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DTSTART;TZID=America/Los_Angeles:20250827T110000
DTEND;TZID=America/Los_Angeles:20250827T110000
DTSTAMP:20260501T100114
CREATED:20250825T070000Z
LAST-MODIFIED:20250925T231632Z
UID:10000124-1756292400-1756292400@events.ucsc.edu
SUMMARY:Rakshit\, G. (CSE) -Improving Question Answering through Figurativeness Understanding\, Semantic Representation and Multi-Agent Conflict Resolution
DESCRIPTION:Open-domain question answering (ODQA) systems come with diverse challenges — ranging from resolving conflicting information to interpreting figurative expressions and representing meaning in a human-understandable form. This dissertation presents three complementary contributions toward building more robust and interpretable QA systems. \nFirst\, we investigate QA model performance on figurative language. Introducing FigurativeQA\, a benchmark of yes/no questions with figurative and literal contexts\, we demonstrate that popular BERT-based QA systems underperform significantly on figurative text. However\, prompting-based approaches like ChatGPT with chain-of-thought reasoning can mitigate this gap\, particularly when figurative contexts are automatically simplified. \nSecond\, we present ASQ\, a novel tool for automatically generating question-answer meaning representations (QMR) from Abstract Meaning Representation (AMR) graphs. ASQ enables scalable and linguistically grounded QA dataset construction\, bridging traditional formal semantics with natural language interfaces. We show that ASQ-generated questions exhibit high content fidelity and overlap with existing crowd-annotated resources like QAMR. \nFinally\, we explore how large language models (LLMs) handle conflicting evidence in ODQA\, proposing a multi-agent framework where answers generated by different models are evaluated through a verification step. Experiments using the QACC dataset and state-of-the-art LLMs (GPT-4o\, Claude 4\, DeepSeek-R1) reveal that model diversity enhances answer quality\, though requiring explanations during verification does not always lead to improvements. \nTogether\, these contributions advance the interpretability\, robustness\, and accuracy of QA systems. \nEvent Host: Geetanjali Rakshit\, Ph.D Candidate\, Computer Science & Engineering
URL:https://events.ucsc.edu/event/rakshit-g-cse-improving-question-answering-through-figurativeness-understanding-semantic-representation-and-multi-agent-conflict-resolution/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
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DTSTART;TZID=America/Los_Angeles:20250827T120000
DTEND;TZID=America/Los_Angeles:20250827T120000
DTSTAMP:20260501T100114
CREATED:20250819T070000Z
LAST-MODIFIED:20250925T231630Z
UID:10000116-1756296000-1756296000@events.ucsc.edu
SUMMARY:Leavitt\, J. (BMEB) - Evolutionary Dynamics\, Functional Adaptations in Stress Response\, and Direct Detection of tRNA modifications in Archaea
DESCRIPTION:Transfer RNA (tRNA) modifications are essential for structural integrity\, decoding fidelity\, and stress adaptation\, yet their dynamics across phylogenetically distinct archaeal species and their functional roles during stress remain incompletely understood. This dissertation aims to address some of these gaps through a multi-scale investigation that spans the evolutionary dynamics\, stress-responsive functions\, and direct detection of archaeal tRNA modifications. The first chapter maps this unexplored landscape by applying Ordered Two-Template Relay sequencing (OTTR-seq) across nine archaeal species from diverse and extreme environments. This comparative analysis revealed previously unrecognized\, coordinated modification patterns\, including mutually exclusive methylation patterns in the acceptor stem of hyperthermophiles. Additional comparisons revealed co-evolution of tRNA modifying enzymes\, demonstrating how their domain architectures and substrate specificities have diverged to shape lineage-specific adaptations. Building on these evolutionary observations\, the second chapter investigates the functional role of tRNA modifications in the stress responses of the model halophile Haloferax volcanii. This work reveals how tRNA modification dynamics might balance structural stability against flexibility to manage stress\, focusing on N2\,N2-dimethylguanosine (m22G) at position 26. By integrating tRNA sequencing\, proteomics\, and codon usage data within a linear mixed-effects model\, this work quantifies how the m22G modification status fine-tunes the translation of specific\, codon-biased genes\, establishing it as a modulator of the adaptive stress response. Addressing limitations of reverse-transcription (RT)-based sequencing methods for detecting modifications\, the final chapter explores the use of direct RNA nanopore sequencing. The focus is on archaeosine (G+)\, a modification unique to Archaea that is inaccessible to RT-based sequencing methods. The resulting custom model accurately detects archaeosine in its native species. However\, cross-species comparisons reveal significant challenges with species-specific overfitting\, providing insights into development of universally applicable modification callers. \nEvent Host: Jesse Leavitt\, Ph.D Candidate\, Biomolecular Engineering & Bioinformatics
URL:https://events.ucsc.edu/event/leavitt-j-bmeb-evolutionary-dynamics-functional-adaptations-in-stress-response-and-direct-detection-of-trna-modifications-in-archaea/
LOCATION:Biomedical Sciences Building\, 575 McLaughlin Drive
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