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Objective. Recently, many electrocardiogram (ECG) classification algorithms using deep learning have been proposed. Because the ECG characteristics vary across datasets owing to variations in factors such as recorded hospitals and the race of participants, the model needs to have a consistently high generalization performance across datasets. In this study, as part of the PhysioNet/Computing in Cardiology Challenge (PhysioNet Challenge) 2021, we present a model to classify cardiac abnormalities from the 12- and the reduced-lead ECGs. Approach. To improve the generalization performance of our earlier proposed model, we adopted a practical suite of techniques, i.e. constant-weighted cross-entropy loss, additional features, mixup augmentation, squeeze/excitation block, and OneCycle learning rate scheduler. We evaluated its generalization performance using the leave-one-dataset-out cross-validation setting. Furthermore, we demonstrate that the knowledge distillation from the 12-lead and large-teacher models improved the performance of the reduced-lead and small-student models. Main results. With the proposed model, our DSAIL SNU team has received Challenge scores of 0.55, 0.58, 0.58, 0.57, and 0.57 (ranked 2nd, 1st, 1st, 2nd, and 2nd of 39 teams) for the 12-, 6-, 4-, 3-, and 2-lead versions of the hidden test set, respectively. Significance. The proposed model achieved a higher generalization performance over six different hidden test datasets than the one we submitted to the PhysioNet Challenge 2020.

Single-stage deammonification (SSD) processes have been successfully operated using the step-feeding strategy to treat high-strength NH

The importance of physically contiguous memory has increased in modern computing environments, including both low- and high-end systems. Existing physically contiguous memory allocators generally have critical limitations. For example, the most commonly adopted solution, the memory reservation technique, wastes a significant amount of memory space. Scatter/Gather direct memory access (DMA) and input-output memory management units (IOMMUs) avoid this problem by utilizing additional hardware for address space virtualization. However, additional hardware means an increase in costs and power consumption, which is especially disadvantageous for small systems and they do not provide real contiguous memory. Linux Contiguous Memory Allocator (CMA) aims to provide both contiguous memory allocation and to maximize memory utilization based on page migration, but they suffer from unpredictably long latency and a high probability of allocation failure. Therefore, we introduce a new solution to this problem, the guaranteed contiguous memory allocator (GCMA). This guarantees efficient memory space utilization, short latency, and successful allocation. The GCMA uses a reservation scheme and increases memory utilization by sharing the memory with immediately discardable data. Our evaluation of a GCMA on a Raspberry Pi 2 finds a latency that is 15-130 times lower compared to a CMA, and a latency that is up to 10 times lower when taking a photo. Using a large working set in a memory-fragmented high-end system, the GCMA is able to produce a 2.27x speedup.

For the fabrication of high-performance top-gated MoS2 transistors, a uniform atomic layer deposition (ALD) of an ultrathin high-k gate dielectric film without abnormal leakage paths on a MoS2 channel is required. In this study, we fabricated a ~5.2 nm-thick monolithic HfO2 gate dielectric film by utilizing an e-beam-evaporated Hf seed layer (target thickness of 3 nm) prior to the ALD of a HfO2 film (~2 nm). The Hf seed layer was fully converted to HfO2 without metallic residues during the following ALD process, without damages to the Raman and photoluminescence characteristics of the underlying MoS2. The conformal and pinhole-free ALD of the subsequent HfO2 film was verified using conductive atomic force microscopy. In addition, operation of a top-gated MoS2 transistor was demonstrated by integrating the Hf-seeded HfO2 gate dielectric film.

Read-copy update (RCU) can provide ideal scalability for read-mostly workloads, but some believe that it provides only poor performance for updates. This belief is due to the lack of RCU-centric update synchronization mechanisms. RCU instead works with a range of update-side mechanisms, such as locking. In fact, many developers embrace simplicity by using global locking. Logging, hardware transactional memory, or fine-grained locking can provide better scalability, but each of these approaches has limitations, such as imposing overhead on readers or poor scalability on non-uniform memory access (NUMA) systems, mainly due to their lack of NUMA-aware design principles. This paper introduces an RCU extension (RCX) that provides highly scalable RCU updates on NUMA systems while retaining RCU's read-side benefits. RCX is a software-based synchronization mechanism combining hardware transactional memory (HTM) and traditional locking based on our NUMA-aware design principles for RCU. Micro-bench-marks on a NUMA system having 144 hardware threads show RCX has up to 22.6 times better performance and up to 145 times lower HTM abort rates compared to a state-of-the-art RCU/HTM combination. To demonstrate the effectiveness and applicability of RCX, we have applied RCX to parallelize some of the Linux kernel memory management system and an in-memory database system. The optimized kernel and the database show up to 24 and 17 times better performance compared to the original version, respectively.

Modern workloads tend to have huge working sets and low locality. Despite this trend, the capacity of DRAM has not been increased enough to accommodate such huge working sets. Therefore, memory management mechanisms optimized for such modern workloads are widely required today. For such optimizations, knowing the data access pattern of given workloads is essential. However, manually extracting such patterns from huge and complex workloads is exhaustive. Worse yet, existing memory access analysis tools incur unacceptably high overheads for unnecessarily detailed analysis results.To mitigate this situation, we introduce a tool that is designed for data access pattern tracing. Two core mechanisms in this tool, a region-based sampling and an adaptive region adjustment, allow users to limit the tracing overhead in a bounded range regardless of the size and complexity of target workloads, while preserving the quality of results. Our empirical evaluations that conducted with 20 realistic workloads show the high quality, low overhead, and a potential use case of this tool.

Organic light-emitting diodes (OLEDs) have been widely studied because of their various advantages. OLEDs are multi-layered structures consisting of organic and inorganic materials arranged in a heterojunction; the nature of adhesion at their heterogeneous interfaces has a significant effect on their properties. In this study, the origin of macroscopic adhesion was explored in OLEDs using a combination of microscopy techniques applied at different length scales. The different techniques allowed the identification of layers exposed by a peel test, which aided direct characterization of their macroscopic adhesion. Further, the contribution of each exposed layer to macroscopic adhesion could be determined through an analysis of photographic images. Finally, analysis of the local roughness and adhesion confirmed that the interface between an anode and emission layer could play a predominant role in determining the nature of macroscopic adhesion in OLEDs. These results may provide guidelines for exploring the origin of macroscopic adhesion properties through a combination of various microscopy techniques.

Hysteresis loop analysis via piezoresponse force microscopy (PFM) is typically performed to probe the existence of ferroelectricity at the nanoscale. However, such an approach is rather complex in accurately determining the pure contribution of ferroelectricity to the PFM. Here, we suggest a facile method to discriminate the ferroelectric effect from the electromechanical (EM) response through the use of frequency dependent ac amplitude sweep with combination of hysteresis loops in PFM. Our combined study through experimental and theoretical approaches verifies that this method can be used as a new tool to differentiate the ferroelectric effect from the other factors that contribute to the EM response.

Even though nanoscale analysis of magnetic properties is of significant interest, probing methods are relatively less developed compared to the significance of the technique, which has multiple potential applications. Here, we demonstrate an approach for probing various magnetic properties associated with eddy current, coil current and magnetic domains in magnetic inductors using multidimensional magnetic force microscopy (MMFM). The MMFM images provide combined magnetic responses from the three different origins, however, each contribution to the MMFM response can be differentiated through analysis based on the bias dependence of the response. In particular, the bias dependent MMFM images show locally different eddy current behavior with values dependent on the type of materials that comprise the MI. This approach for probing magnetic responses can be further extended to the analysis of local physical features.

N 2 and NO gas addition to F 2 /Ar remote plasmas during chemical dry etching (CDE) of low- k SiOCH layer was effective in increasing the etch rate, but the addition of O 2 decreased the etch rate. And, the injection of NO gas directly into the reactor increased the SiOCH etch rate most significantly. The addition of N 2 or NO gas contributes to an effective removal of oxygen in the SiOCH layer, by forming NO 2 and HNO 3 by-products, and of carbon species in the SiOCH layer by forming CF 4 by-product, which leads to enhancement of SiF 4 formation and in turn increase in the SiOCH etch rate.

We explored the transfer of a single-layered graphene membrane assisted by substrate adhesion. A relatively larger adhesion force was measured on the SiO2 substrate compared with its van der Waals contribution, which is expected to result from the additional contribution of the chemical bonding force. Density functional theory calculations verified that the strong adhesion force was indeed accompanied by chemical bonding. The transfer of single-layered graphene and subsequent deposition of the dielectric layer were best performed on the SiO2 substrate exhibiting a larger adhesion force. This study suggests the selection and/or modification of the underlying substrate for proper transfer of graphene as well as other 2D materials similar to graphene.

Since 1990's, Snapshot Isolation (SI) has been widely studied, and it was successfully deployed in commercial and open-source database engines. Berenson et al. showed that data consistency can be violated under SI. Recently, a new class of Serializable SI algorithms (SSI) has been proposed to achieve serializable execution while still allowing concurrency between reads and updates.