In the rapidly evolving field of optical engineering, precise alignment of multi-lens imaging systems is critical yet challenging, as even minor misalignments can significantly degrade performance. Traditional alignment methods rely on specialized equipment and are time-consuming processes, highlighting the need for automated and scalable solutions. We present two complementary deep learning-based inverse-design methods for diagnosing misalignments in multi-element lens systems using only optical measurements. First, we use ray-traced spot diagrams to predict five-degree-of-freedom (5-DOF) errors in a 6-lens photographic prime, achieving a mean absolute error of 0.031mm in lateral translation and 0.011$^\circ$ in tilt. We also introduce a physics-based simulation pipeline that utilizes grayscale synthetic camera images, enabling a deep learning model to estimate 4-DOF, decenter and tilt errors in both two- and six-lens multi-lens systems. These results show the potential to reshape manufacturing and quality control in precision imaging.

We propose a circuit-level attack, SQUASH, a SWAP-Based Quantum Attack to sabotage Hybrid Quantum Neural Networks (HQNNs) for classification tasks. SQUASH is executed by inserting SWAP gate(s) into the variational quantum circuit of the victim HQNN. Unlike conventional noise-based or adversarial input attacks, SQUASH directly manipulates the circuit structure, leading to qubit misalignment and disrupting quantum state evolution. This attack is highly stealthy, as it does not require access to training data or introduce detectable perturbations in input states. Our results demonstrate that SQUASH significantly degrades classification performance, with untargeted SWAP attacks reducing accuracy by up to 74.08\% and targeted SWAP attacks reducing target class accuracy by up to 79.78\%. These findings reveal a critical vulnerability in HQNN implementations, underscoring the need for more resilient architectures against circuit-level adversarial interventions.

The rapid advancement of large language models (LLMs) has redefined artificial intelligence (AI), pushing the boundaries of AI research and enabling unbounded possibilities for both academia and the industry. However, LLM development faces increasingly complex challenges throughout its lifecycle, yet no existing research systematically explores these challenges and solutions from the perspective of software engineering (SE) approaches. To fill the gap, we systematically analyze research status throughout the LLM development lifecycle, divided into six phases: requirements engineering, dataset construction, model development and enhancement, testing and evaluation, deployment and operations, and maintenance and evolution. We then conclude by identifying the key challenges for each phase and presenting potential research directions to address these challenges. In general, we provide valuable insights from an SE perspective to facilitate future advances in LLM development.

Graph machine learning architectures are typically tailored to specific tasks on specific datasets, which hinders their broader applicability. This has led to a new quest in graph machine learning: how to build graph foundation models capable of generalizing across arbitrary graphs and features? In this work, we present a recipe for designing graph foundation models for node-level tasks from first principles. The key ingredient underpinning our study is a systematic investigation of the symmetries that a graph foundation model must respect. In a nutshell, we argue that label permutation-equivariance alongside feature permutation-invariance are necessary in addition to the common node permutation-equivariance on each local neighborhood of the graph. To this end, we first characterize the space of linear transformations that are equivariant to permutations of nodes and labels, and invariant to permutations of features. We then prove that the resulting network is a universal approximator on multisets that respect the aforementioned symmetries. Our recipe uses such layers on the multiset of features induced by the local neighborhood of the graph to obtain a class of graph foundation models for node property prediction. We validate our approach through extensive experiments on 29 real-world node classification datasets, demonstrating both strong zero-shot empirical performance and consistent improvement as the number of training graphs increases.

The appearance of singularities in the function of interest constitutes a fundamental challenge in scientific computing. It can significantly undermine the effectiveness of numerical schemes for function approximation, numerical integration, and the solution of partial differential equations (PDEs), etc. The problem becomes more sophisticated if the location of the singularity is unknown, which is often encountered in solving PDEs. Detecting the singularity is therefore critical for developing efficient adaptive methods to reduce computational costs in various applications. In this paper, we consider singularity detection in a purely data-driven setting. Namely, the input only contains given data, such as the vertex set from a mesh. To overcome the limitation of the raw unlabeled data, we propose a self-supervised learning (SSL) framework for estimating the location of the singularity. A key component is a filtering procedure as the pretext task in SSL, where two filtering methods are presented, based on $k$ nearest neighbors and kernel density estimation, respectively. We provide numerical examples to illustrate the potential pathological or inaccurate results due to the use of raw data without filtering. Various experiments are presented to demonstrate the ability of the proposed approach to deal with input perturbation, label corruption, and different kinds of singularities such interior circle, boundary layer, concentric semicircles, etc.

Accurate intraoperative localization of the subthalamic nucleus (STN) is essential for the efficacy of Deep Brain Stimulation (DBS) in patients with Parkinson's disease. While microelectrode recordings (MERs) provide rich electrophysiological information during DBS electrode implantation, current localization practices often rely on subjective interpretation of signal features. In this study, we propose a quantitative framework that leverages nonlinear dynamics and entropy-based metrics to classify neural activity recorded inside versus outside the STN. MER data from three patients were preprocessed using a robust artifact correction pipeline, segmented, and labelled based on surgical annotations. A comprehensive set of recurrence quantification analysis, nonlinear, and entropy features were extracted from each segment. Multiple supervised classifiers were trained on every combination of feature domains using stratified 10-fold cross-validation, followed by statistical comparison using paired Wilcoxon signed-rank tests with Holm-Bonferroni correction. The combination of entropy and nonlinear features yielded the highest discriminative power, and the Extra Trees classifier emerged as the best model with a cross-validated F1-score of 0.902+/-0.027 and ROC AUC of 0.887+/-0.055. Final evaluation on a 20% hold-out test set confirmed robust generalization (F1= 0.922, ROC AUC = 0.941). These results highlight the potential of nonlinear and entropy signal descriptors in supporting real-time, data-driven decision-making during DBS surgeries

Embedded into information systems, artificial intelligence (AI) faces security threats that exploit AI-specific vulnerabilities. This paper provides an accessible overview of adversarial attacks unique to predictive and generative AI systems. We identify eleven major attack types and explicitly link attack techniques to their impacts -- including information leakage, system compromise, and resource exhaustion -- mapped to the confidentiality, integrity, and availability (CIA) security triad. We aim to equip researchers, developers, security practitioners, and policymakers, even those without specialized AI security expertise, with foundational knowledge to recognize AI-specific risks and implement effective defenses, thereby enhancing the overall security posture of AI systems.

Named Entity Recognition (NER) is a fundamental Natural Language Processing (NLP) task that aims to identify and classify entity mentions in texts across different categories. While languages such as English possess a large number of high-quality resources for this task, Brazilian Portuguese still lacks in quantity of gold-standard NER datasets, especially when considering specific domains. Particularly, this paper considers the importance of NER for analyzing historical texts in the context of digital humanities. To address this gap, this work outlines the construction of MariNER: \textit{Mapeamento e Anotações de Registros hIstóricos para NER} (Mapping and Annotation of Historical Records for NER), the first gold-standard dataset for early 20th-century Brazilian Portuguese, with more than 9,000 manually annotated sentences. We also assess and compare the performance of state-of-the-art NER models for the dataset.

We propose a novel approach for detecting personal data in structured datasets, leveraging GPT-4o, a state-of-the-art Large Language Model. A key innovation of our method is the incorporation of contextual information: in addition to a feature's name and values, we utilize information from other feature names within the dataset as well as the dataset description. We compare our approach to alternative methods, including Microsoft Presidio and CASSED, evaluating them on multiple datasets: DeSSI, a large synthetic dataset, datasets we collected from Kaggle and OpenML as well as MIMIC-Demo-Ext, a real-world dataset containing patient information from critical care units. Our findings reveal that detection performance varies significantly depending on the dataset used for evaluation. CASSED excels on DeSSI, the dataset on which it was trained. Performance on the medical dataset MIMIC-Demo-Ext is comparable across all models, with our GPT-4o-based approach clearly outperforming the others. Notably, personal data detection in the Kaggle and OpenML datasets appears to benefit from contextual information. This is evidenced by the poor performance of CASSED and Presidio (both of which do not utilize the context of the dataset) compared to the strong results of our GPT-4o-based approach. We conclude that further progress in this field would greatly benefit from the availability of more real-world datasets containing personal information.

Recent research works establish deep neural networks as high performing tools for radar target detection, especially on challenging environments (presence of clutter or interferences, multi-target scenarii...). However, the usually large computational complexity of these networks is one of the factors preventing them from being widely implemented in embedded radar systems. We propose to investigate novel neural architecture search (NAS) methods, based on Monte-Carlo Tree Search (MCTS), for finding neural networks achieving the required detection performance and striving towards a lower computational complexity. We evaluate the searched architectures on endoclutter radar signals, in order to compare their respective performance metrics and generalization properties. A novel network satisfying the required detection probability while being significantly lighter than the expert-designed baseline is proposed.