Computing Applications Research Articles

Hospital Medical Behaviour Supervision and Operational Efficiency Evaluation Method based based on Big Data Platform

The application of cloud computing and big data core technologies and concepts to medical informatization can improve its flexibility and efficiency, and achieve the overall deployment and intensive management of the system. In the era of big data, the use of information management platform can optimize and standardize clinical diagnosis and treatment process, improve the quality and efficiency of diagnosis and treatment services, and improve the quality and level of scientific research, which meets the requirements of medical reform for fine management of hospitals and precise medical treatment in the era of evidence-based medicine. This paper introduces the development and application of big data analysis in the field of information technology. Through the research on the supervision of hospital medical service behavior, the top-level design is used to standardize the content of medical behavior supervision, and the effectiveness of supervision is discussed to achieve the purpose of reducing clinical paperwork. Based on the needs, the medical service behavior supervision system is proposed and constructed. Strengthen hospital medical behavior supervision through hospital big data analysis and knowledge base system support. This system can provide management with an effective monitoring and management tool, enabling them to promptly identify and solve problems that arise in medical services. By analyzing a large amount of medical data, hospitals can help predict the trend of disease occurrence in advance, thereby taking preventive and control measures in advance, and reducing the occurrence and spread of diseases. Through deep learning and analysis of patient data, personalized treatment plans can be provided for each patient to improve treatment effectiveness.

A Mathematical Framework for Quantum Hamiltonian Simulation and Duality

AbstractAnalogue Hamiltonian simulation is a promising near-term application of quantum computing and has recently been put on a theoretical footing alongside experiencing wide-ranging experimental success. These ideas are closely related to the notion of duality in physics, whereby two superficially different theories are mathematically equivalent in some precise sense. However, existing characterisations of Hamiltonian simulations are not sufficiently general to extend to all dualities in physics. We give a generalised duality definition encompassing dualities transforming a strongly interacting system into a weak one and vice versa. We characterise the dual map on operators and states and prove equivalence ofduality formulated in terms of observables, partition functions and entropies. A building block is a strengthening of earlier results on entropy preserving maps—extensions of Wigner’s celebrated theorem- –to maps that are entropy preserving up to an additive constant. We show such maps decompose as a direct sum of unitary and antiunitary components conjugated by a further unitary, a result that may be of independent mathematical interest.

Coupling Nanowire Quantum Dots to Optical Waveguides by Microsphere-Induced Photonic Nanojet

Silica on silicon is a major optical integration platform, while the emergent class of the integrated laser-written circuits’ platform offers additionally high customizability and flexibility for rapid prototyping. However, the inherent waveguides’ low core/cladding refractive index contrast characteristic, compared to other photonic platforms in silicon or silicon nitride, sets serious limitations for on-chip efficient coupling with single photon emitters, like semiconductor nanowires with quantum dots, limiting the applications in quantum computing. A new light coupling scheme proposed here overcomes this limitation, providing means for light coupling >50%. The scheme is based on the incorporation of an optical microsphere between the nanowire and the waveguide, which is properly optimized and arranged in terms of size, refractive index, and the distance of the microsphere between the nanowire and waveguide. Upon suitable design of the optical arrangement, the photonic nanojet emitted by the illuminated microsphere excites efficiently the guided eigenmodes of the input channel waveguide, thus launching light with high-coupling efficiency. The method is tolerant in displacements, misalignments, and imperfections and is fabricationally feasible by the current state of art techniques. The proposed method enables the on-chip multiple single photon emitters’ integration, thus allowing for the development of highly customizable and scalable quantum photonic-integrated circuits for quantum computing and communications.

Multi-level phase-change behaviors of Ge2Sb2Te5/Sb7Se3 bilayer films and a design rule of multi-level phase-change films

Multi-level phase-change (MLPC) memory can not only further raise the storage density of high-density phase-change memory, but also have significant applications in neuromorphic computing. However, acquisitions of MLPC materials are very challenging. MLPC is only observed in a small number of phase-change materials and can’t be designed determinately because the mechanism of MLPC emergence has been understood incompletely. Here, we investigate phase-change behaviors of six Ge2Sb2Te5(x nm)/Sb7Se3 (60-x nm) bilayer films with x = 5, 10, 15, 20, 35, and 50. Three-level phase-change memory is observed for five bilayer films with x < 50, but not for one with x = 50, revealing the dependence of MLPC memory on the thickness ratio of two constituent layers composing bilayer films. A parallel resistance model is proposed to describe the measured sheet resistance of bilayer films and can be used to explain the thickness-ratio dependence of three-level phase-change memory very well. Subsequently, a design rule for MLPC memory films is given out unambiguously. Based on the parallel resistance model, MLPC memories are simulated for bilayer and trilayer films. Three-level memory indeed appears in bilayer films only when the design rule is met, confirming the validity of our design rule. Four-level memory is predicted in trilayer films as the design rule is obeyed.

Reconfigurable Resistive Switching in VO2/La0.7Sr0.3MnO3/Al2O3 (0001) Memristive Devices for Neuromorphic Computing.

The coexistence of nonvolatile and volatile switching modes in a single memristive device provides flexibility to emulate both neuronal and synaptic functions in the brain. Furthermore, such a device structure may eliminate the need for additional circuit elements such as transistor-based selectors, enabling low-power consumption and high-density device integration in fully memristive spiking neural networks. In this work, we report dual resistive switching (RS) modes in VO2/La0.7Sr0.3MnO3 (LSMO) bilayer memristive devices. Specifically, the nonvolatile RS is driven by the movement of oxygen vacancies (Vo) at the VO2/LSMO interface and requires a higher biasing voltage, whereas the volatile RS is controlled by the metal-insulator transition (MIT) of VO2 under a lower biasing voltage. The simple device structure is electrically driven between the two RS modes and thus can operate as a one selector-one resistor (1S1R) cell, which is a desirable feature in memristive crossbar arrays to avoid the sneak-path current issue. The RS modes are found to be stable and repeatable and can be reconfigured by exploiting the interfacial and phase transition properties, and thus, they hold great promise for applications in memristive neural networks and neuromorphic computing.

A cloud-edge collaborative task scheduling method based on model segmentation

With the continuous development and combined application of cloud computing and artificial intelligence, some new methods have emerged to reduce task execution time for training neural network models in a cloud-edge collaborative environment. The most attractive method is neural network model segmentation. However, many factors affect the segmentation point, such as resource allocation, system energy consumption, load balancing, and network Bandwidth allocation. Some segmentation methods consider the shortest task execution time, which ignores the utilization of resources at the edge and can result in resource waste. Additionally, these factors are difficult to measure, which presents a challenge in calculating the best segmentation point to achieve the goal of maximum resource utilization and minimum task execution time. To solve this problem, this paper proposes a cloud-edge collaborative task scheduling method based on model segmentation (CECMS). This method first analyzes the factors affecting the segmentation point of the model and then obtains accurate factors that affect the segmentation point calculation through the pre-execution method. Furthermore, a multi-objective solution algorithm is improved to calculate the optimal model segmentation point. And tasks are separately offloaded to the edge and cloud based on the optimal model segmentation point. Finally, the experiments are conducted to verify the effectiveness of this method. Finally, the effectiveness of the CECMS method was verified through simulation experiments. Compared with the Dynamic Adaptive DNN Surgery (DADS) method and an adaptive DNN inference acceleration framework algorithm with end–edge–cloud collaborative computing algorithm (ADC), CECMS achieves the same effectiveness as DADS and ADC in optimizing task execution time by comprehensively considering the utilization of edge resources and minimizing task execution time, while also effectively ensuring resource utilization.

Tarazu: An Adaptive End-to-end I/O Load-balancing Framework for Large-scale Parallel File Systems

The imbalanced I/O load on large parallel file systems affects the parallel I/O performance of high-performance computing (HPC) applications. One of the main reasons for I/O imbalances is the lack of a global view of system-wide resource consumption. While approaches to address the problem already exist, the diversity of HPC workloads combined with different file striping patterns prevents widespread adoption of these approaches. In addition, load-balancing techniques should be transparent to client applications. To address these issues, we propose Tarazu , an end-to-end control plane where clients transparently and adaptively write to a set of selected I/O servers to achieve balanced data placement. Our control plane leverages real-time load statistics for global data placement on distributed storage servers, while our design model employs trace-based optimization techniques to minimize latency for I/O load requests between clients and servers and to handle multiple striping patterns in files. We evaluate our proposed system on an experimental cluster for two common use cases: the synthetic I/O benchmark IOR and the scientific application I/O kernel HACC-I/O. We also use a discrete-time simulator with real HPC application traces from emerging workloads running on the Summit supercomputer to validate the effectiveness and scalability of Tarazu in large-scale storage environments. The results show improvements in load balancing and read performance of up to 33% and 43%, respectively, compared to the state-of-the-art.

МАТЕМАТИЧНІ СИСТЕМИ ДЛЯ РЕАЛІЗАЦІЇ ШТУЧНИХ НЕЙРОННИХ МЕРЕЖ, ОРІЄНТОВАНИХ НА ХМАРОВІ ОБЧИСЛЕННЯ

The article provides a detailed overview of research focusing on artificial neural networks (ANNs) and their applications in cloud computing. Research methods of organizational development and changes based on artificial intelligence technologies and intellectual support systems are presented in the plane of: intellectual expert systems; inductive systems; semantic networks, neural networks, genetic algorithms. The aim of the study. The research is aimed at the study and analysis of modern mathematical systems used to implement artificial neural networks (ANNs). The main focus of the work is on how each artificial neuron in the network is characterized by its current state, which is similar to nerve cells in the brain that can be excited or inhibited. A detailed description of the functioning of neurons is provided, including the processes of summation of input signals and activation using activation functions. Special attention is paid to multilayer neural networks and their ability to form complex multidimensional functions. The methods of building decision-making models based on the analysis of unclear situations and reference states determined by experts are defined. The process of comparing the real states of organizations with reference ones for making optimal decisions is considered. The importance of fuzzy logical operations for determining the degree of closeness of various situations is described. Fuzzy reference situations for cloud computing and their impact on decision-making in various scenarios are proposed. Examples of real and hypothetical fuzzy situations are given, and methods of determining the fuzzy correspondence between different reference situations are also considered. The final part of the abstract emphasizes the possibilities and advantages of using such models in cloud computing, emphasizing their importance for the development of organizations and systems.

Quadripartite quantum steering generated by single-pass cascaded nonlinear processes

Quantum steering has become an important resource for quantum information due to its unique asymmetry. In this paper, quadripartite quantum steering generated by single-pass nondegenerate optical parametric amplification cascaded with a sum-frequency process is investigated. Firstly, an idler beam is produced by a different-frequency process between pump and signal. Secondly, a sum-frequency beam is generated by a cascaded sum-frequency process between idler beam and pump in the same optical superlattice. Quadripartite quantum steering among pump, signal, idler and sum-frequency beams is demonstrated based on the criterion for multipartite quantum steering. The influences of nonlinear coupling coefficients κ, dimensionless propagation length ξ, and the initial values of pump and signal on the variation characteristic of the quadripartite steering are also investigated. The results indicate that slightly larger nonlinear coupling coefficients of cascaded nonlinear processes and weaker signals can achieve better quadripartite quantum steering. This simple quadripartite quantum steering preparation scheme does not require a resonant cavity and is easier to implement experimentally, with potential applications in quantum communication and computing.

Navigating the Landscape of IoT, Distributed Cloud Computing: A Comprehensive Review

This comprehensive academic exploration delves into the revolutionary convergence of the Internet of Things (IoT) with distributed cloud computing, redefining the realms of data processing, storage, and communication. The paper critically analyzes scholarly work from reputable journals, providing profound insights into this integration's multifaceted applications and underlying technological frameworks. The relevance of IoT, a network of interconnected devices and sensors, is emphasized through its significant impact on diverse sectors, including healthcare, education, agriculture, and smart cities. This impact is magnified by its extensive data collection, processing, and analysis capabilities, enabled through cloud computing platforms. The objective of the paper is to methodically compare and contrast contemporary scholarly contributions, shedding light on the diverse applications and technological infrastructures of IoT in conjunction with distributed cloud computing. This endeavor encompasses an examination of IoT-based cloud infrastructure, detailed analysis of specific needs, implementations, and applications of IoT-based cloud computing, and a review of various IoT cloud platforms. The paper also highlights the benefits of integrating IoT with cloud computing, elucidating significant advantages and potential future directions of this technology. Through this scholarly inquiry, the paper aims to offer an in-depth perspective on the state-of-the-art developments in IoT and distributed cloud computing. It underscores their significance and potential in shaping the future of digital technology and its applications across various domains.

How to Design Reinforcement Learning Methods for the Edge: An Integrated Approach toward Intelligent Decision Making

Extensive research has been carried out on reinforcement learning methods. The core idea of reinforcement learning is to learn methods by means of trial and error, and it has been successfully applied to robotics, autonomous driving, gaming, healthcare, resource management, and other fields. However, when building reinforcement learning solutions at the edge, not only are there the challenges of data-hungry and insufficient computational resources but also there is the difficulty of a single reinforcement learning method to meet the requirements of the model in terms of efficiency, generalization, robustness, and so on. These solutions rely on expert knowledge for the design of edge-side integrated reinforcement learning methods, and they lack high-level system architecture design to support their wider generalization and application. Therefore, in this paper, instead of surveying reinforcement learning systems, we survey the most commonly used options for each part of the architecture from the point of view of integrated application. We present the characteristics of traditional reinforcement learning in several aspects and design a corresponding integration framework based on them. In this process, we show a complete primer on the design of reinforcement learning architectures while also demonstrating the flexibility of the various parts of the architecture to be adapted to the characteristics of different edge tasks. Overall, reinforcement learning has become an important tool in intelligent decision making, but it still faces many challenges in the practical application in edge computing. The aim of this paper is to provide researchers and practitioners with a new, integrated perspective to better understand and apply reinforcement learning in edge decision-making tasks.

Enabling The E-NOSE: Evaluating Network Technologies for Optimized Odor Data Transmission in IoT and Edge Computing Applications

Electronic Noses (e-noses) are advanced sensors that are being increasingly used in the Internet of Things (IoT) field. They have various applications in environmental monitoring and medical diagnostics. Optimal transmission of odor-related data is essential for fully harnessing the capabilities of these electronic noses in various network environments. This research paper examines the issue of transmitting odor data in Internet of Things (IoT) applications by assessing recent progress in communication and network technologies. A thorough analysis of various network alternatives, such as Ethernet, cellular (4G/5G), Wi-Fi, Bluetooth, Zigbee, and LoRa, is provided, accompanied by an elaborate table displaying precise technical specifications. The analysis of LoRa, a Low-Power Wide-Area Network (LPWAN) technology, encompasses an investigation into its architecture, modulation method, device classifications, and protocols such as LoRaWAN. The analysis of Edge Computing and cloud-based approaches for managing odor data in IoT culminates in a tabular presentation that succinctly outlines their advantages and disadvantages. This study provides valuable information to enhance the efficiency of transmitting odor data and effectively utilize electronic nose technology in different emerging applications in the Internet of Things (IoT) field.

Neural Network-based Pattern Recognition in the Framework of Edge Computing

Neural network (NN) model has been widely used in pattern recognition (PR), speech recognition, image processing and other fields, but its application in edge computing (EC) environment faces performance and energy consumption problems. This article first introduced the basic structure and training process of NN, including backpropagation algorithms. Then, this article presented a NN modeling approach based on EC, including NN model compression, distributed NN model and knowledge distillation approach. Finally, this article implemented a PR model for the MNIST (Mixed National Institute of Standards and Technology database) dataset and analyzed the experimental results. The experimental outcomes indicated that the presented approach can significantly enhance the performance of the NN model in the EC environment, while ensuring a high recognition accuracy. The NN modeling approach based on EC can reduce the amount of computation and storage of the NN, thus improving the operating efficiency of the NN in the EC environment by 6%-12%. The NN modeling approach based on EC can optimize the performance and efficiency of the NN model in the EC environment, and provide new ideas and approaches for the application of NN in the EC environment.

Effective versus Floquet theory for the Kerr parametric oscillator

Parametric gates and processes engineered from the perspective of the static effective Hamiltonian of a driven system are central to quantum technology. However, the perturbative expansions used to derive static effective models may not be able to efficiently capture all the relevant physics of the original system. In this work, we investigate the conditions for the validity of the usual low-order static effective Hamiltonian used to describe a Kerr oscillator under a squeezing drive. This system is of fundamental and technological interest. In particular, it has been used to stabilize Schrödinger cat states, which have applications for quantum computing. We compare the states and energies of the effective static Hamiltonian with the exact Floquet states and quasi-energies of the driven system and determine the parameter regime where the two descriptions agree. Our work brings to light the physics that is left out by ordinary static effective treatments and that can be explored by state-of-the-art experiments.

Application of nature-inspired computing and implementation of algorithm for earthquake detection

Improve learning techniques and to prepare reference entropy which measures from the field of information theory, building upon entropy  generally calculating the difference between two probability distributions. Cross-entropy can be used as a loss function when optimizing classification models like logistic regression and artificial neural networks. The performance of the proposed neural network with respect to cross entropy is presented in this research.  The performance can be improved by including more data and optimization. The proposed research work will be used for time series data of events detection and prediction such as seismic event’s (Earthquake).The point of the present work is to tune the suitable algorithms for meaningful detection of the disastrous earthquake events and to generate the proper timely warning to the public.

Exploring trajectory embedding via spatial-temporal propagation for dynamic region representations

In smart cities, the development of urban regions stands as a fundamental pillar in the planning process, significantly influencing the overall urban living experience. Effective representations of regions are essential for providing fundamental insights and enabling various applications in urban computing. While research on regional embeddings, especially in dynamic urban representations, has gained considerable attention, there is often a lack of in-depth investigation into the reciprocal impact of mobility trajectories and spatiotemporal interactions. To address this challenge, we present a novel Spatial-Temporal Dynamic Representation framework for urban regions (STDR) to uncover the dynamic functions and variation patterns. Our model leverages interaction information between human mobility and regional features based on motion trajectories, enabling time and geographic encoding for each region. It then combines temporal propagation and spatial proximity to aggregate dynamic function representations. Moreover, it implements a spatiotemporal gating mechanism addressing the imbalance issue in global spatiotemporal transmission. Compared with state-of-the-art research methods, our method can achieve more accurate performance in two downstream tasks.

Ferroelectrically tuned tunneling photodetector based on graphene/h-BN/In2Se3 heterojunction

Transition metal dichalcogenides (TMDs) show promise for photodetection across various wavelengths. However, photoconductive devices often face various limitations such as low photoresponsivity or high dark current. This study introduces a highly sensitive photodetector using a graphene/h-BN/In2Se3 heterostructure. A substantial electron barrier (2.72 eV) at the graphene/h-BN junction effectively suppresses dark current, while the small hole barrier (1.39 eV) at the h-BN/In2Se3 junction facilitates efficient tunneling of photocarriers under illumination. The photodetector demonstrates a fast response time (Tr = 15 ms, Td = 5 ms), outstanding responsivity (1.18 × 104 μA/W), and detectivity (1.74 × 1010 Jones) at 4 V bias and 637 nm light illumination. Spectral measurements reveal a broad detection range from visible to near-infrared (450–1310 nm). Furthermore, leveraging the non-volatile ferroelectric properties of In2Se3 allows modulation of the barrier height, enabling non-volatile tunable photoconductivity. This study represents a significant advancement in ferroelectric based optoelectronic detection devices, providing strong support for applications in high-performance computing, neural networks, and related domains.

Riemannian quantum circuit optimization for Hamiltonian simulation

Hamiltonian simulation, i.e. simulating the real time evolution of a target quantum system, is a natural application of quantum computing. Trotter-Suzuki splitting methods can generate corresponding quantum circuits; however, a faithful approximation can lead to relatively deep circuits. Here we start from the insight that for translation invariant systems, the gates in such circuit topologies can be further optimized on classical computers to decrease the circuit depth and/or increase the accuracy. We employ tensor network techniques and devise a method based on the Riemannian trust-region algorithm on the unitary matrix manifold for this purpose. For the Ising and Heisenberg models on a one-dimensional lattice, we achieve orders of magnitude accuracy improvements compared to fourth-order splitting methods. The optimized circuits could also be of practical use for the time-evolving block decimation algorithm.

Machine Learning-Based Kernel Selector for SpMV Optimization in Graph Analysis

Sparse Matrix and Vector multiplication (SpMV) is one of the core algorithms in various large-scale scientific computing and real-world applications. With the rapid development of AI and big data, the input vector in SpMV becomes sparse in many application fields. Especially in some graph analysis calculations, the sparsity of the input vector will change with the running of the program, and the non-zero element distribution of the adjacency matrix of some graph data has the power law property, leading to serious load imbalance, which requires additional optimization means. Therefore, the optimal SpMV kernel may be different, and a single SpMV kernel can no longer meet the acceleration requirements. In this paper, we propose a decision tree-based adaptive SpMV framework, named DTSpMV, that can automatically select appropriate SpMV kernels according to different input data in iterations of graph computation. Based on the analysis of computing patterns, bit-array compression algorithms, and serial and parallel algorithms, we encapsulate nine SpMV kernels within the framework. We explore machine learning-based kernel selectors in terms of both accuracy and runtime overhead. Experimental results on NVIDIA Tesla T4 GPU show that our adaptive framework achieves the arithmetic average performance improvement of \(152\% \) compared to the SpMV kernel in cuSPARSE.

Broadband nonlinear modulation of incoherent light using a transparent optoelectronic neuron array

Nonlinear optical processing of ambient natural light is highly desired for computational imaging and sensing. Strong optical nonlinear response under weak broadband incoherent light is essential for this purpose. By merging 2D transparent phototransistors (TPTs) with liquid crystal (LC) modulators, we create an optoelectronic neuron array that allows self-amplitude modulation of spatially incoherent light, achieving a large nonlinear contrast over a broad spectrum at orders-of-magnitude lower intensity than achievable in most optical nonlinear materials. We fabricated a 10,000-pixel array of optoelectronic neurons, and experimentally demonstrated an intelligent imaging system that instantly attenuates intense glares while retaining the weaker-intensity objects captured by a cellphone camera. This intelligent glare-reduction is important for various imaging applications, including autonomous driving, machine vision, and security cameras. The rapid nonlinear processing of incoherent broadband light might also find applications in optical computing, where nonlinear activation functions for ambient light conditions are highly sought.