Accurate and efficient circuit behavior modeling is a cornerstone of modern electronic design automation. Among different types of circuits, stiff circuits are challenging to model using previous frameworks. In this work, we propose a new approach using Crossformer, which is a current state-of-the-art Transformer model for time-series prediction tasks, combined with Kolmogorov-Arnold Networks (KANs), to model stiff circuit transient behavior. By leveraging the Crossformer's temporal representation capabilities and the enhanced feature extraction of KANs, our method achieves improved fidelity in predicting circuit responses to a wide range of input conditions. Experimental evaluations on datasets generated through SPICE simulations of analog-to-digital converter (ADC) circuits demonstrate the effectiveness of our approach, with significant reductions in training time and error rates.

The driving environment perception has a vital role for autonomous driving and nowadays has been actively explored for its realization. The research community and relevant stakeholders necessitate the development of Deep Learning (DL) models and AI-enabled solutions to enhance autonomous vehicles (AVs) for smart mobility. There is a need to develop a model that accurately perceives multiple objects on the road and predicts the driver's perception to control the car's movements. This article proposes a novel utility-based analytical model that enables perception systems of AVs to understand the driving environment. The article consists of modules: acquiring a custom dataset having distinctive objects, i.e., motorcyclists, rickshaws, etc; a DL-based model (YOLOv8s) for object detection; and a module to measure the utility of perception service from the performance values of trained model instances. The perception model is validated based on the object detection task, and its process is benchmarked by state-of-the-art deep learning models' performance metrics from the nuScense dataset. The experimental results show three best-performing YOLOv8s instances based on mAP@0.5 values, i.e., SGD-based (0.832), Adam-based (0.810), and AdamW-based (0.822). However, the AdamW-based model (i.e., car: 0.921, motorcyclist: 0.899, truck: 0.793, etc.) still outperforms the SGD-based model (i.e., car: 0.915, motorcyclist: 0.892, truck: 0.781, etc.) because it has better class-level performance values, confirmed by the proposed perception model. We validate that the proposed function is capable of finding the right perception for AVs. The results above encourage using the proposed perception model to evaluate the utility of learning models and determine the appropriate perception for AVs.

Effective evaluation of web data record extraction methods is crucial, yet hampered by static, domain-specific benchmarks and opaque scoring practices. This makes fair comparison between traditional algorithmic techniques, which rely on structural heuristics, and Large Language Model (LLM)-based approaches, offering zero-shot extraction across diverse layouts, particularly challenging. To overcome these limitations, we introduce a concrete evaluation framework. Our framework systematically generates evaluation datasets from arbitrary MHTML snapshots, annotates XPath-based supervision labels, and employs structure-aware metrics for consistent scoring, specifically preventing text hallucination and allowing only for the assessment of positional hallucination. It also incorporates preprocessing strategies to optimize input for LLMs while preserving DOM semantics: HTML slimming, Hierarchical JSON, and Flat JSON. Additionally, we created a publicly available synthetic dataset by transforming DOM structures and modifying content. We benchmark deterministic heuristic algorithms and off-the-shelf LLMs across these multiple input formats. Our benchmarking shows that Flat JSON input enables LLMs to achieve superior extraction accuracy (F1 score of 0.9567) and minimal hallucination compared to other input formats like Slimmed HTML and Hierarchical JSON. We establish a standardized foundation for rigorous benchmarking, paving the way for the next principled advancements in web data record extraction.

This work has been submitted to the IEEE for possible publication. Abstract --Gas Chromatography coupled with Ion Mobility Spectrometry (GC-IMS) is a dual-separation analytical technique widely used for identifying components in gaseous samples by separating and analysing the arrival times of their constituent species. Data generated by GC-IMS is typically represented as two-dimensional spectra, providing rich information but posing challenges for data-driven analysis due to limited labelled datasets. This study introduces a novel method for generating synthetic 2D spectra using a deep learning framework based on Autoencoders. Although applied here to GC-IMS data, the approach is broadly applicable to any two-dimensional spectral measurements where labelled data are scarce. While performing component classification over a labelled dataset of GC-IMS records, the addition of synthesized records significantly has improved the classification performance, demonstrating the method's potential for overcoming dataset limitations in machine learning frameworks. I NTRODUCTION Gas Chromatography coupled with Ion Mobility Spectrometry (GC-IMS) is a technique used to identify chemical components within a sample [1]. Initially, the sample, carried by a carrier gas, is introduced into the GC column, where interactions between the sample components and the column affect their transit speeds, leading to an initial separation.

This phenomenon--often called the Rashomon effect [7], predictive multiplicity [22], or model multiplicity [5]--has wide-ranging implications for both understanding and improving fairness, as these equally accurate models often differ substantially in other properties such as fairness [21, 28] or model simplicity [29-31]. As prior work has pointed out, this multiplicity of models can be viewed as both a fairness opportunity and a concern [5, 10]. On the positive side, legal scholarship has pointed to the fact that model multiplicity is relevant to how to interpret and enforce U.S. anti-discrimination law, and specifically, can strengthen the disparate impact doctrine to more effectively combat algorithmic discrimination [3]. In a recent paper, Black et al. [3] suggest that the phenomenon of model multiplicity could support a reading of the disparate impact doctrine that requires companies to proactively search the set of equally accurate models for less discriminatory alternatives that have equivalent accuracy to a base model deemed acceptable for deployment from a model performance perspective. On the negative side, several scholars have pointed out that facially similar models, with equivalent accuracy but differences in their individual predictions, can suggest that some model decisions are arbitrary since they seem to be made on the basis of model choice that does not impact performance (e.g., a <1% change in a model's training set accuracy) [2, 17, 22]. This arbitrariness can impact model explanations and recourse as well: individuals with decisions that are unstable across small model changes may not receive reliable explanations for their model outcome, or ways to change it [4, 6, 25]. Further, if there is a group-based asymmetry of arbitrariness-e.g., if female loan applicants have more arbitrariness in their decisions than male loan applicants-- this could lead to a group-based equity concern in and of itself. Understanding the extent of the benefits and risks of model multiplicity relies upon an understanding of the properties of the Rashomon set, or the set of approximately equally accurate models for a given prediction task, i.e., equally accurate up to

Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. In the United States, more than 15.7 million Americans have been diagnosed with COPD, with 96% of individuals living with at least one other chronic health condition. It is the 4th leading cause of death in the country. Over 2.2 million patients are admitted to hospitals annually due to COPD exacerbations. Monitoring and predicting patient exacerbations on-time could save their life. This paper presents two different predictive models to predict COPD exacerbation using AI and natural language processing (NLP) approaches. These models use respiration summary notes, symptoms, and vital signs. To train and test these models, data records containing physiologic signals and vital signs time series were used. These records were captured from patient monitors and comprehensive clinical data obtained from hospital medical information systems for tens of thousands of Intensive Care Unit (ICU) patients. We achieved an area under the Receiver operating characteristic (ROC) curve of 0.82 in detection and prediction of COPD exacerbation.

$k$-Nearest Neighbor search on dense vector embeddings ($k$-NN retrieval) from pre-trained embedding models is the predominant retrieval method for text and images, as well as Retrieval-Augmented Generation (RAG) pipelines. In practice, application developers often fine-tune the embeddings to improve their accuracy on the dataset and query workload in hand. Existing approaches either fine-tune the pre-trained model itself or, more efficiently, but at the cost of accuracy, train adaptor models to transform the output of the pre-trained model. We present NUDGE, a family of novel non-parametric embedding fine-tuning approaches that are significantly more accurate and efficient than both sets of existing approaches. NUDGE directly modifies the embeddings of data records to maximize the accuracy of $k$-NN retrieval. We present a thorough theoretical and experimental study of NUDGE's non-parametric approach. We show that even though the underlying problem is NP-Hard, constrained variations can be solved efficiently. These constraints additionally ensure that the changes to the embeddings are modest, avoiding large distortions to the semantics learned during pre-training. In experiments across five pre-trained models and nine standard text and image retrieval datasets, NUDGE runs in minutes and often improves NDCG@10 by more than 10% over existing fine-tuning methods. On average, NUDGE provides 3.3x and 4.3x higher increase in accuracy and runs 200x and 3x faster, respectively, over fine-tuning the pre-trained model and training adaptors.

Dataset condensation (DC) methods aim to learn a smaller synthesized dataset with informative data records to accelerate the training of machine learning models. Current distribution matching (DM) based DC methods learn a synthesized dataset by matching the mean of the latent embeddings between the synthetic and the real dataset. However two distributions with the same mean can still be vastly different. In this work we demonstrate the shortcomings of using Maximum Mean Discrepancy to match latent distributions i.e. the weak matching power and lack of outlier regularization. To alleviate these shortcomings we propose our new method: Latent Quantile Matching (LQM) which matches the quantiles of the latent embeddings to minimize the goodness of fit test statistic between two distributions. Empirical experiments on both image and graph-structured datasets show that LQM matches or outperforms previous state of the art in distribution matching based DC. Moreover we show that LQM improves the performance in continual graph learning (CGL) setting where memory efficiency and privacy can be important. Our work sheds light on the application of DM based DC for CGL.