Robotic Intra-Operative Ultrasound:Virtual Environments and Parallel Systems

Robotic intra-operative ultrasound has the potential to improve the conventional practice of diagnosis and procedure guidance that are currently performed manually.Working towards automatic or semi-automatic ultrasound,being able to define ultrasound views and the corresponding probe poses via intelligent approaches become crucial.Based on the concept of parallel system which incorporates the ingredients of artificial systems,computational experiments,and parallel execution,this paper utilized a recent developed robotic trans-esophageal ultrasound system as the study object to explore the method for developing the corresponding virtual environments and present the potential applications of such systems.The proposed virtual system includes the use of 3 D slicer as the main workspace and graphic user interface(GUI),Matlab engine to provide robotic control algorithms and customized functions,and PLUS(Public software Library for Ultra Sound imaging research)toolkit to generate simulated ultrasound images.Detailed implementation methods were presented and the proposed features of the system were explained.Based on this virtual system,example uses and case studies were presented to demonstrate its capabilities when used together with the physical TEE robot.This includes standard view definition and customized view optimization for pre-planning and navigation,as well as robotic control algorithm evaluations to facilitate real-time automatic probe pose adjustments.To conclude,the proposed virtual system would be a powerful tool to facilitate the further developments and clinical uses of the robotic intra-operative ultrasound systems.

Parallel Inference for Real-Time Machine Learning Applications

Hyperparameter tuning is a key step in developing high-performing machine learning models, but searching large hyperparameter spaces requires extensive computation using standard sequential methods. This work analyzes the performance gains from parallel versus sequential hyperparameter optimization. Using scikit-learn’s Randomized SearchCV, this project tuned a Random Forest classifier for fake news detection via randomized grid search. Setting n_jobs to -1 enabled full parallelization across CPU cores. Results show the parallel implementation achieved over 5× faster CPU times and 3× faster total run times compared to sequential tuning. However, test accuracy slightly dropped from 99.26% sequentially to 99.15% with parallelism, indicating a trade-off between evaluation efficiency and model performance. Still, the significant computational gains allow more extensive hyperparameter exploration within reasonable timeframes, outweighing the small accuracy decrease. Further analysis could better quantify this trade-off across different models, tuning techniques, tasks, and hardware.

Parallel Control for Continuous-Time Linear Systems:A Case Study

In this paper,a new parallel controller is developed for continuous-time linear systems.The main contribution of the method is to establish a new parallel control law,where both state and control are considered as the input.The structure of the parallel control is provided,and the relationship between the parallel control and traditional feedback controls is presented.Considering the situations that the systems are controllable and incompletely controllable,the properties of the parallel control law are analyzed.The parallel controller design algorithms are given under the conditions that the systems are controllable and incompletely controllable.Finally,numerical simulations are carried out to demonstrate the effectiveness and applicability of the present method.Index Terms-Continuous-time linear systems,digital twin,parallel controller,parallel intelligence,parallel systems.

Parallel Learning:Overview and Perspective for Computational Learning Across Syn2Real and Sim2Real

The virtual-to-real paradigm,i.e.,training models on virtual data and then applying them to solve real-world problems,has attracted more and more attention from various domains by successfully alleviating the data shortage problem in machine learning.To summarize the advances in recent years,this survey comprehensively reviews the literature,from the viewport of parallel intelligence.First,an extended parallel learning framework is proposed to cover main domains including computer vision,natural language processing,robotics,and autonomous driving.Second,a multi-dimensional taxonomy is designed to organize the literature in a hierarchical structure.Third,the related virtual-toreal works are analyzed and compared according to the three principles of parallel learning known as description,prediction,and prescription,which cover the methods for constructing virtual worlds,generating labeled data,domain transferring,model training and testing,as well as optimizing the strategies to guide the task-oriented data generator for better learning performance.Key issues remained in virtual-to-real are discussed.Furthermore,the future research directions from the viewpoint of parallel learning are suggested.

Optimization of earth observation satellite system based on parallel systems and computational experiments

Earth observation satellite system （EOSS） is the main space platform to collect ground information. Op- timization of EOSS is still a difficult problem, as it is a complex system concerning a great deal of design variables and uncertain factors. To solve the problem, an optimization framework based on parallel system and computational experi- ments is proposed. An artificial system for EOSS is firstly constructed, which is the integration of resource data, task data, environment data and related operation rules. Real EOSS together with artificial EOSS constitute the parallel systems for EOSS. Based on the parallel systems, concept of computational experiments is detailed. Moreover, surrogate models are built to approximate real EOSS. Genetic algorithm and improved general pattern search method are adopted to optimize the model. According to the framework, a case study is carried out. Through the results, we illustrated the proposed framework to be useful and effective for EOSS optimization problem.

Strategy and Simulation of Adaptive RID for Distributed Dynamic Load Balancing in Parallel Systems

Dynamic load balancing schemes are significant for efficiently executing nonuniform problems in highly parallel multicomputer systems. The objective is to minimize the total execution time of single applications. This paper has proposed an ARID strategy for distributed dynamic load balancing. Its principle and control protocol are described, and the communication overhead, the effect on system stability and the performance efficiency are analyzed. Finally,simulation experiments are carried out to compare the adaptive strategy with other dynamic load balancing schemes.

Analytical and Computational Analysis of Flow Splitting in Multiple, Parallel Channels Systems

Previous analytical results on flow splitting are generalized to consider multiple boiling channels systems. The analysis is consistent with the approximations usually adopted in the use of systems codes (like RELAP5 and TRACE5, among others) commonly applied to perform safety analyses of nuclear power plants. The problem is related to multiple, identical, parallel boiling channels, connected through common plena. A theoretical model limited in scope explains this flow splitting without reversal. The unified analysis performed and the confirmatory computational results found are summarized in this paper. New maps showing the zones where this behavior is predicted are also shown considering again twin pipes. Multiple pipe systems have been found not easily amenable for analytical analysis when dealing with more than four parallel pipes. However, the particular splitting found (flow along N pipes dividing in one standalone pipe flow plus N -1 identical pipe flows) has been verified up to fourteen pipes, involving calculations in systems with even and odd number of pipes using the RELAP5 systems thermal-hydraulics code.

Parallel Learning:a Perspective and a Framework

The development of machine learning in complex system is hindered by two problems nowadays.The first problem is the inefficiency of exploration in state and action space,which leads to the data-hungry of some state-of-art data-driven algorithm.The second problem is the lack of a general theory which can be used to analyze and implement a complex learning system.In this paper,we proposed a general methods that can address both two issues.We combine the concepts of descriptive learning,predictive learning,and prescriptive learning into a uniform framework,so as to build a parallel system allowing learning system improved by self-boosting.Formulating a new perspective of data,knowledge and action,we provide a new methodology called parallel learning to design machine learning system for real-world problems.

Research Progress of Parallel Control and Management

Based on ACP(artificial systems,computational experiments,and parallel execution)methodology,parallel control and management has become a popularly systematic and complete solution for the control and management of complex systems.This paper focuses on summarizing comprehensive review of the research literature of parallel control and management achieved in the recent years including the theoretical framework,core technologies,and the application demonstration.The future research,application directions,and suggestions are also discussed.

Parallel Algorithm Case Study Using Virtual Machine

This paper takes the Sobel operator as example to study parallel sequential algorithm onto a memory-sharing multiprocessor by using a virtual machine. Several different parallel algorithms using function decomposition and/or data decomposition methods are compared and their performances are analyzed in terms of processor utilization, data traffic, shared memory access, and synchronization overhead. The analysis is validated through a simulation experiment on the virtual machine of 64 parallel processors. Conclusions are presented at the end of this paper.

Simulation Modeling by Fuzzy Nets

Fuzzy technology is a newly developed discipline based on fuzzy mathematics. In the recent years, it has been successfully applied into many areas, such as process control, diagnosis, evaluation, decision making and scheduling, especially in simulation where accurate mathematical models can not or very hard be established. In this paper, to meet the demands of fuzzy simulation, two fuzzy nets will first be presented, which are quite suitable for modeling the parallel or concurrent systems with fuzzy behavior. Then, a concept of active simulation will be introduced, in which the simulation model not only can show its fuzzy behavior, but also has a certain ability which can actively perform many very useful actions, such as automatic warning, realtime monitoring, simulation result checking, simulation model self-adapting, error recovery, simulating path tracing, system states inspecting and exception handling, by a unified approach while some specified events occur. The simulation model described by this powerful simulation modeling tool is concurrently driven by a network interpreter and an event monitor that all can be implemented by software or hardware. Besides, some interesting applications are given in the paper.

Adapting Backward Error Recovery to Parallel Real Time Systems

The problem of adapting backward error recovery to parallel real time systems is discussed in this paper. Because of error propagation among different cooperating processes, an error occurring in one process may influence some important outputs in other processes. Therefore, a local output has to be delayed until its validity is confirmed globally. Since backward error recovery adopts redundancy of computing time instead of processing equipment, the variation of the actual execution time of a cooperating process may be very large if it works in an unreliable environment. These problems are the primary obstacles to be removed. Previous studies focus their attentions on how to eliminate domino-effect dynamically. But backward error recovery cannot be applied directly in parallel real time systems even under the condition that no domino-effect exists. How to reduce output delays efficiently if no domino-effect remains? How to estimate this delay time? How to calculate the actual execution time of every process and how to schedule these processes under an unstable condition? These problems were omitted in literature unfortunately. The interest of this paper is to provide satisfactory solutions to these problems to make it possible to adopt backward error recovery efficiently in parallel real time systems.

Mutually trustworthy human-machine knowledge automation and hybrid augmented intelligence:mechanisms and applications of cognition,management,and control for complex systems

In this paper,we aim to illustrate the concept of mutually trustworthy human-machine knowledge automation(HM-KA)as the technical mechanism of hybrid augmented intelligence(HAI)based complex system cognition,management,and control(CMC).We describe the historical development of complex system science and analyze the limitations of human intelligence and machine intelligence.The need for using human-machine HAI in complex systems is then explained in detail.The concept of“mutually trustworthy HM-KA”mechanism is proposed to tackle the CMC challenge,and its technical procedure and pathway are demonstrated using an example of corrective control in bulk power grid dispatch.It is expected that the proposed mutually trustworthy HM-KA concept can provide a novel and canonical mechanism and benefit real-world practices of complex system CMC.

Database Research： Achievements and Challenges

Database system is the infrastructure of the modern information system. The R＆D in the database system and its technologies is one of the important research topics in the field. The database R＆D in China took off later but it moves along by giant steps. This report presents the achievements Renmin University of China （RUC） has made in the past 25 years and at the same time addresses some of the research projects we, RUC, are currently working on. The National Natural Science Foundation of China supports and initiates most of our research projects and these successfully conducted projects have produced fruitful results.

Fine-grained management of I/O optimizations based on workload characteristics

With the advent of new computing paradigms,parallel file systems serve not only traditional scientific computing applications but also non-scientific computing applications,such as financial computing,business,and public administration.Parallel file systems provide storage services for multiple applications.As a result,various requirements need to be met.However,parallel file systems usually provide a unified storage solution,which cannot meet specific application needs.In this paper,an extended tile handle scheme is proposed to deal with this problem.The original file handle is extended to record I/O optimization information,which allows file systems to specify optimizations for a file or directory based on workload characteristics.Therefore,fine-grained management of I/O optimizations can be achieved.On the basis of the extended file handle scheme,data prefetching and small file optimization mechanisms are proposed for parallel file systems.The experimental results show that the proposed approach improves the aggregate throughput of the overall system by up to 189.75%.