129 research outputs found
Microstructure-Empowered Stock Factor Extraction and Utilization
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
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
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
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
Multimode Jahn-Teller effect in bulk systems: A case of the NV0 center in diamond
The multimode Jahn-Teller (JT) effect in a bulk system of a neutral nitrogen-vacancy ( N V 0 ) center in diamond is investigated via first-principles density-functional-theory calculations and the intrinsic distortion path (IDP) method. The adiabatic potential energy surface of the electronic ground state of the N V 0 center is calculated based on the local spin-density approximation. Our calculations confirm the presence of the dynamic Jahn-Teller effect in the ground 2 E state of the N V 0 center. Within the harmonic approximation, the IDP method provides the reactive path of JT distortion from unstable high-symmetry geometry to stable low-symmetry energy minimum geometry, and it describes the active normal modes participating in the distortion. We find that there is more than one vibrational mode contributing to the distortion, and their contributions change along the IDP. Several vibrational modes with large contributions to JT distortion, especially those modes close to 44 meV, are clearly observed as the phonon sideband in photoluminescence spectra in a series of experiments, indicating that the dynamic Jahn-Teller effect plays an important role in the optical transition of the N V 0 center
SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks
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
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 and
for fault-tolerant and non-fault-tolerant GEMMs, outperforming cuBLAS by up to
.Comment: 11 pages, 2023 International Conference on Supercomputin
High-precision, non-invasive anti-microvascular approach via concurrent ultrasound and laser irradiation
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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