Electronic design engineers often struggle to efficiently access relevant information for tasks like design verification and technology development. While large language models (LLMs) can enhance productivity as conversational agents, pre-trained open-source LLMs lack domain-specific knowledge for Electronic Design Automation (EDA). In a Retrieval-Augmented Generation (RAG) context, LLMs rely on external context but may still produce inaccurate responses. Retrieval-Augmented Fine-Tuning (RAFT) improves LLM performance, but acquiring labeled question/answer (Q/A) data in EDA is difficult. To address this, we propose using synthetic Q/A datasets to enhance LLMs with RAFT. Our results show that RAFT with synthetic data significantly boosts LLM performance for RAG-based EDA tasks. We also investigate the impact of using real user questions as Retrieval-Augmented Few-Shot (RAFS) examples for synthetic data generation. Additionally, we implement secure access control to ensure sensitive information is only accessible to authorized personnel. Finally, we assess the risk of data leakage and unintended memorization during fine-tuning with synthetic data, providing practical insights.

A common type of zero-inflated data has certain true values incorrectly replaced by zeros due to data recording conventions (rare outcomes assumed to be absent) or details of data recording equipment (e.g. artificial zeros in gene expression data). Existing methods for zero-inflated data either fit the observed data likelihood via parametric mixture models that explicitly represent excess zeros, or aim to replace excess zeros by imputed values. If the goal of the analysis relies on knowing true data realizations, a particular challenge with zero-inflated data is identifiability, since it is difficult to correctly determine which observed zeros are real and which are inflated. This paper views zero-inflated data as a general type of missing data problem, where the observability indicator for a potentially censored variable is itself unobserved whenever a zero is recorded. We show that, without additional assumptions, target parameters involving a zero-inflated variable are not identified. However, if a proxy of the missingness indicator is observed, a modification of the effect restoration approach of Kuroki and Pearl allows identification and estimation, given the proxy-indicator relationship is known. If this relationship is unknown, our approach yields a partial identification strategy for sensitivity analysis. Specifically, we show that only certain proxy-indicator relationships are compatible with the observed data distribution. We give an analytic bound for this relationship in cases with a categorical outcome, which is sharp in certain models. For more complex cases, sharp numerical bounds may be computed using methods in Duarte et al.[2023]. We illustrate our method via simulation studies and a data application on central line-associated bloodstream infections (CLABSIs).

Electronic design engineers are challenged to find relevant information efficiently for a myriad of tasks within design construction, verification and technology development. Large language models (LLM) have the potential to help improve productivity by serving as conversational agents that effectively function as subject-matter experts. In this paper we demonstrate Ask-EDA, a chat agent designed to serve as a 24x7 expert available to provide guidance to design engineers. Ask-EDA leverages LLM, hybrid retrieval augmented generation (RAG) and abbreviation de-hallucination (ADH) techniques to deliver more relevant and accurate responses. We curated three evaluation datasets, namely q2a-100, cmds-100 and abbr-100. Each dataset is tailored to assess a distinct aspect: general design question answering, design command handling and abbreviation resolution. We demonstrated that hybrid RAG offers over a 40% improvement in Recall on the q2a-100 dataset and over a 60% improvement on the cmds-100 dataset compared to not using RAG, while ADH yields over a 70% enhancement in Recall on the abbr-100 dataset. The evaluation results show that Ask-EDA can effectively respond to design-related inquiries.

Support Vector Machine (SVM) algorithm requires a high computational cost (both in memory and time) to solve a complex quadratic programming (QP) optimization problem during the training process. Consequently, SVM necessitates high computing hardware capabilities. The central processing unit (CPU) clock frequency cannot be increased due to physical limitations in the miniaturization process. However, the potential of parallel multi-architecture, available in both multi-core CPUs and highly scalable GPUs, emerges as a promising solution to enhance algorithm performance. Therefore, there is an opportunity to reduce the high computational time required by SVM for solving the QP optimization problem. This paper presents a comparative study that implements the SVM algorithm on different parallel architecture frameworks. The experimental results show that SVM MPI-CUDA implementation achieves a speedup over SVM TensorFlow implementation on different datasets. Moreover, SVM TensorFlow implementation provides a cross-platform solution that can be migrated to alternative hardware components, which will reduces the development time.

Automatic summarization with pre-trained language models has led to impressively fluent results, but is prone to 'hallucinations', low performance on non-news genres, and outputs which are not exactly summaries. Targeting ACL 2023's 'Reality Check' theme, we present GUMSum, a small but carefully crafted dataset of English summaries in 12 written and spoken genres for evaluation of abstractive summarization. Summaries are highly constrained, focusing on substitutive potential, factuality, and faithfulness. We present guidelines and evaluate human agreement as well as subjective judgments on recent system outputs, comparing general-domain untuned approaches, a fine-tuned one, and a prompt-based approach, to human performance. Results show that while GPT3 achieves impressive scores, it still underperforms humans, with varying quality across genres. Human judgments reveal different types of errors in supervised, prompted, and human-generated summaries, shedding light on the challenges of producing a good summary.

Businessman draws increase arrow graph corporate future growth year 2022 to 2023. The year 2023 is set to be revolutionary for technology, with many disruptive trends expected to reshape how businesses function and how people interact with each other. From metaverse-based virtual workspaces, advancements in quantum computing and green energy sources to innovations in robots and satellite connectivity – here's a look at the technological trends that could define the coming year. According to BCG's "Mind the Tech Gap" survey, a majority of businesses across 13 countries plan to increase their spending on digital transformation in 2023 vs. 2022. The top two areas for future investments are business model transformation and sustainability, with respondents expressing concern over the uncertain return on investment from digital transformation initiatives.

A personal, guided tour to the best scoops and stories every day in The Wall Street Journal. Dr. Frederick P. Brooks Jr. liked building things, first laying foundations for modern computer systems at International Business Machines Corp. and later at the University of North Carolina, where he started the computer-science department. Dr. Brooks managed the development of IBM's System/360 family of compatible mainframe computers and then the software system that went with them during the 1960s. The computers became some of IBM's most popular models of the era, offering customers a choice of big or small computers with different processing speeds that could be used for both business and scientific tasks. The system was easy to expand since all the hardware ran off the same software, a departure from other systems that required software reprogramming when computers were added.

Former Army Ranger and Save Our Allies co-founder Tim Kennedy discusses the trauma experienced by military veterans following the evacuation of Afghanistan on'Fox News Live.' Top U.S. officials held their first in person meeting with the Taliban since a U.S. military strike killed the leader of Al Qaeda in Afghanistan in July. The Biden administration sent CIA deputy director David Cohen to the Qatari capital of Doha on Saturday to meet with a Taliban delegation led by Abdul Haq Wasiq, the Taliban's head of intelligence, Fox News has confirmed. The meeting marks the first time the two sides have met in person since a U.S. drone strike this summer killed Al Qaeda leader Ayman al-Zawahri in the Taliban controlled Afghanistan capital of Kabul raising questions about the terror group's presence in the country. The Taliban claimed it was unaware that the Al-Qaeda chief was in the country and called the drone strike a "clear violation" of the Doha agreement struck with former President Donald Trump in 2020. AFGHANISTAN ONE YEAR LATER: HOW DAILY LIFE IN THE WAR-TORN COUNTRY HAS CHANGED SINCE THE TALIBAN'S TAKEOVER Taliban fighters escort women march in support of the Taliban government outside Kabul University, Afghanistan.

If an agent, or a coalition of agents, has a strategy, knows that she has a strategy, and knows what the strategy is, then she has a know-how strategy. Several modal logics of coalition power for know-how strategies have been studied before. The contribution of the article is three-fold. First, it proposes a new class of know-how strategies that depend on the intelligence information about the opponents' actions. Second, it shows that the coalition power modality for the proposed new class of strategies cannot be expressed through the standard know-how modality. Third, it gives a sound and complete logical system that describes the interplay between the coalition power modality with intelligence and the distributed knowledge modality in games with imperfect information.

Human intelligence reflects our brain's ability to learn. Computer systems that act like humans use artificial intelligence. That means these systems are under the control of computer programs that can learn. Just as people do, computers can learn to use data and then make decisions or assessments from what they've learned. Called machine learning, it's part of the larger field of artificial intelligence.