VTechWorks
VTechWorks provides global access to Virginia Tech scholarship, including journal articles, books, theses, dissertations, conference papers, slide presentations, technical reports, working papers, administrative documents, videos, images, and more by faculty, students, and staff. Faculty can deposit items to VTechWorks from Elements, including journal articles covered by the University open access policy. Email vtechworks@vt.edu for help.
Open Access Policy
Virginia Tech's open access policy enables researchers to deposit the accepted version of scholarly articles with no embargo.
Theses and Dissertations
Virginia Tech was first in the world to require ETDs in 1997, and continues to add scans of older theses and dissertations.
Open Textbooks
More than 50 freely available and openly licensed textbooks are among our most downloaded items.
Communities in VTechWorks
Select a community to browse its collections.
Recent Submissions
Toward an Understanding of Empathic In-Vehicle Agents
Choe, Mungyeong (Virginia Tech, 2026-10-30)
Driver anger degrades vehicle control and safety, and in-vehicle conversational agents are increasingly proposed as a means of supporting drivers through emotionally difficult moments. Empathy is commonly treated as a desirable property of such agents, yet an agent has no emotional capacity in the sense the construct presupposes: what it has is a repertoire of utterances. This dissertation therefore operationalizes empathy as a verbal design strategy and asks which realization of it supports drivers, and whether the version that is best received is also the version that works best. Study 1 (N = 60) compared cognitive empathy, which names the driver's likely state and explains why the situation warrants it, against affective empathy, which expresses a parallel reaction of the agent's own, and against navigation-only speech, under a pre-drive anger induction. Study 2 (N = 48) then crossed the same three empathy conditions with agent embodiment, comparing a physically present humanoid robot against a voice-only agent. Study 3 (N = 48) crossed empathy condition with conversational interactivity, comparing a pre-scripted agent against one powered by a large language model while holding the robot, the voice, the timing, and the empathic utterances constant. All three studies measured self-reported affect, physiological response, perceptions of the agent, and vehicle control. The findings converge on a separation between how an agent is received and what it does. Cognitive empathy was associated with the most controlled vehicle handling in Study 1 and with the highest rate of active hazard response in Study 3, and it incurred far less of the penalty in perceived safety that emotionally expressive speech carried in Studies 2 and 3. Affective empathy, by contrast, was rated warmest, most animate, and most socially present, and was associated with faster cardiac settling in Study 1. However, it did not outperform cognitive empathy on any measure of vehicle control, and it reduced how safe the agent felt to be with in Studies 2 and 3. Embodiment lowered every dimension of perceived workload largely without changing how empathic content was evaluated. Conversational interactivity produced the strongest perceptual effects observed in this dissertation, nearly doubling perceived social presence, but these gains did not carry over: conversational capability had no effect on vehicle control and influenced physiological response only at the peak of the cardiac reaction. When drivers could converse freely with the agent, they asked overwhelmingly about information and company, with expressions of emotion accounting for a small fraction of what they said. Across the three studies, drivers' evaluations of the agent did not move together with their physiological responses or their driving behavior: ratings did not predict physiological response at the individual level, and the design that drivers judged warmest was not the design associated with the most controlled driving. On this basis, the dissertation argues that empathic in-vehicle agents should be evaluated on driver state and behavior rather than on social impression alone, that cognitive empathy offers the most defensible default for safety-critical settings, and that embodiment and conversational capabilities may contribute more to user acceptance than to physiological or behavioral regulation.
Multi-Fidelity Particle Flow Filters based on Log Homotopy
Bhattacharjee, Abhinab; Popov, Andrey A.; Subrahmanya, Amit N.; Sandu, Adrian (Virginia Tech. Computational Science Laboratory, 2026-10-27)
This work proposes a new Multi-Fidelity Particle Flow Filter (MFPFF) based on a linear control variate framework and multilevel Monte Carlo methods.
Conventional particle filters often suffer from weight collapse and particle degeneracy in high-dimensional spaces. Particle flow filters address this by smoothly transporting prior particles toward the posterior distribution, thereby avoiding the need for resampling. This work constructs multi-fidelity particle flow filters by augmenting a small number of expensive high-fidelity particles (full-order model runs) with a large number of inexpensive low-resolution particles (reduced-order surrogates constructed via intrusive Galerkin projection). These surrogates act as control variates to enhance the statistical estimate of the full-order posterior, yielding enhanced analysis accuracy at comparable computational cost. The work focuses on two particle flows based on log homotopy, the stochastic Gromov flow and deterministic Exact flow. Numerical experiments with the Korteweg-De Vries (KdV) and Quasi-Geostrophic (QG) test problems demonstrate that the proposed MFPFF achieves accuracy comparable to high-fidelity simulations while significantly reducing computational expense.
Exploring Organogenesis in American Chestnut
Kocz, Sebastian James (Virginia Tech, 2026-10-29)
American chestnut (Castanea dentata) was once a major presence in the forests of the Eastern United States. However, fifty years after chestnut blight, a canker disease caused by Cryphonectria parasitica, was first observed in the Bronx Zoo in 1904, the tree was considered functionally extinct. Efforts to increase disease resistance in the species via genetic engineering are ongoing, however they would be aided by improvements in the efficiency and speed of in vitro regeneration. To begin optimizing a de novo organogenesis regeneration protocol for C. dentata, we tested seven different callus induction media formulations as well as Agrobacterium-mediated transformation of explants with ten different morphogenic regulators (MRs) that had been reported to increase shoot induction when overexpressed in other plant species. We identified a high-performing callus induction media (WPM with 2 mg BAP and 0.5 mg NAA) that produced callus on 87.5% of C. dentata leaf explants after being kept in a dark environment for 3 weeks. In our three transformation experiments with MRs, while we found some MRs to cause significantly more callus to form than others, we found no significant improvement in callus induction in explants transformed with MRs compared to GFP-transformed controls. In some cases, the GFP-transformed control performed significantly better. Across both studies, four adventitious shoots were observed. These represent the first reported shoots formed by C. dentata through de novo organogenesis and give us optimism that with more optimization, a consistent protocol can be generated.
Designing Physiological Connections in Artificial Intelligence: Towards integration of an AI collaborator into a musical ensemble for group aesthetics and control using biofeedback with affective states
Kerobo, Justin Adebayo (Virginia Tech, 2026-10-29)
We are artistic and creative human beings. As expert systems encroach on creative or artistic processes, rather than vilify or create certainty, how can we co-create with AI as an agent and have some synergy for the future? While some systems move towards excluding human interaction in favor of AI-generated processes, there is also a growing space for more physiological human-computer interactions. Artificial intelligence in music is often evalu- ated through autonomous generation, leaving unresolved how computational systems can support collaboration while preserving human agency. This dissertation develops Affective Computing in Musical Collaboration (ACMC), an interdisciplinary framework that treats affect as expressed, sensed, reported, negotiated, and acted upon within musical interaction rather than only as a classification label. Informed by participatory and transition-design principles, the project integrated a Russell circumplex interface, low-cost MAX30102 heart- rate and blood-oxygen sensing, Grove skin-conductance sensing, Pd-L2Ork/L2Ork Tweeter, Python, Open Sound Control, event logging, statistical analysis, and machine learning. A related symbolic-generation study using a human-classified emotional MIDI dataset found that continuous-concatenated transformer conditioning performed best among the evaluated conditioning methods. The empirical work followed a validation-to-interaction sequence.
Phase 1 involved eight participants and 80 passive-listening trials across ten high-arousal, mixed-valence excerpts. All trials were reconstructed, heart-rate change showed a reproducible association with target valence, and self-reported valence retained modest stimulus ordering. However, baseline-relative directional concordance ranged from 33.8% to 47.5%, below the prespecified 80% progression criterion. Phase 2 was therefore interpreted as an exploratory feasibility study. Fifty participants completed 600 interactive Solo, Transition, and Drop trials. Skin conductance increased during and after interaction, heart rate rose most consistently after events, and SpO2 showed a modest post-event decrease. Drop examples were rated more arousing than Solo and Transition examples, whereas valence varied more by individual example than by musical function. Complete-case System Usability Scale scores averaged 81.80/100, and post-task and qualitative results indicated strong usability, enjoyment, learning, and creative exploration. The findings establish the feasibility and expressive value of an open-source, psychophysiologically responsive musical interface, but not a universal emotion decoder. Physiological and audio-derived affect estimates agreed weakly with independent retrospective ratings; participant-grouped models produced negative held-out R2 values, and function classification remained near chance. The planned Biopac reference-device comparison was not completed, so medical-grade sensor accuracy is not claimed. Ensemble consensus and audience coupling were also not tested. The dissertation contributes the ACMC framework, an open research stack, a validation model separating acquisition, physiological sensitivity, agreement, prediction, usability, and collaboration, and a design direction for personalized, transparent, mixed-initiative musical AI that treats physiology as contextual evidence rather than unquestioned authority.
Connecting the Opens: Open Access Week 2026
Lener, Edward F.; Walz, Anita R.; Borunda, Luis; Sadler, Jesse; Petters, Jonathan L.; Surprenant, Aimée (Virginia Tech, 2026-10-19)
The 2026 Connecting the Opens panel discussion features brief overviews of open access, open educational resources, open publishing, and open data to provide some general information on the topics. Finally, presenters will discuss how aspects of open have impacted their career development as well as how they have incorporated open practices into their research and scholarship, and the audience will be invited to participate in the conversation.