Efficient Molecular Dynamics Simulation on Reconfigurable Models with MultiGrid Method

In the field of biology, MD simulations are continuously used to investigate biological studies. A Molecular Dynamics (MD) system is defined by the position and momentum of particles and their interactions. The dynamics of a system can be evaluated by an N-body problem and the simulation is continued until the energy reaches equilibrium. Thus, solving the dynamics numerically and evaluating the interaction is computationally expensive even for a small number of particles in the system. We are focusing on long-ranged interactions, since the calculation time is O(N^2) for an N particle system. In this dissertation, we are proposing two research directions for the MD simulation. First, we design a new variation of Multigrid (MG) algorithm called Multi-level charge assignment (MCA) that requires O(N) time for accurate and efficient calculation of the electrostatic forces. We apply MCA and back interpolation based on the structure of molecules to enhance the accuracy of the simulation. Our second research utilizes reconfigurable models to achieve fast calculation time. We have been working on exploiting two reconfigurable models. We design FPGA-based MD simulator implementing MCA method for Xilinx Virtex-IV. It performs about 10 to 100 times faster than software implementation depending on the simulation accuracy desired. We also design fast and scalable Reconfigurable mesh (R-Mesh) algorithms for MD simulations. This work demonstrates that the large scale biological studies can be simulated in close to real time. The R-Mesh algorithms we design highlight the feasibility of these models to evaluate potentials with faster calculation times. Specifically, we develop R-Mesh algorithms for both Direct method and Multigrid method. The Direct method evaluates exact potentials and forces, but requires O(N^2) calculation time for evaluating electrostatic forces on a general purpose processor. The MG method adopts an interpolation technique to reduce calculation time to O(N) for a given accuracy. However, our R-Mesh algorithms require only O(N) or O(logN) time complexity for the Direct method on N linear R-Mesh and N¡¿N R-Mesh, respectively and O(r)+O(logM) time complexity for the Multigrid method on an X¡¿Y¡¿Z R-Mesh. r is N/M and M = X¡¿Y¡¿Z is the number of finest grid points

The Effect Of Internal Control Weakness On Investment Efficiency

This paper examines whether material weakness in internal accounting control is negatively associated with investment efficiency in Korea. Since internal accounting control weakness drives poor accounting quality and poor accounting quality exacerbates information asymmetry between firms and outside capital suppliers, managerial investment cannot be monitored effectively which result in over- and/or under- investment. Since internal accounting system is closely related to corporate governance, weak internal accounting control is often associated with poor corporate governance, and this control environment makes it hard to monitor managerial opportunistic behavior, causing abnormal investment such as over- and/or under- investment.  We find that firms with internal accounting control weakness tend to make over- and under- investment. We also find the number of weakness in internal accounting control is negatively related to investment efficiency. In addition, three types of qualified review opinion - overall company level weakness, account-specific weakness and disclaimer review opinion due to scope limitation - are differentially affected to investment efficiency; disclaimer review opinion is present the most severe problem in internal accounting control that drives over- and under- investment. Our findings suggest weak internal accounting control provides poor monitoring to manager and cannot restrain managerial inefficient investment decision.

Basic Enhancement Strategies When Using Bayesian Optimization for Hyperparameter Tuning of Deep Neural Networks

Compared to the traditional machine learning models, deep neural networks (DNN) are known to be highly sensitive to the choice of hyperparameters. While the required time and effort for manual tuning has been rapidly decreasing for the well developed and commonly used DNN architectures, undoubtedly DNN hyperparameter optimization will continue to be a major burden whenever a new DNN architecture needs to be designed, a new task needs to be solved, a new dataset needs to be addressed, or an existing DNN needs to be improved further. For hyperparameter optimization of general machine learning problems, numerous automated solutions have been developed where some of the most popular solutions are based on Bayesian Optimization (BO). In this work, we analyze four fundamental strategies for enhancing BO when it is used for DNN hyperparameter optimization. Specifically, diversification, early termination, parallelization, and cost function transformation are investigated. Based on the analysis, we provide a simple yet robust algorithm for DNN hyperparameter optimization - DEEP-BO (Diversified, Early-termination-Enabled, and Parallel Bayesian Optimization). When evaluated over six DNN benchmarks, DEEP-BO mostly outperformed well-known solutions including GP-Hedge, BOHB, and the speed-up variants that use Median Stopping Rule or Learning Curve Extrapolation. In fact, DEEP-BO consistently provided the top, or at least close to the top, performance over all the benchmark types that we have tested. This indicates that DEEP-BO is a robust solution compared to the existing solutions. The DEEP-BO code is publicly available at <uri>https://github.com/snu-adsl/DEEP-BO</uri>

Hemophagocytic Syndrome in a Patient with Acute Tubulointerstitial Nephritis Secondary to Hepatitis A Virus Infection

Hepatitis A virus (HAV) infection is generally a self-limited disease, but the infection in adults can be serious, to be often complicated by acute kidney injury (AKI) and rarely by virus-associated hemophagocytic syndrome (VAHS). Our patient, a 48-yr-old man, was diagnosed with HAV infection complicated by dialysis-dependent AKI. His kidney biopsy showed acute tubulointerstitial nephritis with massive infiltration of activated macrophages and T cells, and he progressively demonstrated features of VAHS. With hemodialysis and steroid treatment, he was successfully recovered

Brain computed tomography angiography in postcardiac arrest patients and neurologic outcome

Objective This study aimed to analyze intracranial vessels using brain computed tomography angiography (CTA) and scoring systems to diagnose brain death and predict poor neurologic outcomes of postcardiac arrest patients. Methods Initial brain CTA images of postcardiac arrest patients were analyzed using scoring systems to determine a lack of opacification and diagnose brain death. The primary outcome was poor neurologic outcome, which was defined as cerebral performance category score 3 to 5. The frequency, sensitivity, specificity, positive predictive value, negative predictive value, and area under receiver operating characteristic curve for the lack of opacification of each vessel and for each scoring system used to predict poor neurologic outcomes were determined. Results Patients with poor neurologic outcomes lacked opacification of the intracranial vessels, most commonly in the vein of Galen, both internal cerebral veins, and the mid cerebral artery (M4). The 7-score results (P=0.04) and 10-score results were significantly different (P=0.04) between outcome groups, with an area under receiver operating characteristic of 0.61 (range, 0.48 to 0.72). The lack of opacification of each intracranial vessel and all scoring systems exhibited high specificity (100%) and positive predictive values (100%) for predicting poor neurologic outcomes. Conclusion Lack of opacification of vessels on brain CTA exhibited high specificity for predicting poor neurologic outcomes of patients after cardiac arrest

Pilot study on a rewarming rate of 0.15°C/hr versus 0.25°C/hr and outcomes in post cardiac arrest patients

Objective Cerebral hemodynamic and metabolic changes may occur during the rewarming phase of targeted temperature management in post cardiac arrest patients. Yet, studies on different rewarming rates and patient outcomes are limited. This study aimed to investigate post cardiac arrest patients who were rewarmed with different rewarming rates after 24 hours of hypothermia and the association of these rates to the neurologic outcomes. Methods This study retrospectively investigated post cardiac arrest patients treated with targeted temperature management and rewarmed with rewarming rates of 0.15°C/hr and 0.25°C/hr. The association of the rewarming rate with poor neurologic outcomes (cerebral performance category score, 3 to 5) was investigated. Results A total of 71 patients were analyzed (0.15°C/hr, n=36; 0.25°C/hr, n=35). In the comparison between 0.15°C/hr and 0.25°C/hr, the poor neurologic outcome did not significantly differ (24 [66.7%] vs. 25 [71.4%], respectively; P=0.66). In the multivariate analysis, the rewarming rate of 0.15°C/hr was not associated with the 1-month neurologic outcome improvement (odds ratio, 0.54; 95% confidence interval, 0.16 to 1.69; P=0.28). Conclusion The rewarming rates of 0.15°C/hr and 0.25°C/hr were not associated with the neurologic outcome difference in post cardiac arrest patients