OpenRaiser/Intern-Atlas huggingface dataset:
Deep Learning within Tabular Data: Foundations, Challenges, Advances and Future Directions
Venue: arXiv cs.LG 2025
Year: 2025

Tabular data remains one of the most prevalent data types across a wide range of real-world applications, yet effective representation learning for this domain poses unique challenges due to its irregular patterns, heterogeneous feature distributions, and complex inter-column dependencies. This survey provides a comprehensive review of state-of-the-art techniques in tabular data representation learning, structured around three foundational design elements: training data, neural architectures, and learning objectives. Unlike prior surveys that focus primarily on either architecture design or learning strategies, we adopt a holistic perspective that emphasizes the universality and robustness of representation learning methods across diverse downstream tasks. We examine recent advances in data augmentation and generation, specialized neural network architectures tailored to tabular data, and innovative learning objectives that enhance representation quality. Additionally, we highlight the growing influence of self-supervised learning and the adaptation of transformer-based foundation models for tabular data. Our review is based on a systematic literature search using rigorous inclusion criteria, encompassing 127 papers published since 2020 in top-tier conferences and journals. Through detailed analysis and comparison, we identify emerging trends, critical gaps, and promising directions for future research, aiming to guide the development of more generalizable and effective tabular data representation methods.
Advanced Tutorial: Label-Efficient Two-Sample Tests
Venue: arXiv cs.LG 2025
Year: 2025

Hypothesis testing is a statistical inference approach used to determine whether data supports a specific hypothesis. An important type is the two-sample test, which evaluates whether two sets of data points are from identical distributions. This test is widely used, such as by clinical researchers comparing treatment effectiveness. This tutorial explores two-sample testing in a context where an analyst has many features from two samples, but determining the sample membership (or labels) of these features is costly. In machine learning, a similar scenario is studied in active learning. This tutorial extends active learning concepts to two-sample testing within this \textit{label-costly} setting while maintaining statistical validity and high testing power. Additionally, the tutorial discusses practical applications of these label-efficient two-sample tests.
Neural Cellular Automata and Deep Equilibrium Models
Venue: arXiv cs.NE 2025
Year: 2025

This essay discusses the connections and differences between two emerging paradigms in deep learning, namely Neural Cellular Automata and Deep Equilibrium Models, and train a simple Deep Equilibrium Convolutional model to demonstrate the inherent similarity of NCA and DEQ based methods. Finally, this essay speculates about ways to combine theoretical and practical aspects of both approaches for future research.
BASIC: Semi-supervised Multi-organ Segmentation with Balanced Subclass Regularization and Semantic-conflict Penalty
Venue: arXiv cs.CV 2025
Year: 2025

Semi-supervised learning (SSL) has shown notable potential in relieving the heavy demand of dense prediction tasks on large-scale well-annotated datasets, especially for the challenging multi-organ segmentation (MoS). However, the prevailing class-imbalance problem in MoS caused by the substantial variations in organ size exacerbates the learning difficulty of the SSL network. To address this issue, in this paper, we propose an innovative semi-supervised network with BAlanced Subclass regularIzation and semantic-Conflict penalty mechanism (BASIC) to effectively learn the unbiased knowledge for semi-supervised MoS. Concretely, we construct a novel auxiliary subclass segmentation (SCS) task based on priorly generated balanced subclasses, thus deeply excavating the unbiased information for the main MoS task with the fashion of multi-task learning. Additionally, based on a mean teacher framework, we elaborately design a balanced subclass regularization to utilize the teacher predictions of SCS task to supervise the student predictions of MoS task, thus effectively transferring unbiased knowledge to the MoS subnetwork and alleviating the influence of the class-imbalance problem. Considering the similar semantic information inside the subclasses and their corresponding original classes (i.e., parent classes), we devise a semantic-conflict penalty mechanism to give heavier punishments to the conflicting SCS predictions with wrong parent classes and provide a more accurate constraint to the MoS predictions. Extensive experiments conducted on two publicly available datasets, i.e., the WORD dataset and the MICCAI FLARE 2022 dataset, have verified the superior performance of our proposed BASIC compared to other state-of-the-art methods.
Dynamic Authentication and Granularized Authorization with a Cross-Domain Zero Trust Architecture for Federated Learning in Large-Scale IoT Networks
Venue: arXiv cs.NI 2025
Year: 2025

With the increasing number of connected devices and complex networks involved, current domain-specific security techniques become inadequate for diverse large-scale Internet of Things (IoT) systems applications. While cross-domain authentication and authorization brings lots of security improvement, it creates new challenges of efficiency and security. Zero trust architecture (ZTA), an emerging network security architecture, offers a more granular and robust security environment for IoT systems. However, extensive cross-domain data exchange in ZTA can cause reduced authentication and authorization efficiency and data privacy concerns. Therefore, in this paper, we propose a dynamic authentication and granularized authorization scheme based on ZTA integrated with decentralized federated learning (DFL) for cross-domain IoT networks. Specifically, device requests in the cross-domain process are continuously monitored and evaluated, and only necessary access permissions are granted. To protect user data privacy and reduce latency, we integrate DFL with ZTA to securely and efficiently share device data across different domains. Particularly, the DFL model is compressed to reduce the network transmission load. Meanwhile, a dynamic adaptive weight adjustment mechanism is proposed to enable the DFL model to adapt to data characteristics from different domains. We analyze the performance of the proposed scheme in terms of security proof, including confidentiality, integrity and availability. Simulation results demonstrate the superior performance of the proposed scheme in terms of lower latency and higher throughput compared to other existing representative schemes.
Composing Data Stories with Meta Relations
Venue: arXiv cs.HC 2025
Year: 2025

To facilitate the creation of compelling and engaging data stories, AI-powered tools have been introduced to automate the three stages in the workflow: analyzing data, organizing findings, and creating visuals. However, these tools rely on data-level information to derive inflexible relations between findings. Therefore, they often create one-size-fits-all data stories. Differently, our formative study reveals that humans heavily rely on meta relations between these findings from diverse domain knowledge and narrative intent, going beyond datasets, to compose their findings into stylized data stories. Such a gap indicates the importance of introducing meta relations to elevate AI-created stories to a satisfactory level. Though necessary, it is still unclear where and how AI should be involved in working with humans on meta relations. To answer the question, we conducted an exploratory user study with Remex, an AI-powered data storytelling tool that suggests meta relations in the analysis stage and applies meta relations for data story organization. The user study reveals various findings about introducing AI for meta relations into the storytelling workflow, such as the benefit of considering meta relations and their diverse expected usage scenarios. Finally, the paper concludes with lessons and suggestions about applying meta relations to compose data stories to hopefully inspire future research.
Is social media hindering or helping Academic Performance? A case study of Walter Sisulu University Buffalo City Campus
Venue: arXiv cs.CY 2025
Year: 2025

Social media platforms are popular among higher education students and have seen increased usage for academic purposes, especially during the COVID-19 pandemic. However, excessive use of social media can negatively impact students' academic performance. This preliminary study examines social media's impact on students' academic performance at Walter Sisulu University (WSU), Buffalo City campus. Using a positivist paradigm and a quantitative approach, randomly sampled data were collected from 71 students through a survey to identify trends and generate preliminary insights. Results indicate that while social media can facilitate academic work, it predominantly acts as a distraction, negatively affecting academic performance, particularly for first-year students. Notably, 84.5% of the students spend more than four hours daily on social media, and 39.4% agree that it negatively impacts their assignment completion. The study underscores the need for students to balance their social media use and academic responsibilities, highlighting the importance of this issue. Recommendations for achieving this balance, such as adopting time management strategies and integrating social media into teaching methodologies, are discussed.
LlaMADRS: Prompting Large Language Models for Interview-Based Depression Assessment
Venue: arXiv cs.HC 2025
Year: 2025

This study introduces LlaMADRS, a novel framework leveraging open-source Large Language Models (LLMs) to automate depression severity assessment using the Montgomery-Asberg Depression Rating Scale (MADRS). We employ a zero-shot prompting strategy with carefully designed cues to guide the model in interpreting and scoring transcribed clinical interviews. Our approach, tested on 236 real-world interviews from the Context-Adaptive Multimodal Informatics (CAMI) dataset, demonstrates strong correlations with clinician assessments. The Qwen 2.5--72b model achieves near-human level agreement across most MADRS items, with Intraclass Correlation Coefficients (ICC) closely approaching those between human raters. We provide a comprehensive analysis of model performance across different MADRS items, highlighting strengths and current limitations. Our findings suggest that LLMs, with appropriate prompting, can serve as efficient tools for mental health assessment, potentially increasing accessibility in resource-limited settings. However, challenges remain, particularly in assessing symptoms that rely on non-verbal cues, underscoring the need for multimodal approaches in future work.
A case study on the transformative potential of AI in software engineering on LeetCode and ChatGPT
Venue: arXiv cs.DB 2025
Year: 2025

The recent surge in the field of generative artificial intelligence (GenAI) has the potential to bring about transformative changes across a range of sectors, including software engineering and education. As GenAI tools, such as OpenAI's ChatGPT, are increasingly utilised in software engineering, it becomes imperative to understand the impact of these technologies on the software product. This study employs a methodological approach, comprising web scraping and data mining from LeetCode, with the objective of comparing the software quality of Python programs produced by LeetCode users with that generated by GPT-4o. In order to gain insight into these matters, this study addresses the question whether GPT-4o produces software of superior quality to that produced by humans. The findings indicate that GPT-4o does not present a considerable impediment to code quality, understandability, or runtime when generating code on a limited scale. Indeed, the generated code even exhibits significantly lower values across all three metrics in comparison to the user-written code. However, no significantly superior values were observed for the generated code in terms of memory usage in comparison to the user code, which contravened the expectations. Furthermore, it will be demonstrated that GPT-4o encountered challenges in generalising to problems that were not included in the training data set. This contribution presents a first large-scale study comparing generated code with human-written code based on LeetCode platform based on multiple measures including code quality, code understandability, time behaviour and resource utilisation. All data is publicly available for further research.
On the Locality of Hall's Theorem
Venue: arXiv cs.DS 2025
Year: 2025

The last five years of research on distributed graph algorithms have seen huge leaps of progress, both regarding algorithmic improvements and impossibility results: new strong lower bounds have emerged for many central problems and exponential improvements over the state of the art have been achieved for the runtimes of many algorithms. Nevertheless, there are still large gaps between the best known upper and lower bounds for many important problems. The current lower bound techniques for deterministic algorithms are often tailored to obtaining a logarithmic bound and essentially cannot be used to prove lower bounds beyond $Ω(\log n)$. In contrast, the best deterministic upper bounds are often polylogarithmic, raising the fundamental question of how to resolve the gap between logarithmic lower and polylogarithmic upper bounds and finally obtain tight bounds. We develop a novel algorithm design technique aimed at closing this gap. In essence, each node finds a carefully chosen local solution in $O(\log n)$ rounds and we guarantee that this solution is consistent with the other nodes' solutions without coordination. The local solutions are based on a distributed version of Hall's theorem that may be of independent interest and motivates the title of this work. We showcase our framework by improving on the state of the art for the following fundamental problems: edge coloring, bipartite saturating matchings and hypergraph sinkless orientation. In particular, we obtain an asymptotically optimal $O(\log n)$-round algorithm for $3Δ/2$-edge coloring in bounded degree graphs. The previously best bound for the problem was $O(\log^4 n)$ rounds, obtained by plugging in the state-of-the-art maximal independent set algorithm from arXiv:2303.16043 into the $3Δ/2$-edge coloring algorithm from arXiv:1711.05469 .
Dimension-Free Parameterized Approximation Schemes for Hybrid Clustering
Venue: arXiv cs.DS 2025
Year: 2025

Hybrid $k$-Clustering is a model of clustering that generalizes two of the most widely studied clustering objectives: $k$-Center and $k$-Median. In this model, given a set of $n$ points $P$, the goal is to find $k$ centers such that the sum of the $r$-distances of each point to its nearest center is minimized. The $r$-distance between two points $p$ and $q$ is defined as $\max\{d(p, q)-r, 0\}$ -- this represents the distance of $p$ to the boundary of the $r$-radius ball around $q$ if $p$ is outside the ball, and $0$ otherwise. This problem was recently introduced by Fomin et al. [APPROX 2024], who designed a $(1+\varepsilon, 1+\varepsilon)$-bicrtieria approximation that runs in time $2^{(kd/\varepsilon)^{O(1)}} \cdot n^{O(1)}$ for inputs in $\mathbb{R}^d$; such a bicriteria solution uses balls of radius $(1+\varepsilon)r$ instead of $r$, and has a cost at most $1+\varepsilon$ times the cost of an optimal solution using balls of radius $r$. In this paper we significantly improve upon this result by designing an approximation algorithm with the same bicriteria guarantee, but with running time that is FPT only in $k$ and $\varepsilon$ -- crucially, removing the exponential dependence on the dimension $d$. This resolves an open question posed in their paper. Our results extend further in several directions. First, our approximation scheme works in a broader class of metric spaces, including doubling spaces, minor-free, and bounded treewidth metrics. Secondly, our techniques yield a similar bicriteria FPT-approximation schemes for other variants of Hybrid $k$-Clustering, e.g., when the objective features the sum of $z$-th power of the $r$-distances. Finally, we also design a coreset for Hybrid $k$-Clustering in doubling spaces, answering another open question from the work of Fomin et al.
Local Compositional Complexity: How to Detect a Human-readable Messsage
Venue: arXiv cs.CV 2025
Year: 2025

Data complexity is an important concept in the natural sciences and related areas, but lacks a rigorous and computable definition. In this paper, we focus on a particular sense of complexity that is high if the data is structured in a way that could serve to communicate a message. In this sense, human speech, written language, drawings, diagrams and photographs are high complexity, whereas data that is close to uniform throughout or populated by random values is low complexity. We describe a general framework for measuring data complexity based on dividing the shortest description of the data into a structured and an unstructured portion, and taking the size of the former as the complexity score. We outline an application of this framework in statistical mechanics that may allow a more objective characterisation of the macrostate and entropy of a physical system. Then, we derive a more precise and computable definition geared towards human communication, by proposing local compositionality as an appropriate specific structure. We demonstrate experimentally that this method can distinguish meaningful signals from noise or repetitive signals in auditory, visual and text domains, and could potentially help determine whether an extra-terrestrial signal contained a message.
A Diversity-Enhanced Knowledge Distillation Model for Practical Math Word Problem Solving
Venue: arXiv cs.CL 2025
Year: 2025

Math Word Problem (MWP) solving is a critical task in natural language processing, has garnered significant research interest in recent years. Various recent studies heavily rely on Seq2Seq models and their extensions (e.g., Seq2Tree and Graph2Tree) to generate mathematical equations. While effective, these models struggle to generate diverse but counterpart solution equations, limiting their generalization across various math problem scenarios. In this paper, we introduce a novel Diversity-enhanced Knowledge Distillation (DivKD) model for practical MWP solving. Our approach proposes an adaptive diversity distillation method, in which a student model learns diverse equations by selectively transferring high-quality knowledge from a teacher model. Additionally, we design a diversity prior-enhanced student model to better capture the diversity distribution of equations by incorporating a conditional variational auto-encoder. Extensive experiments on {four} MWP benchmark datasets demonstrate that our approach achieves higher answer accuracy than strong baselines while maintaining high efficiency for practical applications.
Action Quality Assessment via Hierarchical Pose-guided Multi-stage Contrastive Regression
Venue: arXiv cs.CV 2025
Year: 2025

Action Quality Assessment (AQA), which aims at automatic and fair evaluation of athletic performance, has gained increasing attention in recent years. However, athletes are often in rapid movement and the corresponding visual appearance variances are subtle, making it challenging to capture fine-grained pose differences and leading to poor estimation performance. Furthermore, most common AQA tasks, such as diving in sports, are usually divided into multiple sub-actions, each of which contains different durations. However, existing methods focus on segmenting the video into fixed frames, which disrupts the temporal continuity of sub-actions resulting in unavoidable prediction errors. To address these challenges, we propose a novel action quality assessment method through hierarchically pose-guided multi-stage contrastive regression. Firstly, we introduce a multi-scale dynamic visual-skeleton encoder to capture fine-grained spatio-temporal visual and skeletal features. Then, a procedure segmentation network is introduced to separate different sub-actions and obtain segmented features. Afterwards, the segmented visual and skeletal features are both fed into a multi-modal fusion module as physics structural priors, to guide the model in learning refined activity similarities and variances. Finally, a multi-stage contrastive learning regression approach is employed to learn discriminative representations and output prediction results. In addition, we introduce a newly-annotated FineDiving-Pose Dataset to improve the current low-quality human pose labels. In experiments, the results on FineDiving and MTL-AQA datasets demonstrate the effectiveness and superiority of our proposed approach. Our source code and dataset are available at https://github.com/Lumos0507/HP-MCoRe.
SALE-Based Offline Reinforcement Learning with Ensemble Q-Networks
Venue: arXiv cs.LG 2025
Year: 2025

In this work, we build upon the offline reinforcement learning algorithm TD7, which incorporates State-Action Learned Embeddings (SALE) and a prioritized experience replay buffer (LAP). We propose a model-free actor-critic algorithm that integrates ensemble Q-networks and a gradient diversity penalty from EDAC. The ensemble Q-networks introduce penalties to guide the actor network toward in-distribution actions, effectively addressing the challenge of out-of-distribution actions. Meanwhile, the gradient diversity penalty encourages diverse Q-value gradients, further suppressing overestimation for out-of-distribution actions. Additionally, our method retains an adjustable behavior cloning (BC) term that directs the actor network toward dataset actions during early training stages, while gradually reducing its influence as the precision of the Q-ensemble improves. These enhancements work synergistically to improve the stability and precision of the training. Experimental results on the D4RL MuJoCo benchmarks demonstrate that our algorithm achieves higher convergence speed, stability, and performance compared to existing methods.
MAJL: A Model-Agnostic Joint Learning Framework for Music Source Separation and Pitch Estimation
Venue: arXiv cs.SD 2025
Year: 2025

Music source separation and pitch estimation are two vital tasks in music information retrieval. Typically, the input of pitch estimation is obtained from the output of music source separation. Therefore, existing methods have tried to perform these two tasks simultaneously, so as to leverage the mutually beneficial relationship between both tasks. However, these methods still face two critical challenges that limit the improvement of both tasks: the lack of labeled data and joint learning optimization. To address these challenges, we propose a Model-Agnostic Joint Learning (MAJL) framework for both tasks. MAJL is a generic framework and can use variant models for each task. It includes a two-stage training method and a dynamic weighting method named Dynamic Weights on Hard Samples (DWHS), which addresses the lack of labeled data and joint learning optimization, respectively. Experimental results on public music datasets show that MAJL outperforms state-of-the-art methods on both tasks, with significant improvements of 0.92 in Signal-to-Distortion Ratio (SDR) for music source separation and 2.71% in Raw Pitch Accuracy (RPA) for pitch estimation. Furthermore, comprehensive studies not only validate the effectiveness of each component of MAJL, but also indicate the great generality of MAJL in adapting to different model architectures.