--We present eACGM, a full-stack AI/ML system monitoring framework based on eBPF . Additionally, it leverages libnvml to gather process-level GPU resource usage information. By applying a Gaussian Mixture Model (GMM) to the collected multidimensional performance metrics for statistical modeling and clustering analysis, eACGM effectively identifies complex failure modes, such as latency anomalies, hardware failures, and communication inefficiencies, enabling rapid diagnosis of system bottlenecks and abnormal behaviors. T o evaluate eACGM's effectiveness and practicality, we conducted extensive empirical studies and case analyses in multi-node distributed training scenarios. The results demonstrate that eACGM, while maintaining a non-intrusive and low-overhead profile, successfully captures critical performance anomalies during model training and inference.

Detecting structural change in dynamic network data has wide-ranging applications. Existing approaches typically divide the data into time bins, extract network features within each bin, and then compare these features over time. This introduces an inherent tradeoff between temporal resolution and the statistical stability of the extracted features. Despite this tradeoff, reminiscent of time-frequency tradeoffs in signal processing, most methods rely on a fixed temporal resolution. Choosing an appropriate resolution parameter is typically difficult and can be especially problematic in domains like cybersecurity, where anomalous behavior may emerge at multiple time scales. We address this challenge by proposing ANIE (Adaptive Network Intensity Estimation), a multi-resolution framework designed to automatically identify the time scales at which network structure evolves, enabling the joint detection of both rapid and gradual changes. Modeling interactions as Poisson processes, our method proceeds in two steps: (1) estimating a low-dimensional subspace of node behavior, and (2) deriving a set of novel empirical affinity coefficients that quantify change in interaction intensity between latent factors and support statistical testing for structural change across time scales. We provide theoretical guarantees for subspace estimation and the asymptotic behavior of the affinity coefficients, enabling model-based change detection. Experiments on synthetic networks show that ANIE adapts to the appropriate time resolution and is able to capture sharp structural changes while remaining robust to noise. Furthermore, applications to real-world data showcase the practical benefits of ANIE's multiresolution approach to detecting structural change over fixed resolution methods.

Context. The outer Milky Way has a lower metallicity than our solar neighbourhood, but still many molecules are detected in the region. Molecular line ratios can serve as probes to better understand the chemistry and physics in these regions. Aims. We use interpretable machine learning to study 9 different molecular ratios, helping us understand the forward connection between the physics of these environments and the carbon and oxygen chemistries. Methods. Using a large grid of astrochemical models generated using UCLCHEM, we study the properties of molecular clouds of low oxygen and carbon initial abundance. We first try to understand the line ratios using a classical analysis. We then move on to using interpretable machine learning, namely Shapley Additive Explanations (SHAP), to understand the higher order dependencies of the ratios over the entire parameter grid. Lastly we use the Uniform Manifold Approximation and Projection technique (UMAP) as a reduction method to create intuitive groupings of models. Results. We find that the parameter space is well covered by the line ratios, allowing us to investigate all input parameters. SHAP analysis shows that the temperature and density are the most important features, but the carbon and oxygen abundances are important in parts of the parameter space. Lastly, we find that we can group different types of ratios using UMAP. Conclusions. We show the chosen ratios are mostly sensitive to changes in the carbon initial abundance, together with the temperature and density. Especially the CN/HCN and HNC/HCN ratio are shown to be sensitive to the initial carbon abundance, making them excellent probes for this parameter. Out of the ratios, only CS/SO shows a sensitivity to the oxygen abundance.

Domain-adaptive continual pretraining (DAPT) is a state-of-the-art technique that further trains a language model (LM) on its pretraining task, e.g., masked language modeling (MLM), when common domain adaptation via LM fine-tuning is not possible due to a lack of labeled task data. Although popular, MLM requires a significant corpus of domain-related data, which is difficult to obtain for specific domains in languages other than English, such as the process industry in the German language. This paper introduces an efficient approach called ICL-augmented pretraining or ICL-APT that leverages in-context learning (ICL) and k-nearest neighbors (kNN) to augment target data with domain-related and in-domain texts, significantly reducing GPU time while maintaining strong model performance. Our results show that the best configuration of ICL-APT performed better than the state-of-the-art DAPT by 28.7% (7.87 points) and requires almost 4 times less GPU-computing time, providing a cost-effective solution for industries with limited computational capacity.

A structural optimization scheme for a single-layer nonnegative adaptive tensor tree (NATT) that models a target probability distribution is proposed as an alternative paradigm for generative modeling. The NATT scheme, by construction, automatically searches for a tree structure that best fits a given discrete dataset whose features serve as inputs, and has the advantage that it is interpretable as a probabilistic graphical model. We consider the NATT scheme and a recently proposed Born machine adaptive tensor tree (BMATT) optimization scheme and demonstrate their effectiveness on a variety of generative modeling tasks where the objective is to infer the hidden structure of a provided dataset. Our results show that in terms of minimizing the negative log-likelihood, the single-layer scheme has model performance comparable to the Born machine scheme, though not better. The tasks include deducing the structure of binary bitwise operations, learning the internal structure of random Bayesian networks given only visible sites, and a real-world example related to hierarchical clustering where a cladogram is constructed from mitochondrial DNA sequences. In doing so, we also show the importance of the choice of network topology and the versatility of a least-mutual information criterion in selecting a candidate structure for a tensor tree, as well as discuss aspects of these tensor tree generative models including their information content and interpretability.

The Discovery Engine is a general purpose automated system for scientific discovery, which combines machine learning with state-of-the-art ML interpretability to enable rapid and robust scientific insight across diverse datasets. In this paper, we benchmark the Discovery Engine against five recent peer-reviewed scientific publications applying machine learning across medicine, materials science, social science, and environmental science. In each case, the Discovery Engine matches or exceeds prior predictive performance while also generating deeper, more actionable insights through rich interpretability artefacts. These results demonstrate its potential as a new standard for automated, interpretable scientific modelling that enables complex knowledge discovery from data.

Social media platforms serve as central hubs for content dissemination, opinion expression, and public engagement across diverse modalities. Accurately predicting the popularity of social media videos enables valuable applications in content recommendation, trend detection, and audience engagement. In this paper, we present Multimodal Video Predictor (MVP), our winning solution to the Video Track of the SMP Challenge 2025. MVP constructs expressive post representations by integrating deep video features extracted from pretrained models with user metadata and contextual information. The framework applies systematic preprocessing techniques, including log-transformations and outlier removal, to improve model robustness. A gradient-boosted regression model is trained to capture complex patterns across modalities. Our approach ranked first in the official evaluation of the Video Track, demonstrating its effectiveness and reliability for multimodal video popularity prediction on social platforms. The source code is available at https://anonymous.4open.science/r/SMPDVideo.

Social media popularity prediction plays a crucial role in content optimization, marketing strategies, and user engagement enhancement across digital platforms. However, predicting post popularity remains challenging due to the complex interplay between visual, textual, temporal, and user behavioral factors. This paper presents HyperFusion, a hierarchical multimodal ensemble learning framework for social media popularity prediction. Our approach employs a three-tier fusion architecture that progressively integrates features across abstraction levels: visual representations from CLIP encoders, textual embeddings from transformer models, and temporal-spatial metadata with user characteristics. The framework implements a hierarchical ensemble strategy combining CatBoost, TabNet, and custom multi-layer perceptrons. To address limited labeled data, we propose a two-stage training methodology with pseudo-labeling and iterative refinement. We introduce novel cross-modal similarity measures and hierarchical clustering features that capture inter-modal dependencies. Experimental results demonstrate that HyperFusion achieves competitive performance on the SMP challenge dataset. Our team achieved third place in the SMP Challenge 2025 (Image Track). The source code is available at https://anonymous.4open.science/r/SMPDImage.

Many of the observations we make are biased by our decisions. For instance, the demand of items is impacted by the prices set, and online checkout choices are influenced by the assortments presented. The challenge in decision-making under this setting is the lack of counterfactual information, and the need to learn it instead. We introduce an end-to-end method under endogenous uncertainty to train ML models to be aware of their downstream, enabling their effective use in the decision-making stage. We further introduce a robust optimization variant that accounts for uncertainty in ML models -- specifically by constructing uncertainty sets over the space of ML models and optimizing actions to protect against worst-case predictions. We prove guarantees that this robust approach can capture near-optimal decisions with high probability as a function of data. Besides this, we also introduce a new class of two-stage stochastic optimization problems to the end-to-end learning framework that can now be addressed through our framework. Here, the first stage is an information-gathering problem to decide which random variable to poll and gain information about before making a second-stage decision based off of it. We present several computational experiments for pricing and inventory assortment/recommendation problems. We compare against existing methods in online learning/bandits/offline reinforcement learning and show our approach has consistent improved performance over these. Just as in the endogenous setting, the model's prediction also depends on the first-stage decision made. While this decision does not affect the random variable in this setting, it does affect the correct point forecast that should be made.

HIV epidemiological data is increasingly complex, requiring advanced computation for accurate cluster detection and forecasting. We employed quantum-accelerated machine learning to analyze HIV prevalence at the ZIP-code level using AIDSVu and synthetic SDoH data for 2022. Our approach compared classical clustering (DBSCAN, HDBSCAN) with a quantum approximate optimization algorithm (QAOA), developed a hybrid quantum-classical neural network for HIV prevalence forecasting, and used quantum Bayesian networks to explore causal links between SDoH factors and HIV incidence. The QAOA-based method achieved 92% accuracy in cluster detection within 1.6 seconds, outperforming classical algorithms. Meanwhile, the hybrid quantum-classical neural network predicted HIV prevalence with 94% accuracy, surpassing a purely classical counterpart. Quantum Bayesian analysis identified housing instability as a key driver of HIV cluster emergence and expansion, with stigma exerting a geographically variable influence. These quantum-enhanced methods deliver greater precision and efficiency in HIV surveillance while illuminating critical causal pathways. This work can guide targeted interventions, optimize resource allocation for PrEP, and address structural inequities fueling HIV transmission.