91 research outputs found

    Microstructure-Empowered Stock Factor Extraction and Utilization

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    High-frequency quantitative investment is a crucial aspect of stock investment. Notably, order flow data plays a critical role as it provides the most detailed level of information among high-frequency trading data, including comprehensive data from the order book and transaction records at the tick level. The order flow data is extremely valuable for market analysis as it equips traders with essential insights for making informed decisions. However, extracting and effectively utilizing order flow data present challenges due to the large volume of data involved and the limitations of traditional factor mining techniques, which are primarily designed for coarser-level stock data. To address these challenges, we propose a novel framework that aims to effectively extract essential factors from order flow data for diverse downstream tasks across different granularities and scenarios. Our method consists of a Context Encoder and an Factor Extractor. The Context Encoder learns an embedding for the current order flow data segment's context by considering both the expected and actual market state. In addition, the Factor Extractor uses unsupervised learning methods to select such important signals that are most distinct from the majority within the given context. The extracted factors are then utilized for downstream tasks. In empirical studies, our proposed framework efficiently handles an entire year of stock order flow data across diverse scenarios, offering a broader range of applications compared to existing tick-level approaches that are limited to only a few days of stock data. We demonstrate that our method extracts superior factors from order flow data, enabling significant improvement for stock trend prediction and order execution tasks at the second and minute level

    Correcting soft errors online in fast fourier transform

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    While many algorithm-based fault tolerance (ABFT) schemes have been proposed to detect soft errors offline in the fast Fourier transform (FFT) after computation finishes, none of the existing ABFT schemes detect soft errors online before the computation finishes. This paper presents an online ABFT scheme for FFT so that soft errors can be detected online and the corrupted computation can be terminated in a much more timely manner. We also extend our scheme to tolerate both arithmetic errors and memory errors, develop strategies to reduce its fault tolerance overhead and improve its numerical stability and fault coverage, and finally incorporate it into the widely used FFTW library - one of the today's fastest FFT software implementations. Experimental results demonstrate that: (1) the proposed online ABFT scheme introduces much lower overhead than the existing offline ABFT schemes; (2) it detects errors in a much more timely manner; and (3) it also has higher numerical stability and better fault coverage

    Dynamic Quality Metric Oriented Error-bounded Lossy Compression for Scientific Datasets

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    With the ever-increasing execution scale of high performance computing (HPC) applications, vast amounts of data are being produced by scientific research every day. Error-bounded lossy compression has been considered a very promising solution to address the big-data issue for scientific applications because it can significantly reduce the data volume with low time cost meanwhile allowing users to control the compression errors with a specified error bound. The existing error-bounded lossy compressors, however, are all developed based on inflexible designs or compression pipelines, which cannot adapt to diverse compression quality requirements/metrics favored by different application users. In this paper, we propose a novel dynamic quality metric oriented error-bounded lossy compression framework, namely QoZ. The detailed contribution is three-fold. (1) We design a novel highly-parameterized multi-level interpolation-based data predictor, which can significantly improve the overall compression quality with the same compressed size. (2) We design the error-bounded lossy compression framework QoZ based on the adaptive predictor, which can auto-tune the critical parameters and optimize the compression result according to user-specified quality metrics during online compression. (3) We evaluate QoZ carefully by comparing its compression quality with multiple state-of-the-arts on various real-world scientific application datasets. Experiments show that, compared with the second-best lossy compressor, QoZ can achieve up to 70% compression ratio improvement under the same error bound, up to 150% compression ratio improvement under the same PSNR, or up to 270% compression ratio improvement under the same SSIM

    Utilization of Polislidae Wasp Venom as Potential New Insect Drugs in the R&D of Wellness Industry

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    The Polislidae wasp, one species of omnivorous social insects mainly living in the bush or under the leaves. The wasp has a venom sac in its tail, and the venom secreted by a sting can cause a series of body reactions and diseases. Multiple organ failure could be the outcome of wasp sting, if timely treatment or rescue has not been performed. Based on published reports on wasp sting related to medical concerns in recent years, this review summarizes the symptoms caused by wasp sting and corresponding mechanisms of actions. The medical application and relational utilization of the title insect is suggested derived from findings of the systematic review. Furthermore, we herewith sketch the perspectives of R&D on the venom of Polislidae wasp. It is expected to afford comprehensive references and be useful for broader study on the natural components and pharmacological effects of wasp venom

    SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks

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    The fast growth of computational power and scales of modern super-computing systems have raised great challenges for the management of exascale scientific data. To maintain the usability of scientific data, error-bound lossy compression is proposed and developed as an essential technique for the size reduction of scientific data with constrained data distortion. Among the diverse datasets generated by various scientific simulations, certain datasets cannot be effectively compressed by existing error-bounded lossy compressors with traditional techniques. The recent success of Artificial Intelligence has inspired several researchers to integrate neural networks into error-bounded lossy compressors. However, those works still suffer from limited compression ratios and/or extremely low efficiencies. To address those issues and improve the compression on the hard-to-compress datasets, in this paper, we propose SRN-SZ, which is a deep learning-based scientific error-bounded lossy compressor leveraging the hierarchical data grid expansion paradigm implemented by super-resolution neural networks. SRN-SZ applies the most advanced super-resolution network HAT for its compression, which is free of time-costing per-data training. In experiments compared with various state-of-the-art compressors, SRN-SZ achieves up to 75% compression ratio improvements under the same error bound and up to 80% compression ratio improvements under the same PSNR than the second-best compressor

    Anatomy of High-Performance GEMM with Online Fault Tolerance on GPUs

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    General Matrix Multiplication (GEMM) is a crucial algorithm for various applications such as machine learning and scientific computing, and an efficient GEMM implementation is essential for the performance of these systems. While researchers often strive for faster performance by using large compute platforms, the increased scale of these systems can raise concerns about hardware and software reliability. In this paper, we present a design for a high-performance GEMM with algorithm-based fault tolerance for use on GPUs. We describe fault-tolerant designs for GEMM at the thread, warp, and threadblock levels, and also provide a baseline GEMM implementation that is competitive with or faster than the state-of-the-art, proprietary cuBLAS GEMM. We present a kernel fusion strategy to overlap and mitigate the memory latency due to fault tolerance with the original GEMM computation. To support a wide range of input matrix shapes and reduce development costs, we present a template-based approach for automatic code generation for both fault-tolerant and non-fault-tolerant GEMM implementations. We evaluate our work on NVIDIA Tesla T4 and A100 server GPUs. Experimental results demonstrate that our baseline GEMM presents comparable or superior performance compared to the closed-source cuBLAS. The fault-tolerant GEMM incurs only a minimal overhead (8.89\% on average) compared to cuBLAS even with hundreds of errors injected per minute. For irregularly shaped inputs, the code generator-generated kernels show remarkable speedups of 160%183.5%160\% \sim 183.5\% and 148.55%165.12%148.55\% \sim 165.12\% for fault-tolerant and non-fault-tolerant GEMMs, outperforming cuBLAS by up to 41.40%41.40\%.Comment: 11 pages, 2023 International Conference on Supercomputin

    High-precision, non-invasive anti-microvascular approach via concurrent ultrasound and laser irradiation

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    Antivascular therapy represents a proven strategy to treat angiogenesis. By applying synchronized ultrasound bursts and nanosecond laser irradiation, we developed a novel, selective, non-invasive, localized antivascular method, termed photo-mediated ultrasound therapy (PUT). PUT takes advantage of the high native optical contrast among biological tissues and can treat microvessels without causing collateral damage to the surrounding tissue. In a chicken yolk sac membrane model, under the same ultrasound parameters (1 MHz at 0.45 MPa and 10 Hz with 10% duty cycle), PUT with 4 mJ/cm2 and 6 mJ/cm2 laser fluence induced 51% (p = 0.001) and 37% (p = 0.018) vessel diameter reductions respectively. With 8 mJ/cm2 laser fluence, PUT would yield vessel disruption (90%, p < 0.01). Selectivity of PUT was demonstrated by utilizing laser wavelengths at 578 nm or 650 nm, where PUT selectively shrank veins or occluded arteries. In a rabbit ear model, PUT induced a 68.5% reduction in blood perfusion after 7 days (p < 0.001) without damaging the surrounding cells. In vitro experiments in human blood suggested that cavitation may play a role in PUT. In conclusion, PUT holds significant promise as a novel non-invasive antivascular method with the capability to precisely target blood vessels.R01AR060350R01CA1867694K12EY022299-4BL2014089
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