arXiv subjects

Jungeun Lee

Publications and source records attributed to Jungeun Lee.

6 recordsLinked to original sources

arXiv2026

Learning Safe-by-Design Neural Network Controllers

Safety filters constructed from control barrier functions (CBFs) are commonly appended to pre-trained neural network controllers to enforce safety requirements. However, this decoupled design with hand-tuned, fixed CBF parameters often fails to adapt to the underlying controller, yielding overly conservative solutions. Thus, given a valid CBF, we address these limitations by jointly learning a neural network controller and neural-network-parameterized CBF parameters, enforcing the resulting affine safety constraints by construction and avoiding an online quadratic program (QP) safety filter at run time. To further improve computational efficiency and scalability, we introduce a lightweight projection architecture that enforces constraints without full constraint enumeration. Extensive simulation evaluations demonstrate reliable, scalable safety constraint satisfaction at reduced computational cost.

eess.SY↗

arXiv2026

CAffNet: Hard Constraint-Affine Neural Networks

We present a novel framework for embedding hard constraint satisfaction into neural network (NN) architectures, specifically feedforward neural networks and transformers, with input-dependent affine constraints of arbitrary cardinality. Traditional constraint enforcement approaches either rely on penalty-based soft constraints, which offer no guarantee of satisfaction, or on post-processing methods that enforce constraints after the NN is trained, which may lead to suboptimality. We introduce a trainable constraint-affine (CAffine) layer into NNs, yielding CAffNet, which goes beyond enforcing affine constraints via fixed orthogonal or parallel projections and enables joint optimization with network parameters. Moreover, we impose no restrictions on the constraint space dimensions and establish that our construction preserves the universal approximation properties of NNs, while providing provable guarantees on constraint adherence for all inputs. Experimental validation demonstrates robust performance across diverse domains requiring guaranteed constraint satisfaction.

cs.LG↗

arXiv2025

AutiHero: Engaging Parents in Creating Personalized, Multi-path Social Narratives for Autistic Children

Social narratives help autistic children understand and navigate social situations through stories. To ensure effective practice, however, they often require significant time and effort from parents in customizing the narrative materials and delivering repeated instructions on them. We present AutiHero, a generative AI (GenAI)-based social narrative system, which supports parents to create personalized, multi-path stories targeting specific behavior of their autistic children, while enabling them to explore behavioral choices and causal consequences together in reading. A two-week deployment study with 16 autistic child-parent dyads showed that parents actively created, adapted, and read stories with their children, with increased confidence in everyday behavioral guidance. Our work contributes real-world-contextualized text+image content creation approaches harnessing GenAI, ensuring user-aligned application in sensitive contexts involving autistic children and their parents.

cs.HC↗

arXiv2025

Quantum Computing and Cybersecurity Education: A Novel Curriculum for Enhancing Graduate STEM Learning

Quantum computing is an emerging paradigm with the potential to transform numerous application areas by addressing problems considered intractable in the classical domain. However, its integration into cyberspace introduces significant security and privacy challenges. The exponential rise in cyber attacks, further complicated by quantum capabilities, poses serious risks to financial systems and national security. The scope of quantum threats extends beyond traditional software, operating system, and network vulnerabilities, necessitating a shift in cybersecurity education. Traditional cybersecurity education, often reliant on didactic methods, lacks hands on, student centered learning experiences necessary to prepare students for these evolving challenges. There is an urgent need for curricula that address both classical and quantum security threats through experiential learning. In this work, we present the design and evaluation of EE 597: Introduction to Hardware Security, a graduate level course integrating hands-on quantum security learning with classical security concepts through simulations and cloud-based quantum hardware. Unlike conventional courses focused on quantum threats to cryptographic systems, EE 597 explores security challenges specific to quantum computing itself. We employ a mixed-methods evaluation using pre and post surveys to assess student learning outcomes and engagement. Results indicate significant improvements in students' understanding of quantum and hardware security, with strong positive feedback on course structure and remote instruction (mean scores: 3.33 to 3.83 on a 4 point scale).

cs.CR↗

arXiv2024

Bidirectional Temporal Diffusion Model for Temporally Consistent Human Animation

We introduce a method to generate temporally coherent human animation from a single image, a video, or a random noise. This problem has been formulated as modeling of an auto-regressive generation, i.e., to regress past frames to decode future frames. However, such unidirectional generation is highly prone to motion drifting over time, generating unrealistic human animation with significant artifacts such as appearance distortion. We claim that bidirectional temporal modeling enforces temporal coherence on a generative network by largely suppressing the motion ambiguity of human appearance. To prove our claim, we design a novel human animation framework using a denoising diffusion model: a neural network learns to generate the image of a person by denoising temporal Gaussian noises whose intermediate results are cross-conditioned bidirectionally between consecutive frames. In the experiments, our method demonstrates strong performance compared to existing unidirectional approaches with realistic temporal coherence.

cs.CV↗

arXiv2019

Direct Visualization of Perm-Selective Ion Transportation

Perm-selective ion transportation in a nanoscale structure has been extensively studied with aids of nanofabrication technology for a decade. While theoretical and experimental advances pushed the phenomenon to seminal innovative applications, its basic observation has relied only on an indirect analysis such as current-voltage relation or fluorescent imaging adjacent to the nanostructures. Here we experimentally, for the first time, demonstrated a direct visualization of perm-selective ion transportation through the nanostructures using an ionic plasma generation. A micro/nanofluidic device was employed for a micro bubble formation, plasma negation and penetration of the plasma through the nanojunction. The direct observation provided a keen evidence of perm-selectivity, i.e. allowing cationic species and rejecting anionic species. Furthermore, we can capture the plasma of Li+, which has lower mobility than Na+ in aqueous state, passed the nanojunction faster than Na+ due to the absence of hydrated shells around Li+. This simple, but essential visualization technique would be effective means not only for advancing the fundamental nanoscale electrokinetic study but also for providing the insight of new innovative engineering applications.

physics.app-ph↗