Observation of Ultrahigh Mobility Surface States in a Topological Crystalline Insulator by Infrared Spectroscopy

Topological crystalline insulators (TCIs) possess metallic surface states protected by crystalline symmetry, which are a versatile platform for exploring topological phenomena and potential applications. However, progress in this field has been hindered by the challenge to probe optical and transport properties of the surface states owing to the presence of bulk carriers. Here we report infrared (IR) reflectance measurements of a TCI, (001) oriented $Pb_{1-x}Sn_{x}Se$ in zero and high magnetic fields. We demonstrate that the far-IR conductivity is unexpectedly dominated by the surface states as a result of their unique band structure and the consequent small IR penetration depth. Moreover, our experiments yield a surface mobility of 40000 $cm^{2}/(Vs)$, which is one of the highest reported values in topological materials, suggesting the viability of surface-dominated conduction in thin TCI crystals. These findings pave the way for exploring many exotic transport and optical phenomena and applications predicted for TCIs

JDsearch: A Personalized Product Search Dataset with Real Queries and Full Interactions

Recently, personalized product search attracts great attention and many models have been proposed. To evaluate the effectiveness of these models, previous studies mainly utilize the simulated Amazon recommendation dataset, which contains automatically generated queries and excludes cold users and tail products. We argue that evaluating with such a dataset may yield unreliable results and conclusions, and deviate from real user satisfaction. To overcome these problems, in this paper, we release a personalized product search dataset comprised of real user queries and diverse user-product interaction types (clicking, adding to cart, following, and purchasing) collected from JD.com, a popular Chinese online shopping platform. More specifically, we sample about 170,000 active users on a specific date, then record all their interacted products and issued queries in one year, without removing any tail users and products. This finally results in roughly 12,000,000 products, 9,400,000 real searches, and 26,000,000 user-product interactions. We study the characteristics of this dataset from various perspectives and evaluate representative personalization models to verify its feasibility. The dataset can be publicly accessed at Github: https://github.com/rucliujn/JDsearch.Comment: Accepted to SIGIR 202

Synergistic Influence of Local Climate Zones and Wind Speeds on the Urban Heat Island and Heat Waves in the Megacity of Beijing, China

Large-scale modifications to urban underlying surfaces owing to rapid urbanization have led to stronger urban heat island (UHI) effects and more frequent urban heat wave (HW) events. Based on observations of automatic weather stations in Beijing during the summers of 2014–2020, we studied the interaction between HW events and the UHI effect. Results showed that the UHI intensity (UHII) was significantly aggravated (by 0.55°C) during HW periods compared to non-heat wave (NHW) periods. Considering the strong impact of unfavorable weather conditions and altered land use on the urban thermal environment, we evaluated the modulation of HW events and the UHI effect by wind speed and local climatic zones (LCZs). Wind speeds in urban areas were weakened due to the obstruction of dense high-rise buildings, which favored the occurrence of HW events. In detail, 35 HW events occurred over the LCZ1 of a dense high-rise building area under low wind speed conditions, which was much higher than that in other LCZ types and under high wind speed conditions (< 30 HW events). The latent heat flux in rural areas has increased more due to the presence of sufficient water availability and more vegetation, while the increase in heat flux in urban areas is mainly in the form of sensible heat flux, resulting in stronger UHI effect during HW periods. Compared to NHW periods, lower boundary layer and wind speed in the HW events weakened the convective mixing of air, further expanding the temperature gap between urban and rural areas. Note that LCZP type with its high-density vegetation and water bodies in the urban park area generally exhibited, was found to have a mitigating effect on the UHI, whilst at the same time increasing the frequency and duration of HW events during HW periods. Synergies between HWs and the UHI amplify both the spatial and temporal coverage of high-temperature events, which in turn exposes urban residents to additional heat stress and seriously threatens their health. The findings have important implications for HWs and UHII forecasts, as well as for scientific guidance on decision-making to improve the thermal environment and to adjust the energy structure

DeepBurning-MixQ: An Open Source Mixed-Precision Neural Network Accelerator Design Framework for FPGAs

Mixed-precision neural networks (MPNNs) that enable the use of just enough data width for a deep learning task promise significant advantages of both inference accuracy and computing overhead. FPGAs with fine-grained reconfiguration capability can adapt the processing with distinct data width and models, and hence, can theoretically unleash the potential of MPNNs. Nevertheless, commodity DPUs on FPGAs mostly emphasize generality and have limited support for MPNNs especially the ones with lower data width. In addition, primitive DSPs in FPGAs usually have much larger data width than that is required by MPNNs and haven't been sufficiently co-explored with MPNNs yet. To this end, we propose an open source MPNN accelerator design framework specifically tailored for FPGAs. In this framework, we have a systematic DSP-packing algorithm to pack multiple lower data width MACs in a single primitive DSP and enable efficient implementation of MPNNs. Meanwhile, we take DSP packing efficiency into consideration with MPNN quantization within a unified neural network architecture search (NAS) framework such that it can be aware of the DSP overhead during quantization and optimize the MPNN performance and accuracy concurrently. Finally, we have the optimized MPNN fine-tuned to a fully pipelined neural network accelerator template based on HLS and make best use of available resources for higher performance. Our experiments reveal the resulting accelerators produced by the proposed framework can achieve overwhelming advantages in terms of performance, resource utilization, and inference accuracy for MPNNs when compared with both handcrafted counterparts and prior hardware-aware neural network accelerators on FPGAs.Comment: Accepted by 2023 IEEE/ACM International Conference on Computer-Aided Design (ICCAD

Development of one-step SYBR Green real-time RT-PCR for quantifying bovine viral diarrhea virus type-1 and its comparison with conventional RT-PCR

<p>Abstract</p> <p>Background</p> <p>Bovine viral diarrhea virus (BVDV) is a worldwide pathogen in cattle and acts as a surrogate model for hepatitis C virus (HCV). One-step real-time fluorogenic quantitative reverse transcription polymerase chain reaction (RT-PCR) assay based on SYBR Green I dye has not been established for BVDV detection. This study aims to develop a quantitative one-step RT-PCR assay to detect BVDV type-1 in cell culture.</p> <p>Results</p> <p>One-step quantitative SYBR Green I RT-PCR was developed by amplifying cDNA template from viral RNA and using <it>in vitro </it>transcribed BVDV RNA to establish a standard curve. The assay had a detection limit as low as 100 copies/ml of BVDV RNA, a reaction efficiency of 103.2%, a correlation coefficient (R²) of 0.995, and a maximum intra-assay CV of 2.63%. It was 10-fold more sensitive than conventional RT-PCR and can quantitatively detect BVDV RNA levels from 10-fold serial dilutions of titrated viruses containing a titer from 10^-1to 10^-5TCID₅₀, without non-specific amplification. Melting curve analysis showed no primer-dimers and non-specific products.</p> <p>Conclusions</p> <p>The one-step SYBR Green I RT-PCR is specific, sensitive and reproducible for the quantification of BVDV in cell culture. This one-step SYBR Green I RT-PCR strategy may be further optimized as a reliable assay for diagnosing and monitoring BVDV infection in animals. It may also be applied to evaluate candidate agents against HCV using BVDV cell culture model.</p

Development of one-step SYBR Green real-time RT-PCR for quantifying bovine viral diarrhea virus type-1 and its comparison with conventional RT-PCR

Background Bovine viral diarrhea virus (BVDV) is a worldwide pathogen in cattle and acts as a surrogate model for hepatitis C virus (HCV). One-step real-time fluorogenic quantitative reverse transcription polymerase chain reaction (RT-PCR) assay based on SYBR Green I dye has not been established for BVDV detection. This study aims to develop a quantitative one-step RT-PCR assay to detect BVDV type-1 in cell culture. Results One-step quantitative SYBR Green I RT-PCR was developed by amplifying cDNA template from viral RNA and using in vitro transcribed BVDV RNA to establish a standard curve. The assay had a detection limit as low as 100 copies/ml of BVDV RNA, a reaction efficiency of 103.2%, a correlation coefficient (R2) of 0.995, and a maximum intra-assay CV of 2.63%. It was 10-fold more sensitive than conventional RT-PCR and can quantitatively detect BVDV RNA levels from 10-fold serial dilutions of titrated viruses containing a titer from 10-1 to 10-5 TCID50, without non-specific amplification. Melting curve analysis showed no primer-dimers and non-specific products. Conclusions The one-step SYBR Green I RT-PCR is specific, sensitive and reproducible for the quantification of BVDV in cell culture. This one-step SYBR Green I RT-PCR strategy may be further optimized as a reliable assay for diagnosing and monitoring BVDV infection in animals. It may also be applied to evaluate candidate agents against HCV using BVDV cell culture model

Impacts of Drought on Maize and Soybean Production in Northeast China During the Past Five Decades.

Climate change has a distinct impact on agriculture in China, particularly in the northeast, a key agriculture area sensitive to extreme hydroclimate events. Using monthly climate and agriculture data, the influence of drought on maize and soybean yields-two of the main crops in the region-in northeast China since 1961 to 2017 were investigated. The results showed that the temperature in the growing season increased by 1.0 °C from the period 1998-2017 to the period 1961-1980, while the annual precipitation decreased slightly. However, precipitation trends varied throughout the growing season (May-September), increasing slightly in May and June, but decreasing in July, August and September, associated with the weakening of the East Asian summer monsoon. Consequently, the annual and growing season drought frequency increased by 15%, and 25%, respectively, in the period 1998-2017 relative to the period 1961-1980. The highest drought frequency (55%) was observed in September. At the same time, the drought intensity during the growing season increased by 7.8%. The increasing frequency and intensity of drought had negative influences on the two crops. During moderate drought years in the period 1961-2017, 3.2% and 10.4% of the provincial maize and soybean yields were lost, respectively. However, during more severe drought years, losses doubled for soybean (21.8%), but increased more than four-fold for maize (14.0%). Moreover, in comparison to the period 1961-1980, a higher proportion of the yields were lost in the period 1998-2017, particularly for maize, which increased by 15% (increase for soybean was 2.4%). This change largely depends on increasing droughts in August and September, when both crops are in their filling stages. The impact of drought on maize and soybean production was different during different growth stages, where a strong relationship was noted between drought and yield loss of soybean in its filling stage. Given the sensitivity of maize and soybean yields in northeast China to drought, and the observed production trends, climate change will likely have significant negative impacts on productivity in the future

Screening for Climate Change Adaptation: Managing the Potential Impacts of Climate Change on Water Sector in China

The issue on screening for climate change adaptation is addressed. A screening approach is developed for assessing climatechange impacts on water sector and integrating adaptation for water resource projects, and three phases for screening climate changeadaptation are introduced that include the semi-quantitative & quantitative analysis, and the evaluation of different adaptation optionson the water resources affected by climate change in China. According to different climatic regions facing different problems on waterresource, four representative regions in China are chosen in the project; after setting up different objectives, this paper demonstrates thecomprehensive research on climate change adaptation, and proposes new ideas, framework and methodologies on screening for climatechange impacts and adaptation. This research provides the effective framework and methodology for the planning and risk managementof the impacts of future climate change on water resource