SS-IL: Separated Softmax for Incremental Learning

We consider class incremental learning (CIL) problem, in which a learning agent continuously learns new classes from incrementally arriving training data batches and aims to predict well on all the classes learned so far. The main challenge of the problem is the catastrophic forgetting, and for the exemplar-memory based CIL methods, it is generally known that the forgetting is commonly caused by the prediction score bias that is injected due to the data imbalance between the new classes and the old classes (in the exemplar-memory). While several methods have been proposed to correct such score bias by some additional post-processing, e.g., score re-scaling or balanced fine-tuning, no systematic analysis on the root cause of such bias has been done. To that end, we analyze that computing the softmax probabilities by combining the output scores for all old and new classes could be the main source of the bias and propose a new CIL method, Separated Softmax for Incremental Learning (SS-IL). Our SS-IL consists of separated softmax (SS) output layer and ratio-preserving (RP) mini-batches combined with task-wise knowledge distillation (TKD), and through extensive experimental results, we show our SS-IL achieves very strong state-of-the-art accuracy on several large-scale benchmarks. We also show SS-IL makes much more balanced prediction, without any additional post-processing steps as is done in other baselines

Accuracy Improvement of Discharge Measurement with Modification of Distance Made Good Heading

Remote control boats equipped with an Acoustic Doppler Current Profiler (ADCP) are widely accepted and have been welcomed by many hydrologists for water discharge, velocity profile, and bathymetry measurements. The advantages of this technique include high productivity, fast measurements, operator safety, and high accuracy. However, there are concerns about controlling and operating a remote boat to achieve measurement goals, especially during extreme events such as floods. When performing river discharge measurements, the main error source stems from the boat path. Due to the rapid flow in a flood condition, the boat path is not regular and this can cause errors in discharge measurements. Therefore, improvement of discharge measurements requires modification of boat path. As a result, the measurement errors in flood flow conditions are 12.3–21.8% before the modification of boat path, but 1.2–3.7% after the DMG modification of boat path. And it is considered that the modified discharges are very close to the observed discharge in the flood flow conditions. In this study, through the distance made good (DMG) modification of the boat path, a comprehensive discharge measurement with high accuracy can be achieved

Prediction Model for Random Variation in FinFET Induced by Line-Edge-Roughness (LER)

As the physical size of MOSFET has been aggressively scaled-down, the impact of process-induced random variation (RV) should be considered as one of the device design considerations of MOSFET. In this work, an artificial neural network (ANN) model is developed to investigate the effect of line-edge roughness (LER)-induced random variation on the input/output transfer characteristics (e.g., off-state leakage current (Ioff), subthreshold slope (SS), saturation drain current (Id,sat), linear drain current (Id,lin), saturation threshold voltage (Vth,sat), and linear threshold voltage (Vth,lin)) of 5 nm FinFET. Hence, the prediction model was divided into two phases, i.e., “Predict Vth” and “Model Vth”. In the former, LER profiles were only used as training input features, and two threshold voltages (i.e., Vth,sat and Vth,lin) were target variables. In the latter, however, LER profiles and the two threshold voltages were used as training input features. The final prediction was then made by feeding the output of the first model to the input of the second model. The developed models were quantitatively evaluated by the Earth Mover Distance (EMD) between the target variables from the TCAD simulation tool and the predicted variables of the ANN model, and we confirm both the prediction accuracy and time-efficiency of our model

Flood Frequency Analysis for the Annual Peak Flows Simulated by an Event-Based Rainfall-Runoff Model in an Urban Drainage Basin

The proper assessment of design flood is a major concern for many hydrological applications in small urban watersheds. A number of approaches can be used including statistical approach and the continuous simulation and design storm methods. However, each method has its own limitations and assumptions being applied to the real world. The design storm method has been widely used for a long time because of the simplicity of the method, but three critical assumptions are made such as the equality of the return periods between the rainfall and corresponding flood quantiles and the selections of the rainfall hyetograph and antecedent soil moisture conditions. Continuous simulation cannot be applied to small urban catchments with quick responses of runoff to rainfall. In this paper, a new flood frequency analysis for the simulated annual peak flows (FASAP) is proposed. This method employs the candidate rainfall events selected by considering a time step order of five minutes and a sliding duration without any assumptions about the conventional design storm method in an urban watershed. In addition, the proposed methodology was verified by comparing the results with the conventional method in a real urban watershed

Intracranial Pressure Patterns and Neurological Outcomes in Out-of-Hospital Cardiac Arrest Survivors after Targeted Temperature Management: A Retrospective Observational Study

We aimed to investigate intracranial pressure (ICP) changes over time and the neurologic prognosis for out-of-hospital cardiac arrest (OHCA) survivors who received targeted temperature management (TTM). ICP was measured immediately after return of spontaneous circulation (ROSC) (day 1), then at 24 h (day 2), 48 h (day 3), and 72 h (day 4), through connecting a lumbar drain catheter to a manometer or a LiquoGuard machine. Neurological outcomes were determined at 3 months after ROSC, and a poor neurological outcome was defined as Cerebral Performance Category 3–5. Of the 91 patients in this study (males, n = 67, 74%), 51 (56%) had poor neurological outcomes. ICP was significantly higher in the poor outcome group at each time point except day 4. ICP elevation was highest between days 2 and 3 in the good outcome group, and between days 1 and 2 in the poor outcome group. However, there was no difference in total ICP elevation between the poor and good outcome groups (3.0 vs. 3.1; p = 0.476). All OHCA survivors who had received TTM had elevated ICP, regardless of neurologic prognosis. However, the changing pattern of ICP levels differed depending on the neurological outcome