we propose eye-tracking as an alternative to text reports, as it allows for the passive collection of gaze signals without ethical issues. By tracking the gaze of radiologists as they read and diagnose medical images, we can understand their visual attention and clinical reasoning. When a radiologist has similar gazes for two medical images, it may indicate semantic similarity for diagnosis, and these images should be treated as positive pairs when pre-training a computer-assisted diagnosis (CAD) network through contrastive learning. Accordingly, we introduce the Medical contrastive Gaze Image Pre-training (McGIP) as a plug-and-play module for contrastive learning frameworks. McGIP uses radiologist gaze to guide contrastive pre-training. We evaluate our method using two representative types of medical images and two common types of gaze data. The experimental results demonstrate the practicality of McGIP, indicating its high potential for various clinical scenarios and applications.
DiffBEV: Conditional Diffusion Model for Bird’s Eye View Perception
Authors: Zou Jiayu , Tian Kun , Zhu Zheng , Ye Yun , Wang Xingang
Published: March 2024

BEV perception is of great importance in the field of autonomous driving, serving as the cornerstone of planning, controlling, and motion prediction. The quality of the BEV feature highly affects the performance of BEV perception. However, taking the noises in camera parameters and LiDAR scans into consideration, we usually obtain BEV representation with harmful noises. Diffusion models naturally have the ability to denoise noisy samples to the ideal data, which motivates us to utilize the diffusion model to get a better BEV representation. In this work, we propose an end-to-end framework, named DiffBEV, to exploit the potential of diffusion model to generate a more comprehensive BEV representation. To the best of our knowledge, we are the first to apply diffusion model to BEV perception. In practice, we design three types of conditions to guide the training of the diffusion model which denoises the coarse samples and refines the semantic feature in a progressive way. What's more, a cross-attention module is leveraged to fuse the context of BEV feature and the semantic content of conditional diffusion model. DiffBEV achieves a 25.9% mIoU on the nuScenes dataset, which is 6.2% higher than the best-performing existing approach. Quantitative and qualitative results on multiple benchmarks demonstrate the effectiveness of DiffBEV in BEV semantic segmentation and 3D object detection tasks.
Large-Scale Non-convex Stochastic Constrained Distributionally Robust Optimization
Authors: Zhang Qi , Zhou Yi , Prater-Bennette Ashley , Shen Lixin , Zou Shaofeng
Published: March 2024

Distributionally robust optimization (DRO) is a powerful framework for training robust models against data distribution shifts. This paper focuses on constrained DRO, which has an explicit characterization of the robustness level. Existing studies on constrained DRO mostly focus on convex loss function, and exclude the practical and challenging case with non-convex loss function, e.g., neural network. This paper develops a stochastic algorithm and its performance analysis for non-convex constrained DRO. The computational complexity of our stochastic algorithm at each iteration is independent of the overall dataset size, and thus is suitable for large-scale applications. We focus on the general Cressie-Read family divergence defined uncertainty set which includes chi^2-divergences as a special case. We prove that our algorithm finds an epsilon-stationary point with an improved computational complexity than existing methods. Our method also applies to the smoothed conditional value at risk (CVaR) DRO.
Learning to Reweight for Generalizable Graph Neural Network
Authors: Chen Zhengyu , Xiao Teng , Kuang Kun , Lv Zheqi , Zhang Min , Yang Jinluan , Lu Chengqiang , Yang Hongxia , Wu Fei
Published: March 2024

Graph Neural Networks (GNNs) show promising results for graph tasks. However, existing GNNs' generalization ability will degrade when there exist distribution shifts between testing and training graph data.
The fundamental reason for the severe degeneration is that most GNNs are designed based on the I.I.D hypothesis. In such a setting, GNNs tend to exploit subtle statistical correlations existing in the training set for predictions, even though it is a spurious correlation.
In this paper, we study the problem of the generalization ability of GNNs on Out-Of-Distribution (OOD) settings.
To solve this problem, we propose the Learning to Reweight for Generalizable Graph Neural Network (L2R-GNN) to enhance the generalization ability for achieving satisfactory performance on unseen testing graphs that have different distributions with training graphs.
We propose a novel nonlinear graph decorrelation method, which can substantially improve the out-of-distribution generalization ability and compares favorably to previous methods in restraining the over-reduced sample size.
The variables of graph representation are clustered based on the stability of their correlations, and graph decorrelation method learns weights to remove correlations between the variables of different clusters rather than any two variables.
Besides, we introduce an effective stochastic algorithm based on bi-level optimization for the L2R-GNN framework, which enables simultaneously learning the optimal weights and GNN parameters, and avoids the over-fitting issue.
Experiments show that L2R-GNN greatly outperforms baselines on various graph prediction benchmarks under distribution shifts.
Multi-Domain Recommendation to Attract Users via Domain Preference Modeling
Authors: Ju Hyunjun , Kang SeongKu , Lee Dongha , Hwang Junyoung , Jang Sanghwan , Yu Hwanjo
Published: March 2024

Recently, web platforms are operating various service domains simultaneously. Targeting a platform that operates multiple service domains, we introduce a new task, Multi-Domain Recommendation to Attract Users (MDRAU), which recommends items from multiple ``unseen'' domains with which each user has not interacted yet, by using knowledge from the user's ``seen'' domains. In this paper, we point out two challenges of MDRAU task. First, there are numerous possible combinations of mappings from seen to unseen domains because users have usually interacted with a different subset of service domains. Second, a user might have different preference for each of the target unseen domains, which requires recommendations to reflect users' preference on domains as well as items. To tackle these challenges, we propose DRIP framework that models users' preference at two levels (i.e., domain and item) and learns various seen-unseen domain mappings in a unified way with masked domain modeling. Our extensive experiments demonstrate the effectiveness of DRIP in MDRAU task and its ability to capture users' domain-level preferences.
Hawkes-Enhanced Spatial-Temporal Hypergraph Contrastive Learning Based on Criminal Correlations
Authors: Liang Ke , Zhou Sihang , Liu Meng , Liu Yue , Tu Wenxuan , Zhang Yi , Fang Liming , Liu Zhe , Liu Xinwang
Published: March 2024

Crime prediction is a crucial yet challenging task within urban computing, which benefits public safety and resource optimization. Over the years, various models have been proposed, and spatial-temporal hypergraph learning models have recently shown outstanding performances. However, three correlations underlying crime are ignored, thus hindering the performance of previous models. Specifically, there are two spatial correlations and one temporal correlation, i.e., (1) co-occurrence of different types of crimes (type spatial correlation), (2) the closer to the crime center, the more dangerous it is around the neighborhood area (neighbor spatial correlation), and (3) the closer between two timestamps, the more relevant events are (hawkes temporal correlation). To this end, we propose Hawkes-enhanced Spatial-Temporal Hypergraph Contrastive Learning framework (HCL), which mines the aforementioned correlations via two specific strategies. Concretely, contrastive learning strategies are designed for two spatial correlations, and hawkes process modeling is adopted for temporal correlations. Extensive experiments demonstrate the promising capacities of HCL from four aspects, i.e., superiority, transferability, effectiveness, and sensitivity.
Towards Continual Knowledge Graph Embedding via Incremental Distillation
Authors: Liu Jiajun , Ke Wenjun , Wang Peng , Shang Ziyu , Gao Jinhua , Li Guozheng , Ji Ke , Liu Yanhe
Published: March 2024

Traditional knowledge graph embedding (KGE) methods typically require preserving the entire knowledge graph (KG) with significant training costs when new knowledge emerges. To address this issue, the continual knowledge graph embedding (CKGE) task has been proposed to train the KGE model by learning emerging knowledge efficiently while simultaneously preserving decent old knowledge. However, the explicit graph structure in KGs, which is critical for the above goal, has been heavily ignored by existing CKGE methods. On the one hand, existing methods usually learn new triples in a random order, destroying the inner structure of new KGs. On the other hand, old triples are preserved with equal priority, failing to alleviate catastrophic forgetting effectively. In this paper, we propose a competitive method for CKGE based on incremental distillation (IncDE), which considers the full use of the explicit graph structure in KGs. First, to optimize the learning order, we introduce a hierarchical strategy, ranking new triples for layer-by-layer learning. By employing the inter- and intra-hierarchical orders together, new triples are grouped into layers based on the graph structure features. Secondly, to preserve the old knowledge effectively, we devise a novel incremental distillation mechanism, which facilitates the seamless transfer of entity representations from the previous layer to the next one, promoting old knowledge preservation. Finally, we adopt a two-stage training paradigm to avoid the over-corruption of old knowledge influenced by under-trained new knowledge. Experimental results demonstrate the superiority of IncDE over state-of-the-art baselines. Notably, the incremental distillation mechanism contributes to improvements of 0.2%-6.5% in the mean reciprocal rank (MRR) score. More exploratory experiments validate the effectiveness of IncDE in proficiently learning new knowledge while preserving old knowledge across all time steps.
Online Conversion Rate Prediction via Multi-Interval Screening and Synthesizing under Delayed Feedback
Authors: Liu Qiming , Ao Xiang , Guo Yuyao , He Qing
Published: March 2024

Due to the widespread adoption of the cost-per-action(CPA) display strategy that demands a real-time conversion rate prediction(CVR), delayed feedback is becoming one of the major challenges in online advertising. As the true labels of a significant quantity of samples are only available after long delays, the observed training data are usually biased, harming the performance of models. Recent studies show integrating models with varying waiting windows to observe true labels is beneficial, but the aggregation framework remains far from reaching a consensus. In this work, we propose the Multi-Interval Screening and Synthesizing model (MISS for short) for online CVR prediction. We first design a multi-interval screening model with various output heads to produce accurate and distinctive estimates. Then a light-weight synthesizing model with an assembled training pipeline is applied to thoroughly exploit the knowledge and relationship among heads, obtaining reliable predictions. Extensive experiments on two real-world advertising datasets validate the effectiveness of our model.
Pairwise-Label-Based Deep Incremental Hashing with Simultaneous Code Expansion
Authors: Wu Dayan , Su Qinghang , Li Bo , Wang Weiping
Published: March 2024

Deep incremental hashing has become a subject of considerable interest due to its capability to learn hash codes in an incremental manner, eliminating the need to generate codes for classes that have already been learned. However, accommodating more classes requires longer hash codes, and regenerating database codes becomes inevitable when code expansion is required.
In this paper, we present a unified deep hash framework that can simultaneously learn new classes and increase hash code capacity. Specifically, we design a triple-channel asymmetric framework to optimize a new CNN model with a target code length and a code projection matrix. This enables us to directly generate hash codes for new images, and efficiently generate expanded hash codes for original database images from the old ones with the learned projection matrix.
Meanwhile, we propose a pairwise-label-based incremental similarity-preserving loss to optimize the new CNN model, which can incrementally preserve new similarities while maintaining the old ones. Additionally, we design a double-end quantization loss to reduce the quantization error from new and original query images. As a result, our method efficiently embeds both new and original similarities into the expanded hash codes, while keeping the original database codes unchanged.
We conduct extensive experiments on three widely-used image retrieval benchmarks, demonstrating that our method can significantly reduce the time required to expand existing database codes, while maintaining state-of-the-art retrieval performance.
Empowering Dual-Level Graph Self-Supervised Pretraining with Motif Discovery
Authors: Yan Pengwei , Song Kaisong , Jiang Zhuoren , Kang Yangyang , Lin Tianqianjin , Sun Changlong , Liu Xiaozhong
Published: March 2024

While self-supervised graph pretraining techniques have shown promising results in various domains, their application still experiences challenges of limited topology learning, human knowledge dependency, and incompetent multi-level interactions. To address these issues, we propose a novel solution, Dual-level Graph self-supervised Pretraining with Motif discovery (DGPM), which introduces a unique dual-level pretraining structure that orchestrates node-level and subgraph-level pretext tasks. Unlike prior approaches, DGPM autonomously uncovers significant graph motifs through an edge pooling module, aligning learned motif similarities with graph kernel-based similarities. A cross-matching task enables sophisticated node-motif interactions and novel representation learning. Extensive experiments on 15 datasets validate DGPM's effectiveness and generalizability, outperforming state-of-the-art methods in unsupervised representation learning and transfer learning settings. The autonomously discovered motifs demonstrate the potential of DGPM to enhance robustness and interpretability.
Graph Neural Networks with Soft Association between Topology and Attribute
Authors: Yang Yachao , Sun Yanfeng , Wang Shaofan , Guo Jipeng , Gao Junbin , Ju Fujiao , Yin Baocai
Published: March 2024

Graph Neural Networks (GNNs) have shown great performance in learning representations for graph-structured data. However, recent studies have found that the interference between topology and attribute can lead to distorted node representations. Most GNNs are designed based on homophily assumptions, thus they cannot be applied to graphs with heterophily. This research critically analyzes the propagation principles of various GNNs and the corresponding challenges from an optimization perspective. A novel GNN called Graph Neural Networks with Soft Association between Topology and Attribute (GNN-SATA) is proposed. Different embeddings are utilized to gain insights into attributes and structures while establishing their interconnections through soft association. Further as integral components of the soft association, a Graph Pruning Module (GPM) and Graph Augmentation Module (GAM) are developed. These modules dynamically remove or add edges to the adjacency relationships to make the model better fit with graphs with homophily or heterophily. Experimental results on homophilic and heterophilic graph datasets convincingly demonstrate that the proposed GNN-SATA effectively captures more accurate adjacency relationships and outperforms state-of-the-art approaches. Especially on the heterophilic graph dataset Squirrel, GNN-SATA achieves a 2.81% improvement in accuracy, utilizing merely 27.19% of the original number of adjacency relationships. Our code is released at https://github.com/wwwfadecom/GNN-SATA.
ROG_PL: Robust Open-Set Graph Learning via Region-Based Prototype Learning
Authors: Zhang Qin , Li Xiaowei , Lu Jiexin , Qiu Liping , Pan Shirui , Chen Xiaojun , Chen Junyang
Published: March 2024

Open-set graph learning is a practical task that aims to classify the known class nodes and to identify unknown class samples as unknowns. Conventional node classification methods usually perform unsatisfactorily in open-set scenarios due to the complex data they encounter, such as out-of-distribution (OOD) data and in-distribution (IND) noise. OOD data are samples that do not belong to any known classes. They are outliers if they occur in training (OOD noise), and open-set samples if they occur in testing. IND noise are training samples which are assigned incorrect labels. The existence of IND noise and OOD noise is prevalent, which usually cause the ambiguity problem, including the intra-class variety problem and the inter-class confusion problem. Thus, to explore robust open-set learning methods is necessary and difficult, and it becomes even more difficult for non-IID graph data. To this end, we propose a unified framework named ROG_PL to achieve robust open-set learning on complex noisy graph data, by introducing prototype learning. In specific, ROG_PL consists of two modules, i.e., denoising via label propagation and open-set prototype learning via regions. The first module corrects noisy labels through similarity-based label propagation and removes low-confidence samples, to solve the intra-class variety problem caused by noise. The second module learns open-set prototypes for each known class via non-overlapped regions and remains both interior and border prototypes to remedy the inter-class confusion problem. The two modules are iteratively updated under the constraints of classification loss and prototype diversity loss. To the best of our knowledge, the proposed ROG_PL is the first robust open-set node classification method for graph data with complex noise. Experimental evaluations of ROG_PL on several benchmark graph datasets demonstrate that it has good performance.
An Efficient Subgraph-Inferring Framework for Large-Scale Heterogeneous Graphs
Authors: Zhou Wei , Huang Hong , Shi Ruize , Yin Kehan , Jin Hai
Published: March 2024

Heterogeneous Graph Neural Networks (HGNNs) play a vital role in advancing the field of graph representation learning by addressing the complexities arising from diverse data types and interconnected relationships in real-world scenarios. However, traditional HGNNs face challenges when applied to large-scale graphs due to the necessity of training or inferring on the entire graph. As the size of the heterogeneous graphs increases, the time and memory overhead required by these models escalates rapidly, even reaching unacceptable levels. To address this issue, in this paper, we present a novel framework named (SubInfer), which conducts training and inferring on subgraphs instead of the entire graphs, hence efficiently handling large-scale heterogeneous graphs. The proposed framework comprises three main steps: 1) partitioning the heterogeneous graph from multiple perspectives to preserve various semantic information, 2) completing the subgraphs to improve the convergence speed of subgraph training and the performance of subgraph inference, and 3) training and inferring the HGNN model on distributed clusters to further reduce the time overhead. The framework is applicable to the vast majority of HGNN models. Experiments on five benchmark datasets demonstrate that SubInfer effectively optimizes the training and inference phase, delivering comparable performance to traditional HGNN models while significantly reducing time and memory overhead.
Envy-Free House Allocation under Uncertain Preferences
Authors: Aziz Haris , Iliffe Isaiah , Li Bo , Ritossa Angus , Sun Ankang , Suzuki Mashbat
Published: March 2024

Envy-freeness is one of the most important fairness concerns when allocating items. We study envy-free house allocation when agents have uncertain preferences over items and consider several well-studied preference uncertainty models. The central problem that we focus on is computing an allocation that has the highest probability of being envy-free. We show that each model leads to a distinct set of algorithmic and complexity results, including detailed results on (in-)approximability. En route, we consider two related problems of checking whether there exists an allocation that is possibly or necessarily envy-free. We give a complete picture of the computational complexity of these two problems for all the uncertainty models we consider.
Enhancing the Efficiency of Altruism and Taxes in Affine Congestion Games through Signalling
Authors: Bilò Vittorio , Vinci Cosimo
Published: March 2024

We address the problem of improving the worst-case efficiency of pure Nash equilibria (aka, the price of anarchy) in affine congestion games, through a novel use of signalling. We assume that, for each player in the game, a most preferred strategy is publicly signalled. This can be done either distributedly by the players themselves, or be the outcome of some centralized algorithm. We apply this signalling scheme to two well-studied scenarios: games with partially altruistic players and games with resource taxation. We show a significant improvement in the price of anarchy of these games, whenever the aggregate signalled strategy profile is a good approximation of the game social optimum.
Stability in Online Coalition Formation
Authors: Bullinger Martin , Romen René
Published: March 2024

Coalition formation is concerned with the question of how to partition a set of agents into disjoint coalitions according to their preferences. Deviating from most of the previous work, we consider an online variant of the problem, where agents arrive in sequence and whenever an agent arrives, they have to be assigned to a coalition immediately and irrevocably. The scarce existing literature on online coalition formation has focused on the objective of maximizing social welfare, a demanding requirement, even in the offline setting. Instead, we seek to achieve stable coalition structures in an online setting, and focus on stability concepts based on deviations by single agents. We present a comprehensive picture in additively separable hedonic games, leading to dichotomies, where positive results are obtained by deterministic algorithms and negative results even hold for randomized algorithms.
Refined Characterizations of Approval-Based Committee Scoring Rules
Authors: Dong Chris , Lederer Patrick
Published: March 2024

In approval-based committee (ABC) elections, the goal is to select a fixed-size subset of the candidates, a so-called committee, based on the voters' approval ballots over the candidates. One of the most popular classes of ABC voting rules are ABC scoring rules, for which voters give points to each committee and the committees with maximal total points are chosen. While the set of ABC scoring rules has recently been characterized in a model where the output is a ranking of all committees, no full characterization of these rules exists in the standard model where a set of winning committees is returned. We address this issue by characterizing two important subclasses of ABC scoring rules in the standard ABC election model, thereby both extending the result for ABC ranking rules to the standard setting and refining it to subclasses. In more detail, by relying on a consistency axiom for variable electorates, we characterize (i) the prominent class of Thiele rules and (ii) a new class of ABC voting rules called ballot size weighted approval voting. Based on these theorems, we also infer characterizations of three well-known ABC voting rules, namely multi-winner approval voting, proportional approval voting, and satisfaction approval voting.
Repeated Fair Allocation of Indivisible Items
Authors: Igarashi Ayumi , Lackner Martin , Nardi Oliviero , Novaro Arianna
Published: March 2024

The problem of fairly allocating a set of indivisible items is a well-known challenge in the field of (computational) social choice. In this scenario, there is a fundamental incompatibility between notions of fairness (such as envy-freeness and proportionality) and economic efficiency (such as Pareto-optimality). However, in the real world, items are not always allocated once and for all, but often repeatedly. For example, the items may be recurring chores to distribute in a household. Motivated by this, we initiate the study of the repeated fair division of indivisible goods and chores, and propose a formal model for this scenario. In this paper, we show that, if the number of repetitions is a multiple of the number of agents, there always exists a sequence of allocations that is proportional and Pareto-optimal. On the other hand, irrespective of the number of repetitions, an envy-free and Pareto-optimal sequence of allocations may not exist. For the case of two agents, we show that if the number of repetitions is even, it is always possible to find a sequence of allocations that is overall envy-free and Pareto-optimal. We then prove even stronger fairness guarantees, showing that every allocation in such a sequence satisfies some relaxation of envy-freeness. Finally, in case that the number of repetitions can be chosen freely, we show that envy-free and Pareto-optimal allocations are achievable for any number of agents.
Strategyproof Mechanisms for Group-Fair Obnoxious Facility Location Problems
Authors: Li Jiaqian , Li Minming , Chan Hau
Published: March 2024

We study the group-fair obnoxious facility location problems from the mechanism design perspective where agents belong to different groups and have private location preferences on the undesirable locations of the facility. Our main goal is to design strategyproof mechanisms that elicit the true location preferences from the agents and determine a facility location that approximately optimizes several group-fair objectives. We first consider the maximum total and average group cost (group-fair) objectives. For these objectives, we propose deterministic mechanisms that achieve 3-approximation ratios and provide matching lower bounds. We then provide the characterization of 2-candidate strategyproof randomized mechanisms. Leveraging the characterization, we design randomized mechanisms with improved approximation ratios of 2 for both objectives. We also provide randomized lower bounds of 5/4 for both objectives. Moreover, we investigate intergroup and intragroup fairness (IIF) objectives, addressing fairness between groups and within each group. We present a mechanism that achieves a 4-approximation for the IIF objectives and provide tight lower bounds.
Beyond Mimicking Under-Represented Emotions: Deep Data Augmentation with Emotional Subspace Constraints for EEG-Based Emotion Recognition
Authors: Zhang Zhi , Zhong Shenghua , Liu Yan
Published: March 2024

In recent years, using Electroencephalography (EEG) to recognize emotions has garnered considerable attention. Despite advancements, limited EEG data restricts its potential. Thus, Generative Adversarial Networks (GANs) are proposed to mimic the observed distributions and generate EEG data. However, for imbalanced datasets, GANs struggle to produce reliable augmentations for under-represented minority emotions by merely mimicking them. Thus, we introduce Emotional Subspace Constrained Generative Adversarial Networks (ESC-GAN) as an alternative to existing frameworks. We first propose the EEG editing paradigm, editing reference EEG signals from well-represented to under-represented emotional subspaces. Then, we introduce diversity-aware and
boundary-aware losses to constrain the augmented subspace. Here, the diversity-aware loss encourages a diverse emotional subspace by enlarging the sample difference, while boundary-aware loss constrains the augmented subspace near the decision boundary where recognition models can be vulnerable. Experiments show ESC-GAN boosts emotion recognition performance on benchmark datasets, DEAP, AMIGOS, and SEED, while protecting against potential adversarial attacks. Finally, the proposed method opens new avenues for editing EEG signals under emotional subspace constraints, facilitating unbiased and secure EEG data augmentation.
BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving
Authors: Liao Haicheng , Li Zhenning , Shen Huanming , Zeng Wenxuan , Liao Dongping , Li Guofa , Xu Chengzhong
Published: March 2024

The ability to accurately predict the trajectory of surrounding vehicles is a critical hurdle to overcome on the journey to fully autonomous vehicles. To address this challenge, we pioneer a novel behavior-aware trajectory prediction model (BAT) that incorporates insights and findings from traffic psychology, human behavior, and decision-making. Our model consists of behavior-aware, interaction-aware, priority-aware, and position-aware modules that perceive and understand the underlying interactions and account for uncertainty and variability in prediction, enabling higher-level learning and flexibility without rigid categorization of driving behavior. Importantly, this approach eliminates the need for manual labeling in the training process and addresses the challenges of non-continuous behavior labeling and the selection of appropriate time windows.
We evaluate BAT's performance across the Next Generation Simulation (NGSIM), Highway Drone (HighD), Roundabout Drone (RounD), and Macao Connected Autonomous Driving (MoCAD) datasets, showcasing its superiority over prevailing state-of-the-art (SOTA) benchmarks in terms of prediction accuracy and efficiency. Remarkably, even when trained on reduced portions of the training data (25%), our model outperforms most of the baselines, demonstrating its robustness and efficiency in predicting vehicle trajectories, and the potential to reduce the amount of data required to train autonomous vehicles, especially in corner cases. In conclusion, the behavior-aware model represents a significant advancement in the development of autonomous vehicles capable of predicting trajectories with the same level of proficiency as human drivers. The project page is available on our GitHub.
BAIT: Benchmarking (Embedding) Architectures for Interactive Theorem-Proving
Authors: Lamont Sean , Norrish Michael , Dezfouli Amir , Walder Christian , Montague Paul
Published: March 2024

Artificial Intelligence for Theorem Proving (AITP) has given
rise to a plethora of benchmarks and methodologies, particularly in Interactive Theorem Proving (ITP). Research in the
area is fragmented, with a diverse set of approaches being
spread across several ITP systems. This presents a significant challenge to the comparison of methods, which are often
complex and difficult to replicate.
Addressing this, we present BAIT, a framework for the fair
and streamlined comparison of learning approaches in ITP.
We demonstrate BAIT’s capabilities with an in-depth comparison, across several ITP benchmarks, of state-of-the-art
architectures applied to the problem of formula embedding.
We find that Structure Aware Transformers perform particularly well, improving on techniques associated with the original problem sets. BAIT also allows us to assess the end-to-end proving performance of systems built on interactive
environments. This unified perspective reveals a novel end-to-end system that improves on prior work. We also provide
a qualitative analysis, illustrating that improved performance
is associated with more semantically-aware embeddings. By
streamlining the implementation and comparison of Machine
Learning algorithms in the ITP context, we anticipate BAIT
will be a springboard for future research.
A General Theoretical Framework for Learning Smallest Interpretable Models
Authors: Ordyniak Sebastian , Paesani Giacomo , Rychlicki Mateusz , Szeider Stefan
Published: March 2024

We develop a general algorithmic framework that allows us to obtain fixed-parameter tractability for computing smallest symbolic models that represent given data. Our framework applies to all ML model types that admit a certain extension property. By showing this extension property for decision trees, decision sets, decision lists, and binary decision diagrams, we obtain that minimizing these fundamental model types is fixed-parameter tractable. Our framework even applies to ensembles, which combine individual models by majority decision.
NegVSR: Augmenting Negatives for Generalized Noise Modeling in Real-world Video Super-Resolution
Authors: Song Yexing , Wang Meilin , Yang Zhijing , Xian Xiaoyu , Shi Yukai
Published: March 2024

The capability of video super-resolution (VSR) to synthesize high-resolution (HR) video from ideal datasets has been demonstrated in many works. However, applying the VSR model to real-world video with unknown and complex degradation remains a challenging task. First, existing degradation metrics in most VSR methods are not able to effectively simulate real-world noise and blur. On the contrary, simple combinations of classical degradation are used for real-world noise modeling, which led to the VSR model often being violated by out-of-distribution noise. Second, many SR models focus on noise simulation and transfer. Nevertheless, the sampled noise is monotonous and limited. To address the aforementioned problems, we propose a Negatives augmentation strategy for generalized noise modeling in Video Super-Resolution (NegVSR) task. Specifically, we first propose sequential noise generation toward real-world data to extract practical noise sequences. Then, the degeneration domain is widely expanded by negative augmentation to build up various yet challenging real-world noise sets. We further propose the augmented negative guidance loss to learn robust features among augmented negatives effectively. Extensive experiments on real-world datasets (e.g., VideoLQ and FLIR) show that our method outperforms state-of-the-art methods with clear margins, especially in visual quality. Project page is available at: https://negvsr.github.io/.
FLOW BOILING HEAT TRANSFER IN MICROCHANNEL HEAT EXCHANGERS WITH MICRO POROUS COATING SURFACE
Authors: Sung Kuan-Fu , Chang I-Chuan , Yang Chien-Yuh
Published: March 2024

This study experimentally investigated the heat transfer and pressure drop performance of refrigerant HFC-245fa flow boiling in microchannel heat exchangers with and without microporous coating. The flow boiling heat transfer performance at various mass fluxes, heating rate, exit vapor qualities, and surface coating thickness was compared. The test results showed that the 52 μm coating thickness porous surface exhibited 65-148% higher heat transfer performance than the smooth surface, while the 98 μm microporous coating surface showed a lower improvement of 41-90% at various mass fluxes. Partial dry out was observed at high and moderate mass fluxes on both smooth and porous coating channels. It happened at lower exit vapor qualities in microporous coating channels than that in smooth channels. The partial dry-out exit vapor qualities increased with decreasing mass fluxes. For the lowest mass flux, because of the low heat flux and low nucleation suppression, no significant partial dry out was investigated. The pressure drops in microporous coating channels were around 25-47% higher than those in smooth channels. There was no significant influence of microporous coating layer thickness on flow boiling pressures drops.
VISION-BASED MODAL ANALYSIS OF MACHINE TOOL SYSTEMS: PROGRESS AND PROSPECTS
Authors: Law Mohit
Published: March 2024

Modal analysis of machine tools involves estimating natural frequencies, damping ratios, and mode shapes from the vibratory response of the machine tool. Usually, modal hammers and shakers are used to excite, and accelerometers or laser vibrometers are used to measure the response. Though these procedures have become routine, sometimes the use of accelerometers can result in mass-loading that distorts the response, and though laser vibrometers are non-contact, their use is precluded by their high costs. To counter these issues, vision-based modal analysis methods have emerged as a viable and promising alternative. The spatiotemporal response is estimated by treating every pixel in every frame in the video of the vibrating machine as a motion sensor. Image processing schemes leveraged from developments in allied fields are then used to register motion from video. The method is noncontact, full field, and only needs a camera and post-processing on a computer, and as such, it offers advantages over the traditional measurement methods. Since vision-based methods are potentially paradigm-shifting, this paper reviews the recent progress to contextualize the prospects of the method. The review includes discussions on selection considerations of cameras and acquisition parameters, on using markers and the machine's own features to register motion, on the efficacy of different motion registration schemes, and workarounds for when motion is spatiotemporally aliased. The paper concludes by discussing challenges and prospects related to motion synchronization, measuring speed and time-varying dynamics, and technological trends that may aid the adoption of the method.
Experimental study on the influence of cyclic loading frequency on liquefaction characteristics of saturated sand
Authors: Xu Chengshun , Yue Chong , Du Xiuli , Liang Ke , Wang Bing , Chen Guoxing
Published: March 2024

A series of undrained cyclic torsional shear tests was conducted to investigate the effect of cyclic loading frequency on the liquefaction characteristics of saturated sand using a hollow cylinder apparatus. The test results show that the dilative and contractive tendencies of various saturated sands are not only related to the physical properties of the sand, but are also affected by loading frequency. Under low-frequency loading, the saturated sand has a dilative behaviour, excess pore water pressure fluctuates after initial liquefaction and the soil maintains the ability to resist liquefaction to some extent after the initial liquefaction. The liquefaction mode in terms of the stress–strain relationship generally performs as the ‘cyclic mobility’. However, under high-frequency loading, the saturated sand has a contractive behaviour, and excess pore water pressure generally remains stable after the initial liquefaction. The liquefaction mode in terms of stress–strain relationship generally exhibits as ‘cyclic instability’. The deformation caused by low-frequency loading is significantly larger compared with that caused by high-frequency loading. At higher loading frequencies, the phase transformation stress ratio increases with the increase of loading frequency, and gradually approaches the failure stress ratio.
Gweithio tuag at ddiagnosis o alluoedd llythrennedd plant dwyieithog: Rhai ystyriaethau allweddol i athrawon yng Nghymru
Authors: Thomas Enlli , Owen Carla Marie , Hunt Ffion , Young Nia , Dafydd Morgan , Fontaine Lise , Aldridge-Waddon Michelle , Sullivan Kirk , Lloyd-Williams Sian Wynn , ap Gruffudd Gwilym Siôn , Caulfield Gareth
Published: March 2024

Mae un maes sy’n peri pryder o fewn cyd-destun dwyieithog yn ymwneud â’r asesiad ‘diagnostig’ priodol o alluoedd iaith plant dwyieithog a chymhwyso eu canlyniadau asesu’n briodol mewn ymarfer. Mae anawsterau cyfathrebu ac iaith yn niferus a chymhleth, ac yn amlygu eu hunain mewn amrywiaeth o ffyrdd sy’n cael eu cofnodi i wahanol raddau drwy brofion safonedig. Mae adnoddau o’r fath ar gael yn rhwydd – yn aml mewn sawl ffurf – mewn rhai ieithoedd, megis Saesneg, ond nid ydynt ar gael mor rhwydd mewn ieithoedd eraill. Mae hyn yn arbennig o wir yn achos ieithoedd lleiafrifol fel y Gymraeg, ac mae hyn yn peri anhawster mawr wrth geisio cynnal math penodol o asesiad o alluoedd iaith penodol. Mae’r papur hwn yn amlinellu cyflwr cyfredol adnoddau asesu diagnostig ar gyfer y Gymraeg, gan ganolbwyntio’n benodol ar fesuriadau o alluoedd llythrennedd. Gan fanteisio ar dystiolaeth ymchwil o’r cyd-destun Cymreig, rydym yn dadlau o blaid hyfforddi addysgwyr yn briodol yn y maes hwn, a thros yr angen brys i ddatblygu adnoddau sy’n benodol i iaith a chyd-destun, gyda normau siaradwyr dwyieithog perthnasol, sydd â defnydd ymarferol yn yr ystafell ddosbarth, i sicrhau diagnosis a chefnogaeth deg a pherthnasol i bob plentyn sy’n cael ei addysgu yng Nghymru.
Wipe the table clean – German speakers construe telicity differently in adjectival resultatives and transitives
Authors: Weicker Merle , Heßler-Reusch Lea , Schulz Petra
Published: March 2024

Change of state expressions involving weak-endstate verbs such as wipe and drink exhibit variation in telicity. The present study is the first to experimentally investigate change of state expressions referred to as adjectival control resultatives (e.g., wipe the table clean) and the corresponding simple transitives (e.g., wipe the table) regarding their telicity readings. Based on their semantic properties, we ask whether German speakers interpret adjectival resultatives as telic and simple transitives as fluctuating between telic and atelic. Following an event-structural approach to telicity, we also seek to contribute to the issue of which event-semantic properties adjectival resultatives and simple transitives share: if it is the adjective that evokes a telic interpretation, then the possible telic interpretation of simple transitives must arise by different means, suggesting a non-uniform construal of telicity. Twenty-one adults participated in a Truth-Value Judgment Task that varied Structure (resultative/transitive) and Event type (complete/incomplete). Generalized mixed effects logistic regression revealed a significant interaction between Event type and Structure. For complete events, acceptance was at ceiling for both structures. For incomplete events, acceptance was significantly lower for resultatives than for simple transitives. These results indicate that in adjectival resultatives telicity is construed semantically – via entailment – and in the corresponding simple transitives via a pragmatic inference, which is not computed very often. We propose that this type of simple transitives designates process events, exactly like their intransitive variant. Resultatives designate a transition event: the adjective and the main verb form a complex predicate; adjectives with closed scales like clean encode the prominent endstate subevent and the verb encodes the process subevent.
Fazla Kilolu ve Obez Öğencilere Okul Temelli Oyun ve Eğitim Müdahalesinin Kilo Vermeye Etkisinin Karşılaştırılması: Randomize Kontrollü Deneysel Bir Çalışma
Authors: Çevik Cahide , Örsal Özlem , Amaro Salvatore
Published: March 2024

Amaç: Fazla kilolu ve obez çocuklarda Kaledo oyununun ve sağlık inanç modeline göre hazırlanmış beslenme eğitiminin kilo vermeye beslenme özyeterliğine, tutumuna ve davranışına etkisini karşılaştırmaktır.

Yöntemler: Bu araştırma randomize kontrollü deneysel çalışmadır. Araştırmada oyun (n=38), eğitim (n=35) ve kontrol (n=53) olmak üzere üç grup yer aldı. Araştırma başlangıcında, oyun ve eğitim müdahalelerin sonunda her üç grupta boy ve kilo ölçümleri yapılarak beden kitle indeksi değerleri hesaplandı ve ölçekler uygulandı.

Bulgular: Beden kitle indeksi z skoru değerlerinin eğitim grubunda eğitim sonrasında öncesine göre daha düşük olduğu tespit edildi (P<0,05). Beslenme tutum puanları gruplar arası farklılık göstermezken, grup içi değerlendirmelerde oyun grubundaki öğrencilerin oyun müdahalesi sonrası öncesine göre daha olumlu beslenme tutumu sergilediği tespit edildi (P<0,05).

Sonuç : Eğitim sonrasında öğrencilerin BKİ z puanlarında düşüş gözlendi. Oyun sonrası öğrenciler daha olumlu beslenme tutumuna sahip olmuştur. Kaledo oyununun etkisini ve Sağlık İnanç Modeli'ne göre hazırlanan beslenme eğitimi ile arasındaki farkı daha net ortaya koyabilecek daha uzun süreli, daha geniş örneklemli deneysel çalışmalara ihtiyaç
vardır.
Why so few Ps become As: The character, choices and challenges of South Africa’s most talented young researchers
Authors: Jansen Jonathan , Walters Cyrill , White Alistair , Mehl Greame
Published: March 2024

In this research article, we examine why so few of the most promising young scientists in South Africa, those who obtain a P or ‘prestigious’ rating from the South African National Research Foundation, go on to obtain an A rating, i.e. become leading international scholars in their fields. Drawing on in-depth interviews with 36 P-rated scientists in the period 1983 to 2022, we found that personal characteristics (such as work ethic), the right international networks, and strong departmental support are among the key factors that explain the research accomplishments of highly successful achievers.
String Viscous Fluid Cosmological Model in the Framework of Sen-Dunn Theory of Gravitation
Authors:   Department of Mathematics, Shri Shivaji Education Society, Amravati’s Science College, Congress Nagar, Nagpur 440012, Maharashtra, India, Lambat P M , Pund A M
Published: March 2024

Objective: To present a new solution to the field equations for higher-dimensional Bianchi type-III string viscous fluid cosmological model in the context of the Sen-Dunn theory of gravity. Methods: To obtain definite solution of the field equations, we consider a power law relation between the scale factor and scalar field, and we take into account two distinct scale factors, and , in which , are positive constants. This produces a time-dependent deceleration parameter. We analyse the model with constant bulk viscosity constant to explain the current accelerated expansion of the universe. Findings: Spatial volume of the model increases with cosmic time, which shows that universe is spatially expanding. Also, the model possesses a point-type singularity. It is noted that these models depict the universe's accelerated expansion. Novelty: We obtained new solution to the field equations for higher dimensional Bianchi type-III generated by means of a cloud of strings with bulk viscous fluid in Sen-Dunn theory by using quadratic form. Keywords: Bianchi type-III, Cosmic string, Bulk viscosity, Sen-Dunn Theory