Guest Artist Recital: Eunkyung Son, Cello Minji Kwon, Piano

Kemp Recital Hall February 11, 2018 Sunday Afternoon 3:00p.m

THE EFFECT OF KNEE FATIGUE ON SHOCK ABSORPTION DURING CUTTING MOVEMENT AFTER JUMP-LANDING

This study aimed to investigate the effect of knee fatigue on shock absorption during cutting movements after jump-landings. Twenty-four healthy subjects performed cutting movements following jump-landings from 40 cm height, and Pre, Post-SO%, and Post- 30% of their pre-test measured maximum toque, used by isokinetic flexion/extension of the knee. Results showed that Post 30% fatigue were associated with decreased ROM of the knee, increased ROM of the ankle, decreased load rate, increased knee stiffness, decreased peak power of the knee, decreased work of the knee, and increased work of the ankle. We suggest that the post-30% fatigue appears to be the threshold to quantify the fatigue level. This study indicate that increases in fatigue modify the strategy shock absorption from knee to ankle in cutting movements following jump landings

TURNING CHARACTERISTICS IN PATIENTS WITH PARKINSON'S DISEASE DURING TIMED UP AND GO

This study aimed to investigate turning characteristics of patients with PD, using 3D analysis during the TUG test, to examine associations with the severity of PD. A total of 30 individuals performed the TUG test 10 patients with Hoehn and Yarh stages 2.5 and 3.0 PD (group I), 10 patients with H&Y stage 2.0 PD (group II), and 10 healthy elderly controls. Walking speed; step length; ROM of the hip, knee, and shoulder joint; foot clearance height; were significantly different between PD patients and controls. Step length and foot clearance height were significantly different between group I and group II. In conclusion, the TUG test may be a useful task for identifying turning characteristics of the severity of PO and to differentiate between PO patients and controls

Breaking Temporal Consistency: Generating Video Universal Adversarial Perturbations Using Image Models

As video analysis using deep learning models becomes more widespread, the vulnerability of such models to adversarial attacks is becoming a pressing concern. In particular, Universal Adversarial Perturbation (UAP) poses a significant threat, as a single perturbation can mislead deep learning models on entire datasets. We propose a novel video UAP using image data and image model. This enables us to take advantage of the rich image data and image model-based studies available for video applications. However, there is a challenge that image models are limited in their ability to analyze the temporal aspects of videos, which is crucial for a successful video attack. To address this challenge, we introduce the Breaking Temporal Consistency (BTC) method, which is the first attempt to incorporate temporal information into video attacks using image models. We aim to generate adversarial videos that have opposite patterns to the original. Specifically, BTC-UAP minimizes the feature similarity between neighboring frames in videos. Our approach is simple but effective at attacking unseen video models. Additionally, it is applicable to videos of varying lengths and invariant to temporal shifts. Our approach surpasses existing methods in terms of effectiveness on various datasets, including ImageNet, UCF-101, and Kinetics-400.Comment: ICCV 202

Sketch-based Video Object Localization

We introduce Sketch-based Video Object Localization (SVOL), a new task aimed at localizing spatio-temporal object boxes in video queried by the input sketch. We first outline the challenges in the SVOL task and build the Sketch-Video Attention Network (SVANet) with the following design principles: (i) to consider temporal information of video and bridge the domain gap between sketch and video; (ii) to accurately identify and localize multiple objects simultaneously; (iii) to handle various styles of sketches; (iv) to be classification-free. In particular, SVANet is equipped with a Cross-modal Transformer that models the interaction between learnable object tokens, query sketch, and video through attention operations, and learns upon a per-frame set matching strategy that enables frame-wise prediction while utilizing global video context. We evaluate SVANet on a newly curated SVOL dataset. By design, SVANet successfully learns the mapping between the query sketches and video objects, achieving state-of-the-art results on the SVOL benchmark. We further confirm the effectiveness of SVANet via extensive ablation studies and visualizations. Lastly, we demonstrate its transfer capability on unseen datasets and novel categories, suggesting its high scalability in real-world application

The study of the microstructure evolution of nano/meso-porous metal using the Monte Carlo simulation / A finite element method simulation of the stress distribution on the grain boundaries in a three-dimensional polycrystalline microstructure under uniaxial loading

Using the Monte Carlo simulation, we investigated the microstructure evolution in a nano/meso-porous metal. Monte Carlo Potts model have been carried out microstructural evolution of two-phase polycrystalline materials in which grain growth is controlled by grain boundary diffusion. We used Kawasaki-like dynamics and Metropolis acceptation probability, which ensures the evolution towards equilibrium. Evolution of the simulated microstructures is different morphology depending on the MCS(Monte Carlo step), domain temperature, grain boundary energies and heterophase interfacial energies. Also, we compared to simulated and real nanoporous metal microstructure

Short-Term Effects of Experimental Warming and Precipitation Manipulation on Soil Microbial Biomass C and N, Community Substrate Utilization Patterns and Community Composition

Soil microorganisms are major drivers of soil carbon (C) cycling; however, the response of these microorganisms to climate change remains unclear. In the present study, we investigated how 18 months of multifactor climate treatments (warmed air temperature by 3 °C and decreased or increased precipitation manipulation by 30%) affected soil microbial biomass C and nitrogen (N), community substrate utilization patterns, and community composition. Decreased and increased precipitation significantly reduced microbial biomass C by 13.5% and 24.9% and microbial biomass N by 22.9% and 17.6% in unwarmed plots, respectively (P < 0.01). Warming enhanced community substrate utilization by 89.8%, 20.4%, and 141.4% in the natural, decreased, and increased precipitation plots, respectively. Particularly, warming significantly enhanced the utilization of amine and carboxylic acid substrates among all precipitation manipulation plots. Compared with the natural air temperature with natural precipitation treatment, other treatments affected fungal community richness by −0.9% to 33.6% and reduced the relative abundance of the dominant bacterial and fungal groups by 0.5% to 6.8% and 4.3% to 10.7%, respectively. The warming and/or precipitation manipulation treatments significantly altered Zygomycota abundance (P < 0.05). Our results indicate that climate change drivers and their interactions may cause changes in soil microbial biomass C and N, community substrate utilization patterns, and community composition, particularly for the fungal community, and shifts in the microorganism community may further shape the ecosystems function.Scopu

Seismic Vulnerability Assessment and Mapping of Gyeongju, South Korea Using Frequency Ratio, Decision Tree, and Random Forest

The main purpose of this study was to compare the prediction accuracies of various seismic vulnerability assessment and mapping methods. We applied the frequency ratio (FR), decision tree (DT), and random forest (RF) methods to seismic data for Gyeongju, South Korea. A magnitude 5.8 earthquake occurred in Gyeongju on 12 September 2016. Buildings damaged during the earthquake were used as dependent variables, and 18 sub-indicators related to seismic vulnerability were used as independent variables. Seismic data were used to construct a model for each method, and the models&rsquo; results and prediction accuracies were validated using receiver operating characteristic (ROC) curves. The success rates of the FR, DT, and RF models were 0.661, 0.899, and 1.000, and their prediction rates were 0.655, 0.851, and 0.949, respectively. The importance of each indicator was determined, and the peak ground acceleration (PGA) and distance to epicenter were found to have the greatest impact on seismic vulnerability in the DT and RF models. The constructed models were applied to all buildings in Gyeongju to derive prediction values, which were then normalized to between 0 and 1, and then divided into five classes at equal intervals to create seismic vulnerability maps. An analysis of the class distribution of building damage in each of the 23 administrative districts showed that district 15 (Wolseong) was the most vulnerable area and districts 2 (Gangdong), 18 (Yangbuk), and 23 (Yangnam) were the safest areas