Miracle-dz/UniFAR-FTUs huggingface dataset:
QRS complex recognition based on adaptive wavelet threshold and Hilbert transform for continuous ECGs

The wearable continuous ECG monitoring and cardiovascular disease detection system has the characteristics of strong noises and big continuous data. It puts forward higher requirements on the accuracy and efficiency of the QRS recognition algorithm. The currently used QRS detection algorithms still have the problem of missed detection and false detection for continuous ECG data. Therefore, a fast R-peak recognition method based on self-adaptive wavelet threshold and Hilbert transform is proposed for processing noisy continuous ECGs. First, wavelet self-adaptive threshold filter is used to denoise the dynamic ECGs, and then the first-order difference, Shannon energy envelope extraction, Hilbert transform and self-adaptive threshold back-check technology are employed for R-peak detection. Using the proposed method, experimental results show that the accuracy, sensitivity, and specificity of the MIT-BIH arrhythmia database are 99.79%, 99.92%, and 99.87%, respectively. Using the PhysioNet/CinC Challenge 2014 dynamic ECG database, the accuracy, sensitivity and specificity are 96.89%, 97.92% and 98.92%, respectively. Furthermore, the realtime ECGs collecting from the portable ECG detector mECG-101 are also used to evaluate the method. The experimental results show that the accuracy, sensitivity and specificity reach to 97.75%, 98.25% and 99.47%, respectively. Compared with Pan-Tompkins algorithm and wavelet transform algorithm, the proposed method has higher detection accuracy and generalization ability, especially for the wide and low-amplitude QRS complexes.
Initial Results of CT Volume Metal Artifacts Reduction based on 3D U-net

Computed tomography (CT) allows rapid acquisition of information about the internal three-dimensional (3D) structure of the scanned object. However, when the scanned object contains metal implants, it will result in metal artifacts in the reconstructed images. Metal artifacts seriously interfere with the analysis and judgment of CT images. To solve the problem of metal artifacts in CT volumes, this paper introduces 3D U-net into the metal artifact reduction of CT volume data. The 3D convolution in 3D U-net can make full use of the spatial and structural information of CT volumes, and can better maintain the internal structural information based on effectively reducing the metal artifacts. The direct processing for 3D data can improve the stability and continuity of the corrected image in all dimensions. After artifact reduction for 3D data, the information of arbitrary spatial coordinate points in the image can be obtained more accurately and effectively. To verify the performance of the algorithm, experiments were conducted using medical datasets. The results show that the method in this paper can effectively suppress metal artifacts while obtaining better stability and continuity in all dimensions.
Virtual Reality Learning Environment with Haptic Gloves

Several new types of sense gloves have been developed in recent years. These can generate a dynamically changing haptic force for each individual finger and can be effectively integrated with virtual environments that contain new applications. This research project aims to develop new innovative virtual reality learning environments (VRLEs) based on this novel technology, with a focus on the gripping and manipulation of virtual objects. A virtual laboratory was set up containing sense gloves and virtual input and output devices. The aim was to establish a virtual training tool for automotive and mechanical engineering students, as well as service technicians. The sense gloves were integrated into Unity and SteamVR for the interaction and motion tracking of virtual objects. To ensure real time interaction when capturing and manipulating virtual objects, effective communication was required between the designed objects and layers making up the sense glove. Access to the virtual glove from the Unity libraries was the preferred option to generate the required events while the virtual objects were in contact with the sense gloves.
Predicting Center of Pressure Velocity Based on Regional Plantar Force in Elderly Men Using Artificial Neural Networks

Abstract: This study aimed to develop an artificial neural network (ANN) model to predict the forward velocity of the center of pressure (COP) in reference to regional plantar force in older adults and verify its validity. Plantar pressure variables in eight artificially divided anatomical areas were recorded barefoot with a Footscan plantar pressure plate system in sixteen community-dwelling males over sixty-five. The ANN was employed to build a predictive model for the velocity of COP based on measured regional plantar force information. The validation test showed that the determination coefficient (R2) for the forward velocity of COP were 0.65 for hindfoot, 0.66 for midfoot, and 0.38 for the forefoot. These results add additional insights into the basis for building an ANN model for gait analysis and pathologic evaluation in older adults. Relevant information may be necessary for clinical applications, such as further elucidating the causes of common age-related foot diseases.
MeNDA: a near-memory multi-way merge solution for sparse transposition and dataflows

Near-memory processing has been extensively studied to optimize memory intensive workloads. However, none of the proposed designs address sparse matrix transposition, an important building block in sparse linear algebra applications. Prior work shows that sparse matrix transposition does not scale as well as other sparse primitives such as sparse matrix vector multiplication (SpMV) and hence has become a growing bottleneck in common applications. Sparse matrix transposition is highly memory intensive but low in computational intensity, making it a promising candidate for near-memory processing. In this work, we propose MeNDA, a scalable near-DRAM multi-way merge accelerator that eliminates the off-chip memory interface bottleneck and exposes the high internal memory bandwidth to improve performance and reduce energy consumption for sparse matrix transposition. MeNDA adopts a merge sort based algorithm, exploiting spatial locality, and proposes a near-memory processing unit (PU) featuring a high-performance hardware merge tree. Because of the wide application of merge sort in sparse linear algebra, MeNDA is an extensible solution that can be easily adapted to support other sparse primitives such as SpMV. Techniques including seamless back-to-back merge sort, stall reducing prefetching and request coalescing are further explored to take full advantage of the increased system memory bandwidth. Compared to two state-of-the-art implementations of sparse matrix transposition on a CPU and a sparse library on a GPU, MeNDA is able to achieve a speedup of 19.1X, 12.0X, and 7.7x, respectively. MeNDA also shows an efficiency gain of 3.8x over a recent SpMV accelerator integrated with HBM. Incurring a power consumption of only 78.6 mW, a MeNDA PU can be easily accommodated by commodity DIMMs.
URAMDS: Utterances Relation Aware Model for Dialogue Summarization: A Combined Model for Dialogue Summarization

Dialogue summarization, which aims to make summarization on the given dialogue automatically, is a challenging task in natural language processing. Compared to text summarization, it needs to consider the interaction between speakers and the colloquialization of expression to generate better summarization. Many existing methods treat dialogue as a plain sequence of text and simply ignore the structural information, which could be important for summarization generation of the dialogue. In this paper, to alleviate the above challenges, we propose a deep-learning-based method that combines a serialized model and a graph model. More specifically, we utilize a Sequence to Sequence (Seq2Seq) as the backbone to cope with the informal text and a Graph Neural Network (GNN) to take advantage of the structural information. The two models are combined by sharing the key information with each other. Besides, special attention is drawn to the specific speaker in our proposed method. Extensive experiments have shown the effectiveness of our proposed method.
BlockLens: Visual Analytics of Student Coding Behaviors in Block-Based Programming Environments

Block-based programming environments have been widely used to introduce K-12 students to coding. To guide students effectively, instructors and platform owners often need to understand behaviors like how students solve certain questions or where they get stuck and why. However, it is challenging for them to effectively analyze students' coding data. To this end, we propose BlockLens, a novel visual analytics system to assist instructors and platform owners in analyzing students' block-based coding behaviors, mistakes, and problem-solving patterns. BlockLens enables the grouping of students by question progress and performance, identification of common problem-solving strategies and pitfalls, and presentation of insights at multiple granularity levels, from a high-level overview of all students to a detailed analysis of one student's behavior and performance. A usage scenario using real-world data demonstrates the usefulness of BlockLens in facilitating the analysis of K-12 students' programming behaviors.
Integrated Lean BPM model to increase customer loyalty in a last-mile courier

With the accelerated growth of online shopping, especially as a result of the Covid 19 pandemic, concern about offering a good quality delivery service has also grown. This has increased competition in this sector from the emergence of new proposals such as Uber or Rappi, which is why Courier companies are having problems maintaining the loyalty of their customers. It is important then, to differentiate yourself and a good way to do it is by improving the management of non-conforming orders, since several studies indicate that if you sell online, you must be prepared to receive an average return´s ratio of 20% of the products depending on the sector. A good way to improve this process is with the application of the lean philosophy to eliminate activities that do not generate value, in conjunction with the BPM methodology to have a better global control of the process, identify repetitive activities and determine the relevant KPIs for the company. That is why this paper proposes a model with the application of these 2 methodologies to improve customer loyalty in a courier company. The implementation of the proposed model and its improvements were simulated using the software Arena and compared with the initial situation. The results showed the effectiveness of the method, reducing the service time of non-compliant orders by 37%. In addition, an increase in customer loyalty is expected taking it up to 60.81%.
Identifying the scope of the implications of a Digital Transformation: A formal approach to define the business dimensions involved

Companies around the world are finding themselves in a race against the relentless evolution of digital technologies that are accelerating innovation and creating a highly competitive environment. Many works in research and practice are trying to guide companies to a Digital Transformation (DT) that allows them to take full advantage of new technologies. However, generic solutions, that are mainly focused on technological aspects, make it clear that there is a lack of understanding of the whole scope of their implications. In this sense, this work takes a step back to analyze the origin of the shortcomings of current solution approaches. The results point out a lack of theoretical foundation on identifying the business dimensions implicated in a DT that define its scope. In this sense, this study contributes with a more comprehensive view of the DT process by using a formal approach to define the business dimensions involved in such a change based on the principles of the Socio-Technical Systems (STS) theory. As a result, this proposal goes beyond the purely technological views to (1) identify five business dimensions involved in the DT process: technology, processes, structure, competencies and culture and (2) recognize the key role of strategy and performance measurement, not as dimensions but as external elements that drive and control the DT process. A multiple case study of the DT process of three French manufacturers is presented to validate the proposition. General remarks and future research concerning the implementation of these dimensions conclude this study.
AMOS: enabling automatic mapping for tensor computations on spatial accelerators with hardware abstraction

Hardware specialization is a promising trend to sustain performance growth. Spatial hardware accelerators that employ specialized and hierarchical computation and memory resources have recently shown high performance gains for tensor applications such as deep learning, scientific computing, and data mining. To harness the power of these hardware accelerators, programmers have to use specialized instructions with certain hardware constraints. However, these hardware accelerators and instructions are quite new and there is a lack of understanding of the hardware abstraction, performance optimization space, and automatic methodologies to explore the space. Existing compilers use hand-tuned computation implementations and optimization templates, resulting in sub-optimal performance and heavy development costs. In this paper, we propose AMOS, which is an automatic compilation framework for spatial hardware accelerators. Central to this framework is the hardware abstraction that not only clearly specifies the behavior of spatial hardware instructions, but also formally defines the mapping problem from software to hardware. Based on the abstraction, we develop algorithms and performance models to explore various mappings automatically. Finally, we build a compilation framework that uses the hardware abstraction as compiler intermediate representation (IR), explores both compute mappings and memory mappings, and generates high-performance code for different hardware backends. Our experiments show that AMOS achieves more than 2.50× speedup to hand-optimized libraries on Tensor Core, 1.37× speedup to TVM on vector units of Intel CPU for AVX-512, and up to 25.04× speedup to AutoTVM on dot units of Mali GPU. The source code of AMOS is publicly available.
AI accelerator on IBM Telum processor: industrial product

IBM Telum is the next generation processor chip for IBM Z and LinuxONE systems. The Telum design is focused on enterprise class workloads and it achieves over 40% per socket performance growth compared to IBM z15. The IBM Telum is the first server-class chip with a dedicated on-chip AI accelerator that enables clients to gain real time insights from their data as it is getting processed. Seamlessly infusing AI in all enterprise workloads is highly desirable to get real business insight on every transaction as well as to improve IT operation, security, and data privacy. While it would undeniably provide significant additional value, its application in practice is often accompanied by hurdles from low throughput if run on-platform to security concerns and inconsistent latency if run off-platform. The IBM Telum chip introduces an on-chip AI accelerator that provides consistent low latency and high throughput (over 200 TFLOPS in 32 chip system) inference capacity usable by all threads. The accelerator is memory coherent and directly connected to the fabric like any other general-purpose core to support low latency inference while meeting the system's transaction rate. A scalable architecture providing transparent access to AI accelerator functions via a non-privileged general-purpose core instruction further reduces software orchestration and library complexity as well as provides extensibility to the AI functions. On a global bank customer credit card fraud detection model, the AI accelerator achieves 22× speed up in latency compared to a general purpose core utilizing vector execution units. For the same model, the AI accelerator achieves 116k inferences every second with a latency of only 1.1 msec. As the system is scaled up from one chip to 32 chips, it performs more than 3.5 Million inferences/sec and the latency still stays very low at only 1.2 msec. This paper briefly introduces the IBM Telum chip and later describes the integrated AI accelerator. IBM Telum's AI accelerator architecture, microarchitecture, integration into the system stack, performance, and power are covered in detail.
Transforming Growth Factor a and 0 Expression in Human Colon Cancer Lines: Implications for an Autocrine Model1

Three human colon cancer lines (SW480, SW620, WIDR) secrete different levels of transforming growth factor ÃŸ(TGF/9)-like and trans forming growth factor a (TGFa)/epidermal growth factor (KGI->like molecules into serum-free conditioned media as measured by competing activity in TGF/3and EGF radioreceptor assays. SW480 cells, the highest producers of TGF/8-like activity, lack detectable TGF0 receptors while SW620 cells, the highest producers of TGFa/EGF-like activity, lack EGF receptors. This study investigated the production of these growth factors at the itiHNA level and examined the mechanism of loss of detectable receptors. Using complementary 1)V\ probes for TGF/8 and TGFa, it was demonstrated that mRNA levels correlated with the amounts of TGF/3 and TGFa produced; TGF/3 gene expression was highest in SU 4811cells and TGFa gene expression was highest in SW620 cells. Acid washing of the SW480 cells prior to performing the TGF/3 binding assay resulted in the unmasking of substantial levels of TGF/8 receptors. Neither acid washing nor preincubation with suramin uncov ered EGF receptors in SW620 cells. Also, and in contrast to the other two lines, EGF receptor expression could not be detected in SU 620 cells by Northern gel analysis of receptor messenger RNA or by immunological analysis of receptor protein. Thus two distinct mechanisms (occupation of TGF/3 receptor in SU 480 cells, or absence of EGF receptor in SW620 cells) explain the lack of detectable TGF/3 and EGF receptors in the binding assays. The autocrine hypothesis remains viable for TGF/8 in SU 480 cells but not for TGFa in SW620 cells; this would not discount a paracrine role in this latter case.
Research on Ultrasonic Image Segmentation of Thyroid Nodules Based on Improved U-net++

The ultrasound image of thyroid nodules has low contrast and severe speckle noise, and the morphology of thyroid nodules in different patients is quite different, which makes it extremely difficult for doctors to accurately and quickly diagnose the nodules. In order to accurately segment the thyroid nodules from the ultrasound image, the paper improves the U-Net++ network. Based on the U-Net++ model, EfficientDet is used as the encoder, and the CSSE block is merged in the encoder and decoder to improve performance. In addition, the paper improves the network structure and reduces the number of model parameters. After testing 720 ultrasound images of thyroid nodules, the improved U-Net++ image segmentation has an average Dice coefficient of 0.9213, an average accuracy of 0.9262, an average recall rate of 0.9011, and an average F1 score of 0.9202. The Dice coefficient of the improved algorithm segmentation is 9.01% higher than that of U-Net++. The improved algorithm is of great significance for the application of automatic segmentation of ultrasound images of thyroid nodules in actual clinical medicine.
Research on driving mechanism of total energy consumption in Jiangsu province based on the extended STIRPAT model

Grasping the driving factors of energy consumption is an important research topic for effective energy saving and consumption reduction. Based on the classic IPAT model, the extended STIRPAT model is used to analyze the main driving factors of Jiangsu's energy consumption from 2005 to 2020, and quantitatively study the mechanism and influence mechanism of key driving factors on Jiangsu's energy consumption. The research conclusions are as follows: Firstly, the population size, urbanization, and foreign trade are the main factors that promote the growth of energy consumption, and the industrial structure and technological progress are the main factors that inhibit the growth of energy consumption. Secondly, the population size effect reflects Jiangsu's urban function of absorbing employment and agglomerating population on the one hand, and on the other hand illustrates the energy demand caused by population growth and the accompanying urbanization process; While import and export trade is driving economic growth, its energy-consuming import processing and export-oriented trade model has become an important contributor to Jiangsu's energy consumption growth. Finally, industrial structure and energy consumption intensity are the main contributors to curbing the growth of energy consumption during this period. The inhibitory effect of industrial structure on energy growth is stronger than energy consumption intensity. Especially in the adjustment of industrial structure, Jiangsu continues to promote industrial optimization and adjustment, and the proportion of industrial added value continues to decrease, which effectively reduces the growth of energy consumption demand. Energy utilization efficiency is another contributing factor to curb energy consumption growth. Although the energy consumption index of Jiangsu's main industrial products has reached the international advanced level, there is still a big gap between the energy intensity of Jiangsu's unit GDP and the international advanced level.
Quantum phase diagram for homogeneous Bose‐Einstein condensate

We calculate the quantum phase transition for a homogeneous Bose gas in the plane of s‐wave scattering length as and temperature T. This is done by improving a one‐loop result near the interaction‐free Bose‐Einstein critical temperature Tc(0) with the help of recent high‐loop results on the shift of the critical temperature due to a weak atomic repulsion based on variational perturbation theory. The quantum phase diagram shows a nose above Tc(0), so that we predict the existence of a reentrant transition above Tc(0), where an increasing repulsion leads to the formation of a condensate.
Information in asset pricing: a wave function approach

This paper introduces a quantum‐like wave function as an information wave function. We show how the option pricing partial differential equation can be re‐written when we account for such information wave function. We use two stochastic differential equations, one of which relates to Nelson's hypothesis of Universal Brownian motion. We also provide for two examples which further highlight the proposed theory.
Introducing relativity in global navigation satellite systems

Today, the Global Navigation Satellite Systems, used as global positioning systems, are the GPS and the GLONASS. They are based on a Newtonian model and hence they are only operative when several relativistic effects are taken into account. The most important relativistic effects (to order 1/c2) are: the Einstein gravitational blue shift effect of the satellite clock frequency (Equivalence Principle of General Relativity) and the Doppler red shift of second order, due to the motion of the satellite (Special Relativity). On the other hand, in a few years the Galileo system will be built, copying the GPS system unless an alternative project is designed. In this work, it will be also shown that the SYPOR project, using fully relativistic concepts, is an alternative to a mere copy of the GPS system. According to this project, the Galileo system would be exact and there would be no need for relativistic corrections.