Proposed searches for candidate sources of gravitational waves in a nearby core-collapse supernova survey

Gravitational wave bursts in the formation of neutron stars and black holes in energetic core-collapse supernovae (CC-SNe) are of potential interest to LIGO-Virgo and KAGRA. Events nearby are readily discovered using moderately sized telescopes. CC-SNe are competitive with mergers of neutron stars and black holes, if the fraction producing an energetic output in gravitational waves exceeds about 1%. This opportunity motivates the design of a novel Sejong University Core-CollapsE Supernova Survey (SUCCESS), to provide triggers for follow-up searches for gravitational waves. It is based on the 76 cm Sejong university telescope (SUT) for weekly monitoring of nearby star-forming galaxies, i.e., M51, M81-M82 and blue dwarf galaxies from the unified nearby galaxy catalog with an expected yield of a few hundred per year. Optical light curves will be resolved for the true time-of-onset for probes of gravitational waves by broadband time-sliced matched filtering

Transcriptional regulatory networks of tumor-associated macrophages that drive malignancy in mesenchymal glioblastoma.

BACKGROUND: Glioblastoma (GBM) is a complex disease with extensive molecular and transcriptional heterogeneity. GBM can be subcategorized into four distinct subtypes; tumors that shift towards the mesenchymal phenotype upon recurrence are generally associated with treatment resistance, unfavorable prognosis, and the infiltration of pro-tumorigenic macrophages. RESULTS: We explore the transcriptional regulatory networks of mesenchymal-associated tumor-associated macrophages (MA-TAMs), which drive the malignant phenotypic state of GBM, and identify macrophage receptor with collagenous structure (MARCO) as the most highly differentially expressed gene. MARCO CONCLUSIONS: Collectively, our study characterizes the global transcriptional profile of TAMs driving mesenchymal GBM pathogenesis, providing potential therapeutic targets for improving the effectiveness of GBM immunotherapy

Machine-Learning-Based Compact Modeling for Sub-3-nm-Node Emerging Transistors

In this paper, we present an artificial neural network (ANN)-based compact model to evaluate the characteristics of a nanosheet field-effect transistor (NSFET), which has been highlighted as a next-generation nano-device. To extract data reflecting the accurate physical characteristics of NSFETs, the Sentaurus TCAD (technology computer-aided design) simulator was used. The proposed ANN model accurately and efficiently predicts currents and capacitances of devices using the five proposed key geometric parameters and two voltage biases. A variety of experiments were carried out in order to create a powerful ANN-based compact model using a large amount of data up to the sub-3-nm node. In addition, the activation function, physics-augmented loss function, ANN structure, and preprocessing methods were used for effective and efficient ANN learning. The proposed model was implemented in Verilog-A. Both a global device model and a single-device model were developed, and their accuracy and speed were compared to those of the existing compact model. The proposed ANN-based compact model simulates device characteristics and circuit performances with high accuracy and speed. This is the first time that a machine learning (ML)-based compact model has been demonstrated to be several times faster than the existing compact model

An Efficient Statistical Analysis Methodology and Its Application to High-Density DRAMs

In this work, a new approach for the statistical worst case of full-chip circuit performance and parametric yield prediction, using both the Modified-Principal Component Analysis (MPCA) and the Gradient Method (GM), is proposed and verified. This method enables designers not only to predict the standard deviations of circuit performances but also track the circuit performances associated with the process shift using wafer test structure measurements. This new method is validated experimentally during the development and production of high density DRAMs

A Systematic IP and Bus Subsystem Modeling for Platform-Based System Design

The topic on platform-based system modeling has received a great deal of attention today. One of the important tasks that significantly affect the effectiveness and efficiency of the system modeling is the modeling of IP components and communication between IPs. To be effective, it is generally accepted that the system modeling should be performed in two steps; In the first step, a fast but some inaccurate system modeling is considered to facilitate the simultaneous development of software and hardware. The second step then refines the models of the software and hardware blocks (i.e., IPs) to increase the simulation accuracy for the system performance analysis. Here, one critical factor required for a successful system modeling is a systematic modeling of the IP blocks and bus subsystem connecting the IPs. In this respect, this work addresses the problem of systematic modeling of the IPs and bus subsystem in different levels of refinements. In the experiments, we found that by applying our proposed IP and bus modeling methods to the MPEG-4 application, we are able to achieve 4x performance improvement and at the same time, reduce the software development time by 35%, compared to that by conventional modeling methods. 1