November 2024

TechTalk – Thermal Insulation in Materials for Efficient Energy Conversion

Innovation Wing Two, TechTalk / By

December 19, 2024 (Thursday) 4:30-5:30pm
To enhance the thermoelectric conversion efficiency of materials, the thermal conduction needs to be suppressed, and the lattice dynamics and the thermal transport mechanisms must be better understood. Lattice thermal conduction of conventional solids is dominated by phonon propagation; however, diffuson-like thermal transport can become predominant in materials with ultralow thermal conductivity. New strategies for a simultaneous suppression of both propagative and diffusive thermal transports will be discussed in this talk based on state-of-the-art theories. Zintl compounds were recently found to exhibit exceptional thermoelectric properties. A thorough experimental study of the thermoelectric transport and carrier properties of Zintl compounds will be discussed. It will be shown that a high figure of merit over a broad temperature range can be realized through the suppression of the intrinsic carrier excitation.

TechTalk – Optimizing Distributed Large Model Training in AI Clouds

Innovation Wing Two, TechTalk / By

December 12, 2024 (Thursday) 4:30-5:30pm
Distributed training using a large number of devices has been widely adopted for learning large deep learning models. Improving distributed training efficiency is critical for time, resource and energy consumption of large model learning. In this talk, I will introduce recent research works in my group on optimizing distributed training parallelisms for effective training acceleration and maximal resource utilization. Especially, we have designed optimized strategies and systems for operator sharding, computation and communication scheduling for SPMD parallelism (e.g., in Mixture-of-Experts model training) in both homogeneous and heterogeneous AI clusters, as well as dynamic micro-batching and pipelining to tackle sequence length variation in multi-task model training (e.g., Large Language Model training).

TechTalk – Medical Image Representation Learning via Cross-supervision between Images and Text Reports

Innovation Wing Two, TechTalk / By

December 5, 2024 (Thursday) 4:30-5:30pm
Pre-training lays the foundation for recent successes in radiograph analysis supported by deep learning. It learns transferable image representations by conducting large-scale fully- or self-supervised learning on a source domain; however, supervised pre-training requires a complex and labour-intensive two-stage human-assisted annotation process, whereas self-supervised learning cannot compete with the supervised paradigm. To tackle these issues, we propose a cross-supervised methodology called reviewing free-text reports for supervision (REFERS), which acquires free supervision signals from the original radiology reports accompanying the radiographs. The proposed approach employs a vision transformer and is designed to learn joint representations from multiple views within every patient study. REFERS outperforms its transfer learning and self-supervised learning counterparts on four well-known X-ray datasets under extremely limited supervision. Moreover, REFERS even surpasses methods based on a source domain of radiographs with human-assisted structured labels; it therefore has the potential to replace canonical pre-training methodologies.

TechTalk – Wafer-scale Structural Coloration Using Gray-scale Lithographic Fabrication

Innovation Wing Two, TechTalk / By

November 28, 2024 (Thursday) 4:30-5:30pm
Structural colors use nanostructured building blocks or thin films to resonantly reflect or scatter light to generate colors and can exhibit higher resolution, saturation, and durability than pigment-based colors. To create structural color based paintings, it is essential to develop a capability of spatially varying the dimensions of these nanosized structures. Recently we reported a high-throughput and wafer-scale nanopatterning method by combining interference lithography and grayscale-patterned secondary exposure (IL-GPSE) to spatially modulate nanostructure feature sizes on large scale while maintaining sufficiently high resolution. Here, we employ the IL-GPSE method in the fabrication of wafer-scale structural color paintings, which can improve the patterning efficiency by orders of magnitude when compared with e-beam lithography. The fabrication techniques developed in this work have unique potentials for broader applications in biomedical sensing, spectral filtering, anti-counterfeiting or encryption, etc.

HKAE TechTalk – The Grain Boundary Ratchet: How to Engineer Grain Size

Innovation Wing Two, TechTalk / By

January 9, 2024 (Thursday) 4:30-5:30pm
We demonstrate that grain boundaries (GBs) behave as Brownian ratchets, exhibiting direction-dependent mobilities and unidirectional motion under oscillatory driving forces or cyclic thermal annealing. We observed these phenomena for nearly all nonsymmetric GBs but not for symmetric ones. Our observations build on molecular dynamics and phase-field crystal simulations for a wide range of GB types and driving forces in both bicrystal and polycrystalline microstructures. We corroborate these simulation results through in situ experimental observations. We analyze these results with a Markov chain model and explore the implications of GB ratchet behavior for materials processing and microstructure tailoring.