arXiv subjects

Ken-Tye Yong

Publications and source records attributed to Ken-Tye Yong.

9 recordsLinked to original sources

arXiv2026

Q-BridgeNet: A Quantization Network for Cross-Lingual Sign Language Translation

Most sign language translation (SLT) methods focus on isolated native sign-spoken pairs (e.g., American Sign Language - English). Extending language-specific SLT models to multilingual translation would improve accessibility by enabling communication across diverse sign and spoken language communities. However, existing multilingual SLT approaches still struggle to learn a unified model that minimizes cross-lingual conflicts while capturing shared cross-lingual semantics and preserving language-specific variations across different sign languages. Therefore, we propose Q-BridgeNet, a unified framework for multilingual SLT that jointly mitigates cross-lingual conflicts across both the sign language and spoken language sides. On the sign language side, Q-BridgeNet learns discrete Q-units via adaptive segmentation and residual vector quantization: a shared base codebook provides language-agnostic semantic primitives, while language-specific residual codebooks refine heterogeneous signing semantics. On the spoken language side, a multilingual LLM is fine-tuned to operate in the Q-unit space, leveraging cross-lingual priors to enable a unified SLT model. Experiments on PHOENIX14T, How2Sign, and CSL-Daily show that Q-BridgeNet effectively mitigates cross-lingual conflicts, achieving state-of-the-art performance on native sign-spoken pairs while also demonstrating strong generalization to non-native pairs. Our source code is publicly available at: https://github.com/FengLiQ/Q-BridgeNet

cs.CL↗

arXiv2026

A Unified Spectrum for Turbulence in Microfluidic Flow

We present a predictive master spectrum describing turbulence-like flows in microfluidic systems. Extending Pao's viscous-range closure, the model introduces (i) an adaptive inertial-range slope dependent on measurable dimensionless numbers and (ii) a physics-specific cutoff that captures entropy-producing sinks such as electrokinetic forcing, compliant walls, active stresses, and interfacial tension. This formulation unifies turbulence regimes -- electrokinetic, active, interfacial, and compressible -- within one compact expression. Comparison with reported data reproduces both spectral slopes and dissipation cutoffs while requiring only global observables (velocity, viscosity, Taylor microscale, and forcing strength). The framework provides a design-level predictive tool for turbulent microflows prior to computationally heavy DNS or CFD.

physics.flu-dyn↗

arXiv2026

Universal Quartic Scaling Law for Kerr-Type Interactions: Projection-Law Factorization Across Nonlinear Quantum Platforms

We present a rigorous derivation and numerical validation of a universal projection-law factorization for quartic nonlinear coupling rates across physically distinct platforms. The central result is that observable Kerr-type interactions -- self-Kerr, cross-Kerr, and cross-phase modulation -- factorize into a dimensionless projection coefficient and an intrinsic quartic energy scale. This structure follows from canonical quantization of a quartic interaction projected onto a finite normal-mode basis. We state this result as a formal proposition with clearly enumerated assumptions, provide a proof sketch based on second quantization of the anharmonic potential, and enumerate the domain of validity. We then implement the scaling law as a lightweight computational toolkit (UEFT-Designer) with platform-specific kernels for superconducting circuits, photonic microcavities, and epsilon-near-zero (ENZ) structures. A complete, step-by-step worked example for a superconducting quarton device -- with full uncertainty propagation -- predicts a cross-Kerr rate $χ/2π= 361\pm 13\,\mathrm{MHz}$, agreeing with the independently measured value of $366\pm 0.5\,\mathrm{MHz}$ \cite{Ye2025} to within 1.4\%. A formal error-propagation analysis establishes that the dominant uncertainty is the Josephson-energy extraction uncertainty ($\sim\!2\%$), not the geometric projection factor. Cross-platform validation against five independent experiments spanning eight orders of magnitude in coupling strength confirms the universality of the factorization to within reported experimental uncertainties. The ghost-sector spectral correction introduced in earlier versions is reframed as an optional phenomenological self-energy model with no mandatory role in the validated scaling law.

quant-ph↗

arXiv2026

Generative Modeling in Protein Design: Neural Representations, Conditional Generation, and Evaluation Standards

Generative modeling has become a central paradigm in protein research, extending machine learning beyond structure prediction toward sequence design, backbone generation, inverse folding, and biomolecular interaction modeling. However, the literature remains fragmented across representations, model classes, and task formulations, making it difficult to compare methods or identify appropriate evaluation standards. This survey provides a systematic synthesis of generative AI in protein research, organized around (i) foundational representations spanning sequence, geometric, and multimodal encodings; (ii) generative architectures including $\mathrm{SE}(3)$-equivariant diffusion, flow matching, and hybrid predictor-generator systems; and (iii) task settings from structure prediction and de novo design to protein-ligand and protein-protein interactions. Beyond cataloging methods, we compare assumptions, conditioning mechanisms, and controllability, and we synthesize evaluation best practices that emphasize leakage-aware splits, physical validity checks, and function-oriented benchmarks. We conclude with critical open challenges: modeling conformational dynamics and intrinsically disordered regions, scaling to large assemblies while maintaining efficiency, and developing robust safety frameworks for dual-use biosecurity risks. By unifying architectural advances with practical evaluation standards and responsible development considerations, this survey aims to accelerate the transition from predictive modeling to reliable, function-driven protein engineering.

cs.LG↗

arXiv2026

Computation and Communication Efficient Federated Unlearning via On-server Gradient Conflict Mitigation and Expression

Federated Unlearning (FUL) aims to remove specific participants' data contributions from a trained Federated Learning model, thereby ensuring data privacy and compliance with regulatory requirements. Despite its potential, progress in FUL has been limited due to several challenges, including the cross-client knowledge inaccessibility and high computational and communication costs. To overcome these challenges, we propose Federated On-server Unlearning (FOUL), a novel framework that comprises two key stages. The learning-to-unlearn stage serves as a preparatory learning phase, during which the model identifies and encodes the key features associated with the forget clients. This stage is communication-efficient and establishes the basis for the subsequent unlearning process. Subsequently, on-server knowledge aggregation phase aims to perform the unlearning process at the server without requiring access to client data, thereby preserving both efficiency and privacy. We introduce a new data setting for FUL, which enables a more transparent and rigorous evaluation of unlearning. To highlight the effectiveness of our approach, we propose a novel evaluation metric termed time-to-forget, which measures how quickly the model achieves optimal unlearning performance. Extensive experiments conducted on three datasets under various unlearning scenarios demonstrate that FOUL outperforms the Retraining in FUL. Moreover, FOUL achieves competitive or superior results with significantly reduced time-to-forget, while maintaining low communication and computation costs.

cs.LG↗

arXiv2026

Molecular Dynamics Simulations Reveal PolyQ-Length-Dependent Conformational Changes in Huntingtin Exon-1: Implications for Environmental Co-Solvent Modulation of Aggregation-Prone States

Huntington's disease (HD) is caused by CAG-repeat expansion in HTT, which lengthens the polyglutamine (polyQ) tract in huntingtin (HTT) and promotes misfolding and aggregation. While polyQ-length-dependent aggregation is well established, the atomistic conformational dynamics preceding aggregation remain less defined. Here we perform all-atom molecular dynamics simulations of HTT exon-1 constructs containing the N17 domain, polyQ tracts of clinically relevant lengths (Q21, wildtype; Q40, adult onset threshold; Q70, juvenile onset), and the polyproline (polyP) region. Multi-copy simulations (four chains) were run for 100 ns in explicit SPC/E water using the OPLS-AA force field. We quantified radius of gyration (Rg), solvent-accessible surface area (SASA), root-mean-square deviation (RMSD), and intra-protein hydrogen bonds as proxies for conformational expansion and aggregation propensity. PolyQ expansion drove progressive increases in Rg and SASA, consistent with more extended, solvent-exposed ensembles. We further tested organic co-solvents (methanol, hexane, trichloroethylene; 0.5 to 1.0 M), which modulated these landscapes in a solvent-dependent manner. Trichloroethylene induced marked expansion in Q21 and Q40, whereas methanol produced mild compaction in Q21. To our knowledge, this is the first MD study to systematically examine co-solvent effects on HTT exon-1 conformational dynamics. Although limited sampling precludes definitive mechanistic conclusions, the observed trends suggest that hydrophobic co-solvents can bias HTT exon-1 toward more expanded ensembles, motivating computational studies of gene-environment modulation in HD.

cs.CE↗

arXiv2025

Unified Effective Field Theory for Nonlinear and Quantum Optics

Predicting phenomena that mix few-photon quantum optics with strong field nonlinear optics is hindered by the use of separate theoretical formalisms for each regime. We close this gap with a unified effective field theory valid for frequencies lower than the material-dependent cutoff set by the band gap, plasma frequency, or similar scale. The action couples the electromagnetic gauge field to vector polarisation modes. An isotropic potential generates the optical susceptibilities, while a higher-dimension axion-like term captures magnetoelectric effects; quantisation on the Schwinger-Keldysh contour with doubled BRST ghosts preserves gauge symmetry in dissipative media. One-loop renormalisation-group equations reproduce the measured dispersion of the third-order susceptibility from terahertz to near-visible frequencies after matching a single datum per material. Real-time dynamics solved with a matrix-product-operator engine yield good agreement with published results for GaAs polariton cavities, epsilon-near-zero indium-tin-oxide films and superconducting quarton circuits. The current formulation is limited to these 1-D geometries and sub-cut-off frequencies; higher-dimensional or above-cut-off phenomena will require additional degrees of freedom or numerical methods.

physics.optics↗

arXiv2024

Advancements in Programmable Lipid Nanoparticles: Exploring the Four-Domain Model for Targeted Drug Delivery

Programmable lipid nanoparticles, or LNPs, represent a breakthrough in the realm of targeted drug delivery, offering precise spatiotemporal control essential for the treatment of complex diseases such as cancer and genetic disorders. In order to provide a more modular perspective and a more balanced analysis of the mechanism, this review presents a novel Four-Domain Model that consists of Architecture, Interface, Payload, and Dispersal Domain. We explored the dynamical equilibrium between LNPs components and the surroundings throughout their destiny, from formulation to release. On the basis of this, we delve deep into manufacturing challenges, scalability issues, and regulatory hurdles, associated with the clinical translation of LNP technology. Within the framework focusing on the programmability in each domain, we prioritized patient-centric factors like dosing regimens, administration techniques, and potential consequences. Notably, this review expands to innovative anatomical routes, such as intranasal and intraocular administration, offering a thorough examination of the advantages and disadvantages of each route. We also offered a comprehensive comparison between artificial LNPs and natural exosomes in terms of functionality, biocompatibility, and therapeutic potential. Ultimately, this review highlights the potential of programmable LNPs to evolve into more intelligent, naturally integrated systems, achieving optimal biocompatibility and functionality.

q-bio.BM↗

arXiv2019

Phase-controllable growth of ultrathin 2D magnetic FeTe crystals

Two-dimensional (2D) magnets with intrinsic ferromagnetic/antiferromagnetic (FM/AFM) ordering are highly desirable for future spintronics devices. However, the synthesis of 2D magnetic crystals, especially the direct growth on SiO2/Si substrate, is just in its infancy. Here, we report a chemical vapor deposition (CVD)-based rational growth approach for the synthesis of ultrathin FeTe crystals with controlled structural and magnetic phases. By precisely optimizing the growth temperature (Tgrowth), FeTe nanoplates with either layered tetragonal or non-layered hexagonal phase can be controlled with high-quality. The two controllable phases lead to square and triangular morphologies with a thickness down to 3.6 and 2.8 nm, respectively. More importantly, transport measurements reveal that tetragonal FeTe is antiferromagnetic with a Neel temperature (TN) about 71.8 K, while hexagonal FeTe is ferromagnetic with a Curie temperature (TC) around 220 K. Theoretical calculations indicate that the ferromagnetic order in hexagonal FeTe is originated from a concomitant lattice distortion and the spin-lattice coupling. This study represents a major step forward in the CVD growth of 2D magnetic materials on SiO2/Si substrates and highlights on their potential applications in the future spintronic devices.

cond-mat.mtrl-sci↗