In this paper the author investigates the following predator-prey model with prey-taxis and rotational?ux terms■in a bounded domain with smooth *** presents the global existence of generalized solutions to the model...
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In this paper the author investigates the following predator-prey model with prey-taxis and rotational?ux terms■in a bounded domain with smooth *** presents the global existence of generalized solutions to the model■in any dimension.
We present a novel attention-based mechanism to learn enhanced point features for point cloud processing tasks, e.g., classification and segmentation. Unlike prior studies, which were trained to optimize the weights o...
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We present a novel attention-based mechanism to learn enhanced point features for point cloud processing tasks, e.g., classification and segmentation. Unlike prior studies, which were trained to optimize the weights of a pre-selected set of attention points, our approach learns to locate the best attention points to maximize the performance of a specific task, e.g., point cloud classification. Importantly, we advocate the use of single attention point to facilitate semantic understanding in point feature learning. Specifically,we formulate a new and simple convolution, which combines convolutional features from an input point and its corresponding learned attention point(LAP). Our attention mechanism can be easily incorporated into state-of-the-art point cloud classification and segmentation networks. Extensive experiments on common benchmarks, such as Model Net40, Shape Net Part, and S3DIS, all demonstrate that our LAP-enabled networks consistently outperform the respective original networks, as well as other competitive alternatives, which employ multiple attention points, either pre-selected or learned under our LAP framework.
Reachability query plays a vital role in many graph analysis *** researches proposed many methods to efficiently answer reachability queries between vertex *** many real graphs are labeled graph,it highly demands Labe...
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Reachability query plays a vital role in many graph analysis *** researches proposed many methods to efficiently answer reachability queries between vertex *** many real graphs are labeled graph,it highly demands Label-Constrained Reachability(LCR)query inwhich constraint includes a set of labels besides vertex *** researches proposed several methods for answering some LCR queries which require appearance of some labels specified in constraints in the *** that constraint may be a label set,query constraint may be ordered labels,namely OLCR(Ordered-Label-Constrained Reachability)queries which retrieve paths matching a sequence of ***,no solutions are available for ***,we propose DHL,a novel bloom filter based indexing technique for answering OLCR *** can be used to check reachability between vertex *** the answers are not no,then constrained DFS is ***,we employ DHL followed by performing constrained DFS to answer OLCR *** show that DHL has a bounded false positive rate,and it's powerful in saving indexing time and *** experiments on 10 real-life graphs and 12 synthetic graphs demonstrate that DHL achieves about 4.8-22.5 times smaller index space and 4.6-114 times less index construction time than two state-of-art techniques for LCR queries,while achieving comparable query response *** results also show that our algorithm can answer OLCR queries effectively.
Speech enhancement is the task of taking a noisy speech input and pro-ducing an enhanced speech *** recent years,the need for speech enhance-ment has been increased due to challenges that occurred in various applicati...
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Speech enhancement is the task of taking a noisy speech input and pro-ducing an enhanced speech *** recent years,the need for speech enhance-ment has been increased due to challenges that occurred in various applications such as hearing aids,Automatic Speech Recognition(ASR),and mobile speech communication *** of the Speech Enhancement research work has been carried out for English,Chinese,and other European *** a few research works involve speech enhancement in Indian regional *** this paper,we propose a two-fold architecture to perform speech enhancement for Tamil speech signal based on convolutional recurrent neural network(CRN)that addresses the speech enhancement in a real-time single channel or track of sound created by the *** thefirst stage mask based long short-term mem-ory(LSTM)is used for noise suppression along with loss function and in the sec-ond stage,Convolutional Encoder-Decoder(CED)is used for speech *** proposed model is evaluated on various speaker and noisy environments like Babble noise,car noise,and white Gaussian *** proposed CRN model improves speech quality by 0.1 points when compared with the LSTM base model and also CRN requires fewer parameters for *** performance of the pro-posed model is outstanding even in low Signal to Noise Ratio(SNR).
Energy efficiency is the prime concern in Wireless Sensor Networks(WSNs) as maximized energy consumption without essentially limits the energy stability and network lifetime. Clustering is the significant approach ess...
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Energy efficiency is the prime concern in Wireless Sensor Networks(WSNs) as maximized energy consumption without essentially limits the energy stability and network lifetime. Clustering is the significant approach essential for minimizing unnecessary transmission energy consumption with sustained network lifetime. This clustering process is identified as the Non-deterministic Polynomial(NP)-hard optimization problems which has the maximized probability of being solved through metaheuristic *** adoption of hybrid metaheuristic algorithm concentrates on the identification of the optimal or nearoptimal solutions which aids in better energy stability during Cluster Head(CH) selection. In this paper,Hybrid Seagull and Whale Optimization Algorithmbased Dynamic Clustering Protocol(HSWOA-DCP)is proposed with the exploitation benefits of WOA and exploration merits of SEOA to optimal CH selection for maintaining energy stability with prolonged network lifetime. This HSWOA-DCP adopted the modified version of SEagull Optimization Algorithm(SEOA) to handle the problem of premature convergence and computational accuracy which is maximally possible during CH selection. The inclusion of SEOA into WOA improved the global searching capability during the selection of CH and prevents worst fitness nodes from being selected as CH, since the spiral attacking behavior of SEOA is similar to the bubble-net characteristics of WOA. This CH selection integrates the spiral attacking principles of SEOA and contraction surrounding mechanism of WOA for improving computation accuracy to prevent frequent election process. It also included the strategy of levy flight strategy into SEOA for potentially avoiding premature convergence to attain better trade-off between the rate of exploration and exploitation in a more effective manner. The simulation results of the proposed HSWOADCP confirmed better network survivability rate, network residual energy and network overall throughput on par wi
In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems,...
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In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, and selfdriving capabilities for improved system performance. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.
Unikernels provide an efficient and lightweight way to deploy cloud computing services in application-specialized and single-address-space virtual machines (VMs). They can efficiently deploy hundreds of unikernel-base...
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Unikernels provide an efficient and lightweight way to deploy cloud computing services in application-specialized and single-address-space virtual machines (VMs). They can efficiently deploy hundreds of unikernel-based VMs in a single physical server. In such a cloud computing platform, main memory is the primary bottleneck resource for high-density application deployment. Recently, non-volatile memory (NVM) technologies has become increasingly popular in cloud data centers because they can offer extremely large memory capacity at a low expense. However, there still remain many challenges to utilize NVMs for unikernel-based VMs, such as the difficulty of heterogeneous memory allocation and high performance overhead of address *** this paper, we present UCat, a heterogeneous memory management mechanism that support multi-grained memory allocation for unikernels. We propose front-end/back-end cooperative address space mapping to expose the host memory heterogeneity to unikernels. UCat exploits large pages to reduce the cost of two-layer address translation in virtualization environments, and leverages slab allocation to reduce memory waste due to internal memory fragmentation. We implement UCat based on a popular unikernel--OSv and conduct extensive experiments to evaluate its efficiency. Experimental results show that UCat can reduce the memory consumption of unikernels by 50% and TLB miss rate by 41%, and improve the throughput of real-world benchmarks such as memslap and YCSB by up to 18.5% and 14.8%, respectively.
Data race is one of the most important concurrent anomalies in multi-threaded *** con-straint-based techniques are leveraged into race detection,which is able to find all the races that can be found by any oth-er soun...
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Data race is one of the most important concurrent anomalies in multi-threaded *** con-straint-based techniques are leveraged into race detection,which is able to find all the races that can be found by any oth-er sound race ***,this constraint-based approach has serious limitations on helping programmers analyze and understand data ***,it may report a large number of false positives due to the unrecognized dataflow propa-gation of the ***,it recommends a wide range of thread context switches to schedule the reported race(in-cluding the false one)whenever this race is exposed during the constraint-solving *** ad hoc recommendation imposes too many context switches,which complicates the data race *** address these two limitations in the state-of-the-art constraint-based race detection,this paper proposes DFTracker,an improved constraint-based race detec-tor to recommend each data race with minimal thread context ***,we reduce the false positives by ana-lyzing and tracking the dataflow in the *** this means,DFTracker thus reduces the unnecessary analysis of false race *** further propose a novel algorithm to recommend an effective race schedule with minimal thread con-text switches for each data *** experimental results on the real applications demonstrate that 1)without removing any true data race,DFTracker effectively prunes false positives by 68%in comparison with the state-of-the-art constraint-based race detector;2)DFTracker recommends as low as 2.6-8.3(4.7 on average)thread context switches per data race in the real world,which is 81.6%fewer context switches per data race than the state-of-the-art constraint based race ***,DFTracker can be used as an effective tool to understand the data race for programmers.
As embodied intelligence(EI), large language models(LLMs), and cloud computing continue to advance, Industry5.0 facilitates the development of industrial artificial intelligence(Ind AI) through cyber-physical-social s...
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As embodied intelligence(EI), large language models(LLMs), and cloud computing continue to advance, Industry5.0 facilitates the development of industrial artificial intelligence(Ind AI) through cyber-physical-social systems(CPSSs) with a human-centric focus. These technologies are organized by the system-wide approach of Industry 5.0, in order to empower the manufacturing industry to achieve broader societal goals of job creation, economic growth, and green production. This survey first provides a general framework of smart manufacturing in the context of Industry 5.0. Wherein, the embodied agents, like robots, sensors, and actuators, are the carriers for Ind AI, facilitating the development of the self-learning intelligence in individual entities, the collaborative intelligence in production lines and factories(smart systems), and the swarm intelligence within industrial clusters(systems of smart systems). Through the framework of CPSSs, the key technologies and their possible applications for supporting the single-agent, multi-agent and swarm-agent embodied Ind AI have been reviewed, such as the embodied perception, interaction, scheduling, multi-mode large language models, and collaborative training. Finally, to stimulate future research in this area, the open challenges and opportunities of applying Industry 5.0 to smart manufacturing are identified and discussed. The perspective of Industry 5.0-driven manufacturing industry aims to enhance operational productivity and efficiency by seamlessly integrating the virtual and physical worlds in a human-centered manner, thereby fostering an intelligent, sustainable, and resilient industrial landscape.
Welcome to the twelfth issue of 2023 in the IEEE/CAA Journal of Automatica Sinica(JAS).In the sixth issue of2023,I systematically addressed the latest development of Meta Vehicles,and sorted out some important contrib...
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Welcome to the twelfth issue of 2023 in the IEEE/CAA Journal of Automatica Sinica(JAS).In the sixth issue of2023,I systematically addressed the latest development of Meta Vehicles,and sorted out some important contributions published in the IEEE/CAA JAS focusing on control,estimation,and optimization of automated vehicles with reliability,security,efficiency,and intelligence.
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