Analysis of radial oscillations of gas bubbles in Newtonian or viscoelastic mediums under acoustic excitation

Acoustic cavitation plays an important role in a broad range of biomedical, chemical and oceanic engineering problems. For example, kidney stone can be crushed into the powder (being discharged naturally) by the acoustic cavitation generated by carefully controlled focused ultrasonic beams. Therefore, the prediction of generation of acoustic cavitation is essential to the aforementioned emerging non-invasive technique for kidney stone crushing. The objective of this PhD program is to study the generation of acoustic cavitation (e.g. through rectified mass diffusion across bubble interface) theoretically in the Newtonian fluids (e.g. water) or viscoelastic mediums (e.g. human soft tissue) under acoustic excitation of single or dual frequency. The compressibility and the viscosity of the liquid, heat and mass transfer across bubble-medium interface are all considered in this study. During this PhD program, the established works in the literature on the above topic have been re-examined. More physically general formulas of natural frequency and damping of gas bubble oscillations in Newtonian or viscoelastic mediums has been derived and further employed for solving the problem of bubble growth under acoustic field (i.e. rectified mass diffusion). For rectified mass diffusion of gas bubbles in Newtonian liquids, the predictions have been improved for high-frequency region of megahertz and above. Effects of medium viscoelasticity and dual-frequency acoustic excitation on rectified mass diffusion have also been studied. To facilitate the fast growth of bubble under acoustic field, dynamic-frequency and dual-frequency techniques have been proposed and demonstrated

Integrated Transformers Inference Framework for Multiple Tenants on GPU

In recent years, Transformer models have gained prominence in the deep learning domain, serving as the foundation for a wide array of applications, including Natural Language Processing (NLP) and Computer Vision (CV). These models have become essential for numerous inference tasks, but their implementation often faces challenges related to GPU utilization and system throughput. Typically, current GPU-based inference frameworks treat each model individually, which results in suboptimal resource management and decreased performance. To address these limitations, we introduce ITIF: Integrated Transformers Inference Framework for multiple tenants with a shared backbone. ITIF allows multiple tenants to share a single backbone Transformer model on a single GPU, consolidating operators from various multi-tenant inference models. This approach significantly optimizes GPU utilization and system throughput. Our proposed framework, ITIF, marks a considerable advancement towards enhancing the efficiency of deep learning, particularly for large-scale cloud providers hosting numerous models with a shared backbone. In our experiments, we extensively evaluated the performance of ITIF in comparison with traditional baselines. We conducted tests on a variety of deep learning tasks, including NLP and CV tasks. We found that ITIF consistently outperformed the baselines, with improvements in performance by up to 2.40 times. In conclusion, our research highlights the potential benefits of adopting the ITIF framework for improving the efficiency and scalability of Transformer-based deep learning systems. By enabling multiple tenants to share a single backbone model, ITIF provides an innovative solution to address the challenges faced by large-scale cloud providers in optimizing GPU utilization and system throughput. As such, ITIF presents a promising direction for further research and development in the field of deep learning

Uncovering multifunctional mechano-intelligence in and through phononic metastructures harnessing physical reservoir computing

The recent advances in autonomous systems have prompted a strong demand for the next generation of adaptive structures and materials to possess more built-in intelligence in their mechanical domain, the so-called mechano-intelligence (MI). Previous MI attempts mainly focused on specific designs and case studies to realize limited aspects of MI, and there is a lack of a systematic foundation in constructing and integrating the different elements of intelligence in an effective and efficient manner. Here, we propose a new approach to create the needed foundation in realizing integrated multifunctional MI via a physical reservoir computing (PRC) framework. That is, to concurrently embody computing power and the various elements of intelligence, namely perception, decision-making, and commanding, directly in the mechanical domain, advancing from conventional adaptive structures that rely solely on add-on digital computers and massive electronics to achieve intelligence. As an exemplar platform, we construct a mechanically intelligent phononic metastructure with the integrated elements of MI by harnessing the PRC power hidden in their high-degree-of-freedom nonlinear dynamics. Through analyses and experimental investigations, we uncover multiple adaptive structural functions ranging from self-tuning wave controls to wave-based logic gates. This research will provide the basis for creating future new structures that would greatly surpass the state of the art - such as lower power consumption, more direct interactions, and much better survivability in harsh environment or under cyberattacks. Moreover, it will enable the addition of new functions and autonomy to systems without overburdening the onboard computers.Comment: 15 pages, 4 figure

MegDet: A Large Mini-Batch Object Detector

The improvements in recent CNN-based object detection works, from R-CNN [11], Fast/Faster R-CNN [10, 31] to recent Mask R-CNN [14] and RetinaNet [24], mainly come from new network, new framework, or novel loss design. But mini-batch size, a key factor in the training, has not been well studied. In this paper, we propose a Large MiniBatch Object Detector (MegDet) to enable the training with much larger mini-batch size than before (e.g. from 16 to 256), so that we can effectively utilize multiple GPUs (up to 128 in our experiments) to significantly shorten the training time. Technically, we suggest a learning rate policy and Cross-GPU Batch Normalization, which together allow us to successfully train a large mini-batch detector in much less time (e.g., from 33 hours to 4 hours), and achieve even better accuracy. The MegDet is the backbone of our submission (mmAP 52.5%) to COCO 2017 Challenge, where we won the 1st place of Detection task

Anomalous wavefront control via nonlinear acoustic metasurface through second-harmonic tailoring and demultiplexing

We propose a nonlinear acoustic metasurface concept by exploiting the nonlinearity of the locally resonant unit cells formed by curved beams. The analytical model is established to explore the nonlinear phenomenon, specifically the second-harmonic generation (SHG) of the acoustic waveguide and validated through numerical and experimental studies. Novel nonlinear acoustic metasurfaces are developed to demultiplex different frequency components and achieve anomalous wavefront control of SHG in the transmitted region. To this end, we demonstrate wave steering, wave focusing, and self-bending propagation. Our results show that the proposed nonlinear metasurface provides an effective and efficient platform to achieve significant SHG, and separate different harmonic components for wavefront control of individual harmonics. Overall, this study offers new avenues to harness nonlinear effects for acoustic wavefront tailoring and develops new potential toward advanced technologies to manipulate acoustic waves