The US-ROK Alliance: the Feasibility of the Continuing US Military Presence in the Republic of Korea

The U.S. – ROK alliance is currently on a high after a decade of fluctuating relations. Both the Obama and Lee administrations have been focused on producing a future plan that takes both nations strategic interests into account while also considering the changing dynamics in the region. The alliance partners have had to overcome differences of opinion on foreign policy, the rise of anti-Americanism in South Korea, the emergence of China as a power in the region, and the continuing nuclear ambitions and instability in North Korea. This paper discusses how the realignment of the force command structure and a unified long-term strategic plan, has effectively modified the USFK to better deal with these wide ranging issues and remain a force of strategic relevance now and in the future of Asia. The USFK therefore remains an important part of the U.S. – ROK alliance that has positive security implications for both the Korean Peninsula and its surrounding region. It has also become the catalyst for stronger all-round relations between the long-time allies, which lead to positive flow-on effects in economic matters such as the U.S. – ROK Free-Trade Agreement (KORUS FTA)

Ergonomic Evaluation and Design Process for Healthcare Products: A Case Study of Patient Transfer Design

Department of Human Factors EngineeringInpatient falls are a critical issue in healthcare facilities. Up to 30% of such falls result in injury, which may in turn lead to impaired rehabilitation and co-morbidity in mental and physical health. One of routine activities that poses high risks of falls of patients is a within-facility patient transfer. Within-facility patient transfer is a high-risk task not only for patients but also for care-givers. Care-givers frequently transfer patients from bed to a wheelchair or wheelchair to bed manually, and it can cause musculoskeletal injuries of the care-giver. Various aid devices such as a powered patient lifter have been introduced to improve the safety of patient transfer and to assist care-givers, but they have not been widely used due to their bulky size and slow operation. To overcome such problems, one of medical robot manufacturers in Korea developed the functional prototype of a semi-powered patient lift and transportation device. The device is equipped with a forward leaning seat to allow easy loading and unloading patients without manual lifting. Since the functionality and usability of the prototype has not been evaluated, it was necessary to conduct thorough evaluation both in fields and laboratory and to come up with redesign goals and strategies. Therefore, this study was aimed to evaluate the functionality and usability of the prototype using various ergonomic evaluation approaches and to redesign the prototype based on the results of the evaluation. In the evaluation process, various methods have been used to understand and identify care-givers??? needs, interaction patterns between the prototype and patients, and safety issues when operating the prototype inside and outside patient rooms through user interview and field observation studies at hospitals. To evaluate the biomechanical advantages over traditional manual transfer methods, a human-subject experiment was also conducted with quantitative assessment of muscle activities, foot reaction forces and transfer time. Then, using the findings of the evaluation, redesign ideas have been made and the prototype has been upgraded to reflect the ideas. The upgraded prototype was evaluated again at hospitals to confirm whether the changes improved the functionality and usability of the device. In this paper, detail procedures for the evaluation and redesign are explained, with related problems and challenges. Also, some ideas for improving the evaluation/redesign processes for healthcare products are proposed for future research and development.ope

Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses

Graph Neural Networks (GNNs) are emerging as a powerful tool for learning from graph-structured data and performing sophisticated inference tasks in various application domains. Although GNNs have been shown to be effective on modest-sized graphs, training them on large-scale graphs remains a significant challenge due to lack of efficient data access and data movement methods. Existing frameworks for training GNNs use CPUs for graph sampling and feature aggregation, while the training and updating of model weights are executed on GPUs. However, our in-depth profiling shows the CPUs cannot achieve the throughput required to saturate GNN model training throughput, causing gross under-utilization of expensive GPU resources. Furthermore, when the graph and its embeddings do not fit in the CPU memory, the overhead introduced by the operating system, say for handling page-faults, comes in the critical path of execution. To address these issues, we propose the GPU Initiated Direct Storage Access (GIDS) dataloader, to enable GPU-oriented GNN training for large-scale graphs while efficiently utilizing all hardware resources, such as CPU memory, storage, and GPU memory with a hybrid data placement strategy. By enabling GPU threads to fetch feature vectors directly from storage, GIDS dataloader solves the memory capacity problem for GPU-oriented GNN training. Moreover, GIDS dataloader leverages GPU parallelism to tolerate storage latency and eliminates expensive page-fault overhead. Doing so enables us to design novel optimizations for exploiting locality and increasing effective bandwidth for GNN training. Our evaluation using a single GPU on terabyte-scale GNN datasets shows that GIDS dataloader accelerates the overall DGL GNN training pipeline by up to 392X when compared to the current, state-of-the-art DGL dataloader.Comment: Under Submission. Source code: https://github.com/jeongminpark417/GID

Quantum Conductance Probing of Oxygen Vacancies in SrTiO3 Epitaxial Thin Film Using Graphene

The quantum Hall conductance in monolayer graphene on an epitaxial SrTiO3 (STO) thin film is studied to understand the role of oxygen vacancies in determining the dielectric properties of STO. As the gate voltage sweep range is gradually increased in our device, we observe systematic generation and annihilation of oxygen vacancies evidenced from the hysteretic conductance behavior in graphene. Furthermore, based on the experimentally observed linear scaling relation between the effective capacitance and the voltage sweep range, a simple model is constructed to manifest the relationship among the dielectric properties of STO with oxygen vacancies. The inherent quantum Hall conductance in graphene can be considered as a sensitive, robust, and non-invasive probe for understanding the electronic and ionic phenomena in complex transition metal oxides without impairing the oxide layer underneath.Comment: 21 pages, 4 figures, 2 supp. figure

Bandwidth-Effective DRAM Cache for GPUs with Storage-Class Memory

We propose overcoming the memory capacity limitation of GPUs with high-capacity Storage-Class Memory (SCM) and DRAM cache. By significantly increasing the memory capacity with SCM, the GPU can capture a larger fraction of the memory footprint than HBM for workloads that oversubscribe memory, achieving high speedups. However, the DRAM cache needs to be carefully designed to address the latency and BW limitations of the SCM while minimizing cost overhead and considering GPU's characteristics. Because the massive number of GPU threads can thrash the DRAM cache, we first propose an SCM-aware DRAM cache bypass policy for GPUs that considers the multi-dimensional characteristics of memory accesses by GPUs with SCM to bypass DRAM for data with low performance utility. In addition, to reduce DRAM cache probes and increase effective DRAM BW with minimal cost, we propose a Configurable Tag Cache (CTC) that repurposes part of the L2 cache to cache DRAM cacheline tags. The L2 capacity used for the CTC can be adjusted by users for adaptability. Furthermore, to minimize DRAM cache probe traffic from CTC misses, our Aggregated Metadata-In-Last-column (AMIL) DRAM cache organization co-locates all DRAM cacheline tags in a single column within a row. The AMIL also retains the full ECC protection, unlike prior DRAM cache's Tag-And-Data (TAD) organization. Additionally, we propose SCM throttling to curtail power and exploiting SCM's SLC/MLC modes to adapt to workload's memory footprint. While our techniques can be used for different DRAM and SCM devices, we focus on a Heterogeneous Memory Stack (HMS) organization that stacks SCM dies on top of DRAM dies for high performance. Compared to HBM, HMS improves performance by up to 12.5x (2.9x overall) and reduces energy by up to 89.3% (48.1% overall). Compared to prior works, we reduce DRAM cache probe and SCM write traffic by 91-93% and 57-75%, respectively.Comment: Published in 2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA'24

Encoding Speaker-Specific Latent Speech Feature for Speech Synthesis

In this work, we propose a novel method for modeling numerous speakers, which enables expressing the overall characteristics of speakers in detail like a trained multi-speaker model without additional training on the target speaker's dataset. Although various works with similar purposes have been actively studied, their performance has not yet reached that of trained multi-speaker models due to their fundamental limitations. To overcome previous limitations, we propose effective methods for feature learning and representing target speakers' speech characteristics by discretizing the features and conditioning them to a speech synthesis model. Our method obtained a significantly higher similarity mean opinion score (SMOS) in subjective similarity evaluation than seen speakers of a best-performing multi-speaker model, even with unseen speakers. The proposed method also outperforms a zero-shot method by significant margins. Furthermore, our method shows remarkable performance in generating new artificial speakers. In addition, we demonstrate that the encoded latent features are sufficiently informative to reconstruct an original speaker's speech completely. It implies that our method can be used as a general methodology to encode and reconstruct speakers' characteristics in various tasks

Hydrogen-bond structure and low-frequency dynamics of electrolyte solutions: Hydration numbers from ab Initio water reorientation dynamics and dielectric relaxation spectroscopy

We present an atomistic simulation scheme for the determination of the hydration number (h) of aqueous electrolyte solutions based on the calculation of the water dipole reorientation dynamics. In this methodology, the time evolution of an aqueous electrolyte solution generated from ab initio molecular dynamics simulations is used to compute the reorientation time of different water subpopulations. The value of h is determined by considering whether the reorientation time of the water subpopulations is retarded with respect to bulk-like behavior. The application of this computational protocol to magnesium chloride (MgCl2 ) solutions at different concentrations (0.6-2.8 mol kg-1 ) gives h values in excellent agreement with experimental hydration numbers obtained using GHz-to-THz dielectric relaxation spectroscopy. This methodology is attractive because it is based on a well-defined criterion for the definition of hydration number and provides a link with the molecular-level processes responsible for affecting bulk solution behavior. Analysis of the ab initio molecular dynamics trajectories using radial distribution functions, hydrogen bonding statistics, vibrational density of states, water-water hydrogen bonding lifetimes, and water dipole reorientation reveals that MgCl2 has a considerable influence on the hydrogen bond network compared with bulk water. These effects have been assigned to the specific strong Mg-water interaction rather than the Cl-water interaction