Logic synthesis, a pivotal stage in chip design, entails optimizing chip specifications encoded in hardware description languages like Verilog into highly efficient implementations using Boolean logic gates. The process involves a sequential application of logic minimization heuristics ("synthesis recipe"), with their arrangement significantly impacting crucial metrics such as area and delay. Addressing the challenge posed by the broad spectrum of design complexities -- from variations of past designs (e.g., adders and multipliers) to entirely novel configurations (e.g., innovative processor instructions) -- requires a nuanced'synthesis recipe' guided by human expertise and intuition. This study conducts a thorough examination of learning and search techniques for logic synthesis, unearthing a surprising revelation: pre-trained agents, when confronted with entirely novel designs, may veer off course, detrimentally affecting the search trajectory. We present ABC-RL, a meticulously tuned α parameter that adeptly adjusts recommendations from pre-trained agents during the search process. Computed based on similarity scores through nearest neighbor retrieval from the training dataset, ABC-RL yields superior synthesis recipes tailored for a wide array of hardware designs. Our findings showcase substantial enhancements in the Quality-of-result (QoR) of synthesized circuits, boasting improvements of up to 24.8% compared to state-of-the-art techniques. Furthermore, ABC-RL achieves an impressive up to 9x reduction in runtime (iso-QoR) when compared to current state-of-the-art methodologies. Modern chips are designed using sophisticated electronic design automation (EDA) algorithms that automatically convert logic functions expressed in a hardware description language (HDL) like Verilog to a physical layout that can be manufactured at a semiconductor foundry. EDA involves a sequence of steps, the first of which is logic synthesis. Logic synthesis converts HDL into a low-level "netlist" of Boolean logic gates that implement the desired function. A netlist is a graph whose nodes are logic gates (e.g., ANDs, NOTs, ORs) and whose edges represent connections between gates.

Table-based reasoning with large language models (LLMs) is a promising direction to tackle many table understanding tasks, such as table-based question answering and fact verification. Compared with generic reasoning, table-based reasoning requires the extraction of underlying semantics from both free-form questions and semi-structured tabular data. Chain-of-Thought and its similar approaches incorporate the reasoning chain in the form of textual context, but it is still an open question how to effectively leverage tabular data in the reasoning chain. Specifically, we guide LLMs using in-context learning to iteratively generate operations and update the table to represent a tabular reasoning chain. LLMs can therefore dynamically plan the next operation based on the results of the previous ones. This continuous evolution of the table forms a chain, showing the reasoning process for a given tabular problem. The chain carries structured information of the intermediate results, enabling more accurate and reliable predictions. Tables are a popular data format and widely used in daily life (Cafarella et al., 2008). Understanding tabular data with language models can benefit various downstream tasks, such as table-based fact verification (Chen et al., 2019), and table-based question answering (Jin et al., 2022). Distinct from pure text, tables deliver rich information through the interaction between rows and columns in the tabular structure, which enhances the data capacity but also increases the difficulty for language models to understand them. Thus, reasoning over the tabular data is an important direction in natural language processing and attracts increasing attention from both academia and industry. In recent years, several approaches have been suggested to tackle the problem of table understanding by training language models. One common direction is to add specialized embedding layers or attention mechanisms into language models and pre-train the models by recovering table cells or segments (Herzig et al., 2020; Wang et al., 2021; Gu et al., 2022; Andrejczuk et al., 2022).

Deep learning techniques, despite their potential, often suffer from a lack of reproducibility and generalizability, impeding their clinical adoption. Image segmentation is one of the critical tasks in medical image analysis, in which one or several regions/volumes of interest should be annotated. This paper introduces the RIDGE checklist, a framework for assessing the Reproducibility, Integrity, Dependability, Generalizability, and Efficiency of deep learning-based medical image segmentation models. The checklist serves as a guide for researchers to enhance the quality and transparency of their work, ensuring that segmentation models are not only scientifically sound but also clinically relevant.

This paper proposes a computational model for policy administration. As an organization evolves, new users and resources are gradually placed under the mediation of the access control model. Each time such new entities are added, the policy administrator must deliberate on how the access control policy shall be revised to reflect the new reality. A well-designed access control model must anticipate such changes so that the administration cost does not become prohibitive when the organization scales up. Unfortunately, past Access Control research does not offer a formal way to quantify the cost of policy administration. In this work, we propose to model ongoing policy administration in an active learning framework. Administration cost can be quantified in terms of query complexity. We demonstrate the utility of this approach by applying it to the evolution of protection domains. We also modelled different policy administration strategies in our framework. This allowed us to formally demonstrate that domain-based policies have a cost advantage over access control matrices because of the use of heuristic reasoning when the policy evolves. To the best of our knowledge, this is the first work to employ an active learning framework to study the cost of policy deliberation and demonstrate the cost advantage of heuristic policy administration.

Text summarization is a pivotal application of NLP that condenses lengthy documents or articles into shorter, coherent representations while retaining the essential information. Through various algorithms and techniques, NLP models identify significant sentences, key phrases, or essential concepts within the text to generate concise summaries. Extractive summarization involves selecting and stitching together important segments directly from the original text, often based on relevance, importance, or frequency of occurrence. On the other hand, abstractive summarization goes beyond extraction, generating novel sentences that convey the core meaning while potentially rephrasing and restructuring the content. NLP-powered summarization systems play a crucial role in information retrieval, aiding in quick comprehension and accessibility of vast amounts of text across diverse domains such as news articles, research papers, and legal documents. ChatGPT boasts impressive text summarization capabilities, harnessing its advanced Natural Language Processing (NLP) architecture to distill lengthy conversations, articles, or documents into concise, coherent summaries. Leveraging its vast understanding of language semantics, context, and syntax, ChatGPT effectively identifies key points, essential information, and significant passages within the text. Its summarization prowess encompasses extractive and abstractive techniques, allowing it to select important segments directly from the input while generating novel, coherent sentences that capture the essence of the content.

Artificial intelligence (AI) models trained using medical images for clinical tasks often exhibit bias in the form of disparities in performance between subgroups. Since not all sources of biases in real-world medical imaging data are easily identifiable, it is challenging to comprehensively assess how those biases are encoded in models, and how capable bias mitigation methods are at ameliorating performance disparities. In this article, we introduce a novel analysis framework for systematically and objectively investigating the impact of biases in medical images on AI models. We developed and tested this framework for conducting controlled in silico trials to assess bias in medical imaging AI using a tool for generating synthetic magnetic resonance images with known disease effects and sources of bias. The feasibility is showcased by using three counterfactual bias scenarios to measure the impact of simulated bias effects on a convolutional neural network (CNN) classifier and the efficacy of three bias mitigation strategies. The analysis revealed that the simulated biases resulted in expected subgroup performance disparities when the CNN was trained on the synthetic datasets. Moreover, reweighing was identified as the most successful bias mitigation strategy for this setup, and we demonstrated how explainable AI methods can aid in investigating the manifestation of bias in the model using this framework. Developing fair AI models is a considerable challenge given that many and often unknown sources of biases can be present in medical imaging datasets. In this work, we present a novel methodology to objectively study the impact of biases and mitigation strategies on deep learning pipelines, which can support the development of clinical AI that is robust and responsible.

While utilizing machine learning models, one of the most crucial aspects is how bias and fairness affect model outcomes for diverse demographics. This becomes especially relevant in the context of machine learning for medical imaging applications as these models are increasingly being used for diagnosis and treatment planning. In this paper, we study biases related to sex when developing a machine learning model based on brain magnetic resonance images (MRI). We investigate the effects of sex by performing brain age prediction considering different experimental designs: model trained using only female subjects, only male subjects and a balanced dataset. We also perform evaluation on multiple MRI datasets (Calgary-Campinas(CC359) and CamCAN) to assess the generalization capability of the proposed models. We found disparities in the performance of brain age prediction models when trained on distinct sex subgroups and datasets, in both final predictions and decision making (assessed using interpretability models). Our results demonstrated variations in model generalizability across sex-specific subgroups, suggesting potential biases in models trained on unbalanced datasets. This underlines the critical role of careful experimental design in generating fair and reliable outcomes.

Deep learning models have achieved state-of-the-art results in estimating brain age, which is an important brain health biomarker, from magnetic resonance (MR) images. However, most of these models only provide a global age prediction, and rely on techniques, such as saliency maps to interpret their results. These saliency maps highlight regions in the input image that were significant for the model's predictions, but they are hard to be interpreted, and saliency map values are not directly comparable across different samples. In this work, we reframe the age prediction problem from MR images to an image-to-image regression problem where we estimate the brain age for each brain voxel in MR images. We compare voxel-wise age prediction models against global age prediction models and their corresponding saliency maps. The results indicate that voxel-wise age prediction models are more interpretable, since they provide spatial information about the brain aging process, and they benefit from being quantitative.

Background: Mental illness can lead to adverse outcomes such as homelessness and police interaction and understanding of the events leading up to these adverse outcomes is important. Predictive models may help identify individuals at risk of such adverse outcomes. Using a fixed observation window cohort with logistic regression (LR) or machine learning (ML) models can result in lower performance when compared with adaptive and parcellated windows. Method: An administrative healthcare dataset was used, comprising of 240,219 individuals in Calgary, Alberta, Canada who were diagnosed with addiction or mental health (AMH) between April 1, 2013, and March 31, 2018. The cohort was followed for 2 years to identify factors associated with homelessness and police interactions. To understand the benefit of flexible windows to predictive models, an alternative cohort was created. Then LR and ML models, including random forests (RF), and extreme gradient boosting (XGBoost) were compared in the two cohorts. Results: Among 237,602 individuals, 0.8% (1,800) experienced first homelessness, while 0.32% (759) reported initial police interaction among 237,141 individuals. Male sex (AORs: H=1.51, P=2.52), substance disorder (AORs: H=3.70, P=2.83), psychiatrist visits (AORs: H=1.44, P=1.49), and drug abuse (AORs: H=2.67, P=1.83) were associated with initial homelessness (H) and police interaction (P). XGBoost showed superior performance using the flexible method (sensitivity =91%, AUC =90% for initial homelessness, and sensitivity =90%, AUC=89% for initial police interaction) Conclusion: This study identified key features associated with initial homelessness and police interaction and demonstrated that flexible windows can improve predictive modeling.

An experimental setup is presented, developed for comprehensive evaluation of high performance inertial sensors for translational motion, accelerometers and geophones. It employs a precision, robust air bearing stage driven by integral brushless DC motor. Experimental results illustrate the performance and capabilities of the setup. 1 Introduction Steadily improving performance of inertial sensors (accelerometers, geophones and gyroscopes) significantly broadens the range of their applications. It also necessitates significant enhancement of the evaluation methods and equipment used throughout the sensor development process. This research is concerned with evaluating accelerometers and geophones used for accurate motion tracking and geophysical sensing.