MLGOPerf: An ML Guided Inliner to Optimize Performance

For the past 25 years, we have witnessed an extensive application of Machine Learning to the Compiler space; the selection and the phase-ordering problem. However, limited works have been upstreamed into the state-of-the-art compilers, i.e., LLVM, to seamlessly integrate the former into the optimization pipeline of a compiler to be readily deployed by the user. MLGO was among the first of such projects and it only strives to reduce the code size of a binary with an ML-based Inliner using Reinforcement Learning. This paper presents MLGOPerf; the first end-to-end framework capable of optimizing performance using LLVM's ML-Inliner. It employs a secondary ML model to generate rewards used for training a retargeted Reinforcement learning agent, previously used as the primary model by MLGO. It does so by predicting the post-inlining speedup of a function under analysis and it enables a fast training framework for the primary model which otherwise wouldn't be practical. The experimental results show MLGOPerf is able to gain up to 1.8% and 2.2% with respect to LLVM's optimization at O3 when trained for performance on SPEC CPU2006 and Cbench benchmarks, respectively. Furthermore, the proposed approach provides up to 26% increased opportunities to autotune code regions for our benchmarks which can be translated into an additional 3.7% speedup value.Comment: Version 2: Added the missing Table 6. The short version of this work is accepted at ACM/IEEE CASES 202

Multi-Phase Cross-modal Learning for Noninvasive Gene Mutation Prediction in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the fourth most common cause of cancer-related death worldwide. Understanding the underlying gene mutations in HCC provides great prognostic value for treatment planning and targeted therapy. Radiogenomics has revealed an association between non-invasive imaging features and molecular genomics. However, imaging feature identification is laborious and error-prone. In this paper, we propose an end-to-end deep learning framework for mutation prediction in APOB, COL11A1 and ATRX genes using multiphasic CT scans. Considering intra-tumour heterogeneity (ITH) in HCC, multi-region sampling technology is implemented to generate the dataset for experiments. Experimental results demonstrate the effectiveness of the proposed model.Comment: Accepted version to be published in the 42nd IEEE Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2020, Montreal, Canad

Generation of spectral wings and temporal ultra-short pulse sequence

We study the generation of supercontinuum (SC) with spectral wings and temporal ultra-short pulse sequence by pumping two identical time-delayed pulses in an all-normal dispersion fibre. The flat dispersion profile over the entire wavelength range of the generated SC provides an opportunity to divide these ultra-short pulses roughly evenly. By adjusting the time delay, the temporal width of these ultra-short pulses can be regulated considerably, which accelerates the development in numerous fields such as multi-photon microscopy and ultrafast spectroscopy. Besides, the temporal overlap of two pulses at different instantaneous frequencies leads to the emergence of spectral wings via four-wave mixing. By changing the input power and time delay, spectral wings with variable intensities and ranges can be emitted at different distances, which can expand the SC spectral bandwidth within a limited fibre length

Crown Ether Grafted Graphene Oxide/Chitosan/Polyvinyl Alcohol Nanofiber Membrane for Highly Selective Adsorption and Separation of Lithium Ion

Nanofiber membranes were successfully prepared with crown ether (CE) functionalized graphene oxide (GO), chitosan (CS), and polyvinyl alcohol (PVA) by low-temperature thermally induced liquid–liquid phase separation. The physical and chemical properties and adsorption performance of nanofiber membrane were studied through SEM, FT-IR, XRD, and static adsorption experiments. The results show that the specific surface area of the nanofiber membrane is as high as 101.5 m2∙g−1. The results of static adsorption experiments show that the maximum adsorption capacity of the nanofiber membrane can reach 168.50 mg∙g−1 when the pH is 7.0. In the selective adsorption experiment, the nanofiber membrane showed high selectivity for Li+ in salt lake brine. After five cycles, the material still retains 88.31% of the adsorption capacity. Therefore, it is proved that the material has good regeneration ability

Fault response comparison of LCC–MMC hybrid topologies and conventional HVDC topology

The modular multilevel converter (MMC) can be used to upgrade conventional line commutated converter-based HVDC systems (LCC-HVDC), construct the hybrid topologies of LCC and MMC to acquire better fault responses. In this study, the mechanisms of three kinds of hybrid topologies for dc fault clearance are firstly analysed. Then the fault responses of the three hybrid topologies and the conventional LCC-HVDC topology are detailedly compared, including dc line fault and ac faults at both rectifier side and inverter side. The comparison helps demonstrate the degree of improvement when MMC is adopted to upgrade the conventional LCC-HVDC

Transgenic Arabidopsis thaliana containing increased levels of ATP and sucrose is more susceptible to Pseudomonas syringae.

Disease resistance exerts a fitness cost on plants, presumably due to the extra consumption of energy and carbon. In this study, we examined whether transgenic Arabidopsis thaliana with increased levels of ATP and sucrose is more resistant or susceptible to pathogen infection. Lines of A. thaliana over-expressing purple acid phosphatase 2 (AtPAP2) (OE lines) contain increased levels of ATP and sucrose, with improved growth rate and seed production. Compared to wild type (WT) and pap2 lines, the OE lines were more susceptible to several Pseudomonas syringae pv. tomato (Pst) strains carrying AvrRpm1, AvrRpt2 AvrRps4, AvrPtoB, HrcC and WT strain DC3000. The increased susceptibility of the OE lines to Pst strains cannot solely be attributed to the suppressed expression of R-genes but must also be attributed to the suppression of downstream signaling components, such as MOS2, EDS1 and EDS5. Before infection, the levels of salicylic acid (SA) and jasmonic acid (JA) precursor OPDA were similar in the leaves of OE, pap2 and WT plants, whereas the levels of JA and its derivative JA-Ile were significantly lower in the leaves of OE lines and higher in the pap2 line. The expression of JA marker defense gene PDF1.2 was up-regulated in the OE lines compared to the WT prior to Pst DC3000 infection, but its expression was lower in the OE lines after infection. In summary, high fitness Arabidopsis thaliana exhibited altered JA metabolism and broad suppression of R-genes and downstream genes as well as a higher susceptibility to Pst infections

Large-Scale Spatial Patterns of Grassland Community Properties in the Inner Mongolia Autonomous Region, China

Mapping large-scale spatial patterns of grassland community properties in the Inner Mongolia Autonomous Region of China and learning how they are affected by environmental factors are vital to understand grassland changes in response to climate change and human activity. We collected data on six grassland community properties across 198 sample plots in the Inner Mongolia Autonomous Region: height, coverage, aboveground biomass (AGB), belowground biomass (BGB), soil bulk density (SBD), and species number (SN). We then analyzed the relationship between these and a range of environmental factors, including elevation, mean annual temperature (MAT), mean annual precipitation (MAP), >= 10 C annual accumulated temperature, humidity index, and normalized difference vegetation index (NDVI), using correlation and regression analysis. On the basis of the regression equation, we undertook a multifactor model using ArcGIS, in which different weights were assigned to each factor according to the degree of fitness between the estimated results and measured data. We then mapped the spatial distribution of grassland community properties in Inner Mongolia. We found a significant correlation between all of the grassland community properties and environmental factors measured (P < 0.01). In terms of spatial patterns, SN, height, coverage, AGB, and BGB were positively correlated with the transition from desert grassland to meadow grassland. The community properties model provided good results, with average accuracies of 53.05-90.21% and R-2 values of 0.40-0.68 (P < 0.01) across the six grassland community properties. The multifactor comprehensive model provides significant correlation between the predicted results and measured data. Therefore, this could be used as a basis for future studies on Inner Mongolia grasslands and to understand temporal and spatial changes of grassland in response to human activity and climate change. (C) 2020 The Society for Range Management. Published by Elsevier Inc. All rights reserved

Electron-beam radiation induced degradation of silicon nitride and its impact to semiconductor failure analysis by TEM

By in-situ transmission electron microscopy (TEM), we performed a detailed study on the electron-beam radiation damage to nanostructured silicon nitride thin-film process layers in a typical semiconductor NVM device. It was found that high-dose electron-beam radiation at 200 kV led to rapid degradation of silicon nitride process layers, i.e. thin-downing of nanostructured silicon nitride, inter-diffusion of O and N, the formation of bubble-like defects and segregation of N at neighbouring interfaces. Further detailed analysis revealed that radiation-induced modification in the microstructure and chemical composition of silicon nitride layers could be ascribed to the electron radiation induced knock-on damage and ionization damage. The radiation enhanced diffusion (RED) accounted for the continuous thin-down of the nitride process layer and the formation of bubble-like defects in thick nitride spacer process layers. The work well demonstrated the electron-beam sensitivity of nanostructured silicon nitride materials in the semiconductor devices, and thus may give useful information about electron-dose control during TEM failure analysis of the semiconductor devices containing nanostructured silicon nitride process layers

The overview of the impacts of electron radiation on semiconductor failure analysis by SEM, FIB and TEM

The paper briefly overviewed electron-beam radiation damage and its impacts on physical failure analysis by SEM, FIB and TEM. Based on our electron radiation study on some typical electron-beam sensitive materials, we discussed some interesting results associated with electron radiation damage to Lk/ULK, silicon nitride and CoFeB thin film materials in semiconductor and MRAM devices. The details included radiation induced microstructure changes., material diffusion and phase transformation. The underlying mechanism was also briefly discussed for electron radiation damage to different materials