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Stringent quality requirements for safety-critical applications drive the demand for “zero defects” in modern ICs. In this context, delay characterization of standard cells for resistive open defects is an increasing concern due to aggressive timing margins in digital circuits. The problem is made worse by the large number of open defect sites in standard cells, combined with a wide range of defect resistance values for each site. This incurs possible prohibitive costs for defect simulation and characterization. To alleviate this complexity, we propose Resistive Fault Dominance (RFD) for resistive open defects. RFD eliminates simulations of certain open defects with intermediate defect resistance values that are guaranteed to exceed specified timing margins for standard cells, based on tests for specific “dominant” open defects. This can significantly reduce the computational costs of cell library characterization and simulation effort by 84%-91%. An algorithmic fault simulation methodology for resistive open defects on parasitic-extracted (PEX) transistor level netlists is developed. 

Large language models (LLMs) have achieved remarkable success in natural language processing (NLP), demonstrating significant capabilities in processing and understanding text data. However, recent studies have identified limitations in LLMs’ ability to manipulate, program, and reason about structured data, especially graphs. We introduce GraphEval36K1 , the first comprehensive graph dataset, comprising 40 graph coding problems and 36,900 test cases to evaluate the ability of LLMs on graph problem solving. Our dataset is categorized into eight primary and four sub-categories to ensure a thorough evaluation across different types of graphs. We benchmark ten LLMs, finding that private models outperform open-source ones, though the gap is narrowing. We also analyze the performance of LLMs across directed vs undirected graphs, different kinds of graph concepts, and network models. Furthermore, to improve the usability of our evaluation framework, we propose Structured Symbolic Decomposition (SSD), an instruction-based method designed to enhance LLM performance on complex graph tasks. Results show that SSD improves the average passing rate of GPT-4, GPT4o, Gemini-Pro and Claude-3-Sonnet by 8.38%, 6.78%, 29.28% and 25.28%, respectively. 

Large Language Models (LLMs) have achieved remarkable success in natural language tasks, yet understanding their reasoning processes re- mains a significant challenge. We address this by introducing XplainLLM, a dataset accom- panying an explanation framework designed to enhance LLM transparency and reliability. Our dataset comprises 24,204 instances where each instance interprets the LLM’s reasoning behavior using knowledge graphs (KGs) and graph attention networks (GAT), and includes explanations of LLMs such as the decoder- only Llama-3 and the encoder-only RoBERTa. XplainLLM also features a framework for gener- ating grounded explanations and the debugger- scores for multidimensional quality analysis. Our explanations include why-choose and why- not-choose components, reason-elements, and debugger-scores that collectively illuminate the LLM’s reasoning behavior. Our evaluations demonstrate XplainLLM’s potential to reduce hallucinations and improve grounded explana- tion generation in LLMs. XplainLLM is a re- source for researchers and practitioners to build trust and verify the reliability of LLM outputs. Our code and dataset are publicly available. 

Abstract The red hypergiant VY CMa is remarkable for its very visible record of high-mass-loss events observed over the range of wavelengths from the optical and infrared to the submillimeter region with Atacama Large Millimeter/submillimeter Array (ALMA). The SW Clump or SW knots are unique in the ejecta of VY CMa. Except for the central star, they are the brightest sources of dusty infrared emission in its complex ejecta. In this paper we combine the proper motions from the Hubble Space Telescope images, and infrared fluxes from 2 to 12μm with the¹²CO images from ALMA to determine their ages and mass estimates. The SW knots were ejected more than 200 yr ago with an active period lasting about 30 yr, and with a total mass in the Clump > 2 × 10⁻²M_⊙. 

Lifelong learning plays an important role in achieving success in one’s professional life. Engaging students in metacognition assists in the development of their lifelong learning abilities. Instructors can integrate reflection activities in their courses to provide multiple opportunities to students for metacognitive engagement. During reflection, students regulate their cognition by engaging themselves in three dimensions of metacognition: Planning, Monitoring, and Evaluating. Reflection is a complex process, and it takes time to reach the level of critical reflection. The purpose of the study was to investigate the change in students' level of engagement in three dimensions of metacognition when reflecting on the third and tenth-week assignments of the environmental engineering course. Data collection took place in the Fall of 2023 at a large Midwest University. Students’ responses to the assigned reflection prompts for each dimension were coded for their level of engagement in each element of the three dimensions using a revised prior coding scheme. Results showed that for both assignments, students' responses were mainly at the vague level for all elements of the three dimensions, indicating students' superficial engagement in the reflection activity. Recommendations for instructors are provided to improve students' understanding of the reflection activity and their level of engagement in the three dimensions of metacognition. 

Abstract TheJ= 5.5 → 4.5 andJ= 5 → 4 transitions of PO and PN, respectively, have been imaged in the envelope of hypergiant star VY Canis Majoris (VY CMa) using the Atacama Large Millimeter/submillimeter Array with angular resolutions of 0.″2 and 1.″5 and data from the Submillimeter Telescope of the Arizona Radio Observatory. These maps are the first high-fidelity images of PO and PN in a circumstellar envelope. Both molecules are primarily present in a spherical, star-centered region with a radius ∼60R_*(0.″5), indicating formation by LTE chemistry and then condensation into grains. PN, however, shows additional, fan-shaped emission 2″ southwest of the star, coincident with dust features resolved by Hubble Space Telescope (HST), as well as four newly identified distinct structures 1″–2″ toward the north, east, and west (Cloudlets I–IV), not visible in HST images. The “SW Fan” and the cloudlets are also prominent in theJ= 5.5 → 4.5 transition of NS. The correlation of PN with NS, SiO, and dust knots in the SW Fan suggests a formation in shocked gas enhanced with nitrogen. Excess nitrogen is predicted to favor PN synthesis over PO. Abundances for PN and PO in the spherical source aref∼ 4.4 × 10⁻⁸and 1.4 × 10⁻⁷, respectively, relative to H₂. Given a cosmic abundance of phosphorus, an unusually high fraction (∼35%) is contained in PO and PN. Alternatively, the stellar winds may be enriched in P (and N) by dredge-up from the interior of VY CMa. 

Wireless links at sub-THz bands require low-SWaP SDR modems. We report early design experimentation of an SDR operating in the 130–150 GHz band, with ASK/BPSK/QPSK modulation on I/Q channels, at a maximum data rate of 128 Mbps. The design utilizes 110–170 GHz front-ends from Virginia Diodes, and Xilinx RF-SoC ZCU-111 for DSP operations. A 1 GHz baseband example at 145.5 GHz is provided. The experiment uses horn antennas with 21 dB gain. The SNR is about 40 dB without cross correlation gain in the detector which provides an additional 15 dB in link margin. Real-time bit rate of 128 Mbps is achieved. Example applications include vehicle-to-vehicle, vehicle-to-infrastructure, backhaul, device-to-aerostat. This paper provides a platform from which further design work will lead to increased data rate and/or range, and enhance security through encryption. Future designs will facilitate digital interfaces, such as, ethernet, AXI, PCIe and USB-C. 

Self-regulation, a skillset involving taking charge of one’s own learning processes, is crucial for workplace success. Learners develop self-regulation skills through reflection where they recognize weaknesses and strengths by employing metacognitive strategies: planning, monitoring, and evaluating. Use of anchors assists learners’ engagement in reflection. The purpose of this work was to gain insight into students’ use of anchors when reflecting on their learning. The two research questions were: (1) To what extent do students link their self-evaluation and learning objective (LO) self-ratings to their reflections? and (2) What dimensions and level of metacognitive strategies do students use in their self-evaluation of and reflections on weekly problem-solving assignments? Data were upper-division engineering students’ anchors (self- evaluations, LO self-ratings) and reflection responses for one assignment. Self-evaluations and reflections were analyzed for the presence of references to LOs. The number of students who linked the anchors to their reflection were tabulated. Additionally, a revised a priori coding scheme was applied to students’ written work to determine type and level of metacognitive strategies employed. Few students linked both anchors to their reflections. Students employed low to medium levels of the metacognitive strategies in their self-evaluations and reflections, even when they linked their anchors and reflections. The evaluating strategy dominated in the self- evaluations, while planning and monitoring dominated in the reflections. Students have limited understanding of the use of anchors to guide their reflection responses. Students overall level of engagement in the metacognitive strategies indicates a need for formal instruction on reflection.