Miracle-dz/UniFAR-FTUs huggingface dataset:
A Research on Technology Innovation and Efficiency Improvement Correlation with Bank Performance Based on Data Envelopment Analysis

Technology evolution plays an important role in current commercial banking industry. Identification on the impact of technology innovation and efficiency improvement is essential for commercial bank operating performance. For his purpose, based on the measurement on technology innovation and efficiency improvement indexes of Chinese commercial banks with the method of non-parametric Malmquist Data Envelopment Analysis, fixed effect panel data models are constructed to empirically analyze the relevance between technology innovation, efficiency improvement and bank performance with 41 Chinese commercial banks. Correlation analysis results show that technology innovation can promote the Chinese commercial bank performance significantly, but the positive correlation impact effect varies from different types of commercial banks; the improvement of scale efficiency is positively correlated with the performance of both the giant State-Owned banks and the regional commercial banks; while the improvement of pure technical efficiency shows no significant performance influence over all types of commercial banks in China.
DSDP: Dual Stream Data Prefetcher

Hardware prefetching is an important DRAM latency hiding technology. Designing prefetchers to maximize system performance often requires a delicate balance between coverage and accuracy. As the number of cores increases, the accuracy of the prefetching algorithm becomes more important. Separating streams based on memory access instructions is an effective way to improve accuracy. However, this technique may lose prefetch opportunities by losing cross-PC relationships, and even reduce algorithm coverage. In this paper, we propose a spatial prefetcher called Dual Stream Data Prefetcher (DSDP). DSDP achieves high coverage and accuracy with two designs. First, DSDP improves prefetching accuracy by PC localizing. Second, DSDP simultaneously learns cross-PC relationships from the spatial localized stream to compensate for the loss of prefetching opportunities brought by PC localization. Our experimental results show that, DSDP improves system performance by 41.9% over a baseline with no data prefetcher and 4.1% over the state-of-the-art spatial data prefetcher.
Research on Nonlinear Relationship Between Fiscal Expenditure Competition and Upgrading of Industrial Structure Based on Threshold Modeling Method

Purposes: To explore the nonlinear effect of fiscal expenditure competition on the upgrading of industrial structure. Methods: Using Threshold Modeling Method and establishes a threshold panel model based on the China's provincial panel data from 2007 to 2019 to analyzes the nonlinear relationship between fiscal expenditure competition and upgrading of industrial structure. Results: There is a nonlinear threshold effect between fiscal expenditure competition and upgrading of industrial structure. The effect of fiscal expenditure competition on upgrading of industrial structure is heterogeneous in regions with different levels of economic development. Conclusions: Many variables have structural mutation problems, that is, there is a nonlinear relationship between these variables, so the threshold modeling method should be considered to explore the nonlinear relationship between variables.
Analyzing Test Smells Refactoring from a Developers Perspective

Test smells represent a set of poorly designed tests, which can harm a test code’s maintenance and quality criteria. Although fundamental steps to understand test smells have been investigated, there is still an evident lack of studies evaluating the impact of test smell refactoring from internal quality attributes, such as size, cohesion, coupling, and complexity. In addition, the literature still lacks research that addresses the difficulties developers encounter during test smell refactoring. The paper investigates the impact of test smell refactoring from developers’ perspectives and internal quality attributes. We investigated the perceptions and difficulties encountered by 20 developers while removing 5 types of test smells in 4 open-source projects over two months. Through this experiment, we analyzed: (i) the impact that test smell refactoring has on internal quality attributes; (ii) developers’ perception of test smells as actual problems within a software system; and (iii) main difficulties encountered by developers during test smell refactoring. Our findings can help developers design a prioritization scheme for test smell refactoring and make them aware of the real benefits of test smell refactoring.
Machine-Learning Based Delay Prediction for FPGA Technology Mapping

Accurate delay prediction is important in the early stages of logic and high-level synthesis. In technology mapping for field programmable gate array (FPGA), a gate-level circuit is transcribed into a lookup table (LUT)-level circuit. Quick timing analysis is necessary on a pre-mapped circuit to guide optimizations downstream. However, a static timing analyzer is too slow due to its complexity and highly inaccurate like other faster empirical heuristics before technology mapping. In this work, we present a machine learning based framework for accurately and efficiently estimating the delay of a gate-level circuit from predicting the depth of the corresponding LUT logic after technology mapping. Our experimental results show that the proposed method achieves a 56x accuracy improvement compared to the existing delay estimation heuristic. Instead of running the mapper for the ground truth, our delay estimator saves 87.5% on runtime with negligible error.
Breaking the Vendor Lock: Performance Portable Programming through OpenMP as Target Independent Runtime Layer

High performance computing (HPC) systems pervasively feature GPU accelerators. For maximum efficiency, these are usually programmed using vendor-specific languages, such as CUDA. However, this is not portable and leads to vendor lock-in. Existing portable proramming models require transcribing the whole application, which is tedious and often results in sub-optimal performance without necessarily avoiding the need to maintain multiple versions. Although solutions for automated translation exist, they sacrifice either features of the original model, performance, or both. We propose a novel compiler-based approach for performance portable programming of GPUs by generating portable code from the original, vendor-specific application source. Specifically, we present LLVM/Clang extensions for performance portable CUDA by leveraging the existing LLVM/OpenMP offloading infrastructure for portable execution on different GPU architectures. Our contributions include: re-designing the compiler driver for portable toolchain generation, defining a target independent math library, and re-architecting compiler lowering from CUDA APIs to existing and new OpenMP runtime calls. We evaluate our approach using six established CUDA proxy and benchmark applications first on NVIDIA GPUs, to measure the overhead of our portability layer, then secondly on AMD GPUs, to determine the efficacy of our approach. In both experiments we compare the performance to native program versions, i.e., CUDA and HIP. Our approach has minimal overhead compared to non-portable alternatives, thus providing viable performance portability for existing code without cost to the user. We further show CUDA code debugged directly on the host.
Phylogenetic Analysis of the Histone-like Protein (pA104R) Reveals High Conservation among African Swine Fever Virus (ASFV) Variants

The African Swine Fever Virus (ASFV) is a highly lethal virus that causes the death of pigs a week after its symptoms begin to manifest; hence, a mortality rate reaching up to 100%. Because of the crucial role of pork as a food preference worldwide, significant economic losses due to ASF outbreaks have been experienced. Despite the severity of this disease, there is still no known treatment that can successfully cure ASF. Recently, the role of the histone-like protein pA104R in the viral replication of ASFV has been unraveled and is gathering the attention of many researchers as a potential target to inhibit ASFV infectivity. However, little is known about the relationship of this protein with its homologs across variants of ASF. Therefore, in this study, we characterized their relationship and highlight conserved and variable regions that allow for the design of effective inhibitors. We acquired the nucleotide sequences of all pA104R homologs through the tBLASTn program. These sequences were subjected to multiple sequence alignment (MSA), and the evolutionary behavior of the sequences was then mapped out. The resulting tree was produced from 91 sequences taken from the NCBI database and contained five distinct clades. The phylogenetic analysis revealed that variants from clades “C” and “E” were highly variable, reflecting higher frequencies of gene mutation compared to the other clades. The alignment and midpoint-rooted phylogenetic tree showed the high conservation of pA104R across variants of ASFV. The variable residues were also determined. From these results, we conclude that drugs and drug-like compounds that can block the protein-DNA binding sites can be administered to afflicted pigs all over the world due to the high conservation of the protein across virulent strains. Thus, pA104R has high potential as a target protein for the inhibition of ASFV replication and spread.
Squaring the circle: Executing Sparse Matrix Computations on FlexTPU---A TPU-Like Processor

Systolic arrays have been successful to accelerate dense linear algebra for deep neural networks (DNNs), but cannot handle sparse computations efficiently. Though early attempts have been made to perform sparse matrix operations on weight-pruned DNNs, handling highly sparse matrices with skewed nonzero distribution commonly seen in real-world graph analytics remains challenging. In this paper, we propose FlexTPU framework to repurpose tensor processing units (TPUs) to execute sparse matrix-vector operations (SpMV). First, we propose a lightweight Z-shape mapping of sparse matrices onto the systolic array to eliminate the processing of zeros as much as possible, regardless of the sparsity and nonzero distribution. On top of the mapping, we devise an SpMV dataflow executed by an array of PEs, which are a slightly modified version of the conventional TPU PE. Second, in contrast to the excess preprocessing mandatory for prior attempts, the Z-shape mapping facilitates on-the-fly matrix condensing from the widely-used compressed sparse matrix (e.g. CSR) representation. This is accomplished by a proposed sparse data loader that includes an on-chip row decoder and parallel nonzero loaders. We evaluate FlexTPU on a broad set of synthetic and real-world sparse matrices. The experimental result shows that FlexTPU achieves 3.55× speedup and 3.27× energy saving over a state-of-the-art design, Sparse-TPU. It performs even better on sparse matrices with power-law distributions. Compared to state-of-the-art library implementations on a CPU and a GPU, FlexTPU also achieves an average speedup of 2.4× and 4.3×, and energy saving of 130.4× and 495.3×, respectively. FlexTPU is also evaluated against a recent re configurable (chip multi-processor) CMP machine, Transmuter. FlexTPU outperforms Transmuter by achieving 5.12× speedup and 2.65× energy saving.
Research on Satisfaction Evaluation of Digital Reading Service in University Libraries Based on ACSI Model

Artificial intelligence, cloud computing, big data and other technologies have been gradually developed and applied to the digital reading service of university libraries. The service basis, content and methods have undergone great changes, and the inter-library connection has been strengthened. In order to explore the satisfaction of college students with digital reading services under the new situation, this paper introduces the structural variable "digitalization level" based on the American Customer Satisfaction Index (ACSI) model. This paper introduces the structural variable of "digitalization level", constructs a digital reading service satisfaction model of university library and conducts an empirical analysis on the service satisfaction, and identifies the key factors affecting and improving the digital reading user satisfaction. The research results show that the perceived quality of digital reading services in university libraries has a significant positive impact on user satisfaction, user satisfaction has a significant positive impact on user loyalty, and users' evaluation of library digitization level has a significant positive impact on perceived quality, which in turn affects user satisfaction. On this basis, this paper puts forward conclusions and suggestions for the high-quality development and satisfaction improvement of digital reading services in university libraries: in the library, it is necessary to strengthen the promotion of services, simplify and reorganize the service function area, and improve the efficiency of feedback services; It is necessary to create community-based services and break down barriers to sharing between libraries.
Parallelizing Neural Network Models Effectively on GPU by Implementing Reductions Atomically

Due to the missing of a good orchestration of loop transformations, existing optimizing compilers for deploying neural networks on GPU either parallelize reductions ineffectively or miss the fusion opportunities with other operators. Neural network models thus exhibit sub-optimal performance on GPU. We present a practical approach called Panamera for the effective parallelization of reductions in neural networks on GPU. Panamera first leverages loop coalescing to flatten the loop dimensions of reductions, converting all reduction operators into canonical forms eligible for the polyhedral model. Next, Panamera uses polyhedral transformations to reduce the data movements caused by unfused reductions and perform multi-block hardware binding not considered by many compilers. Finally, Panamera embeds a highly optimized routine implemented using GPU atomic instructions, further improving the performance of neural network models while guaranteeing the correctness of parallel reductions. The experimental results demonstrate the effectiveness of our approach: for single operators our code obtains a mean speedup of 33.7×, 3.5×, 5.4× and 9.6× over cuDNN, CUB, TVM and Ansor, for sub-graphs our approach outperforms cuDNN, TVM and Ansor by 9.5×, 2.6× and 2.7×, and for end-to-end workloads, a tensor compiler integrated with our approach outperforms them by 122.5%, 19.3% and 15.2%.
Tool recommendation for workflow composition using frequent patterns

Workflows or pipelines provide a means for executing complex data analysis seamlessly. Composing tools into a workflow is essential in bioinformatics experiments. There are scientific workflow systems such as Taverna and Galaxy that facilitate automatic workflow composition. However, designing workflows using workflow systems becomes more complex with the availability of vast numbers of complex, heterogeneous tools. Connecting such heterogeneous tools to a workflow is error-prone and time-consuming. The objective of the study is to develop a suggestive system for interactive workflow composition using frequent patterns in workflows. The approach basically consists of three main phases: pattern mining, component suggestion, and updating the workflow. Frequent patterns of workflows are identified using frequent subgraph mining techniques and N-gram modeling. The suggested components allow reusing best practice workflows while reducing the time required in composing the workflows. Frequent usage patterns identified can also be used in searching similar workflows in workflow repositories. An interactive workflow composition approach is useful for novice as well as experienced scientists in composing workflows with state-of-the-art tools. The approach enhances the reuse of best practice workflows developed by other users. Such systems would succeed more in future with the availability of more and more workflows in the light of open science
Comparison and validation of pressure and acceleration time-domain waveform models of a smart insole for accurate step count in healthy people

Several studies have shown good accuracies for step count based on pressure signals of smart insoles in people walking at different speeds. Although smart insoles are often equipped with pressure sensors and accelerometer, no study has focused on comparing the accuracy of step count separately based on pressure and acceleration signals in healthy people. The objectives of this study were to design a waveform model of accelerometer and pressure sensors, and then compare with commercially well-known step count devices and validate these models using manual step counter for step count. Eight healthy participants (age: 39.8±17.56 years old) wore a pair of smart insoles, a GaitUp, and a StepWatchTM and performed the six-minute walking test at walking speeds from 1.62 to 2.22 m/s. Four pressure and one acceleration waveform models were designed and used for the detection of 341 to 412 steps. Accuracies ranged from 99.80%±0.60% to 99.97%±1.38% for right side, and from 99.67%±0.63% to 99.90%±0.05% for left side with pressure waveform models. In addition, the acceleration waveform model provided accuracies of 99.87%±2.49% and 99.84%±4.77% for right and left sides respectively. Step count accuracies using the GaitUp were 99.51%±2.06% for right side, and 99.51%±4.32% for left side. Finally, the StepWatchTM yielded step count accuracies of 99.31%±15.95% and 98.52%±28.06% for right and left sides respectively. These results suggested the smart insole with pressure and acceleration waveform models as more accurate than the StepWatchTM and the GaitUp for step count.
Prevalence of reduced lung diffusing capacity and CT scan findings in smokers without airflow limitation: a population-based study

Background Population distribution of reduced diffusing capacity of the lungs for carbon monoxide (DLCO) in smokers and main consequences are not properly recognised. The objectives of this study were to describe the prevalence of reduced DLCO in a population-based sample of current and former smoker subjects without airflow limitation and to describe its morphological, functional and clinical implications. Methods A sample of 405 subjects aged 40 years or older with postbronchodilator forced expiratory volume in 1 s/forced vital capacity (FVC) >0.70 was obtained from a random population-based sample of 9092 subjects evaluated in the EPISCAN II study. Baseline evaluation included clinical questionnaires, exhaled carbon monoxide (CO) measurement, spirometry, DLCO determination, 6 min walk test, routine blood analysis and low-dose CT scan with evaluation of lung density and airway wall thickness. Results In never, former and current smokers, prevalence of reduced DLCO was 6.7%, 14.4% and 26.7%, respectively. Current and former smokers with reduced DLCO without airflow limitation were younger than the subjects with normal DLCO, and they had greater levels of dyspnoea and exhaled CO, greater pulmonary artery diameter and lower spirometric parameters, 6 min walk distance, daily physical activity and plasma albumin levels (all p<0.05), with no significant differences in other chronic respiratory symptoms or CT findings. FVC and exhaled CO were identified as independent risk factors for low DLCO. Conclusion Reduced DLCO is a frequent disorder among smokers without airflow limitation, associated with decreased exercise capacity and with CT findings suggesting that it may be a marker of smoking-induced early vascular damage. Trial registration number NCT03028207.
New building signature extraction method from single very high-resolution synthetic aperture radar images based on symmetric analysis

Abstract. To monitor urban areas using a synthetic aperture radar (SAR) sensor, we propose a symmetric analysis-based building signature extraction method. Instead of using separated algorithms, a unified framework is proposed to extract both layover and shadow areas. Since these two primitives usually exhibit long strip patterns in very-high-resolution SAR images, symmetry axes are first delineated. After that, local features are extracted from both symmetry and range direction to better distinguish different primitives. Then, these local radiometric features are used to identify different categories (layover, shadow, and background) via an efficient multiclass logistic regression classifier. To discriminate individual primitives, geometric information is adopted via an improved Ramer Douglas Peucker algorithm, which also simplifies the parameters for describing these primitives. To further enhance accuracy, combinatory analysis is implemented to exclude some false detections, and then shadow areas are extended via a local region growing method. The proposed approach is tested on a 0.75-m resolution airborne C band SAR image. The experiments are carried out under both small- and large-scale scenes, and the comparative results show our method has some advantages in low-contrast target detection and false-alarm elimination.
Assessment of fire blight introduction in the wild apple forests of Kazakhstan

ABSTRACT Fire blight disease of apples continues its worldwide spread, having reached Kazakhstan in 2010. It is a great threat to the wild apple forests of Malus sieversii. The introduction of fire blight is already showing a considerable impact on cultural apple growing and demands radical efforts for conservation of the wild apple forests. A number of studies have been conducted to examine the presence of fire blight distribution within apples in agricultural areas; however, there has been no large-scale monitoring of wild apple tree populations. Here we present the results of three years of monitoring wild apples in three protected areas of Kazakhstan, looking for the presence of fire blight (Erwinia amylovora). A visual inspection showed no signs of fire blight on the trees of M. sieversii in three consecutive years. These findings were confirmed by lateral flow immunochromatography, and conventional and real-time polymerase chain reaction tests of the asymptomatic samples. The findings of this study will be used to produce recommendations for state authorities to prevent fire blight in wild apple forests of Kazakhstan.
Exactly solvable models of sphere interactions in quantum mechanics

The authors discuss exactly solvable Schrodinger Hamiltonians corresponding to a surface delta interaction supported by a sphere and various generalisations thereof. First they treat the pure delta sphere model; self-adjointness of the Hamiltonian, spectral properties, stationary scattering theory, approximation by scaled short-range Hamiltonians. Next they extend the model by adding a point interaction at the centre of the sphere or, alternatively, a Coulomb interaction. Finally the whole analysis is extended to the case of a delta ' sphere interaction, first taken alone, then superimposed on a point interaction or a Coulomb potential.