With the remarkable empirical success of neural networks across diverse scientific disciplines,rigorous error and convergence analysis are also being developed and ***,there has been little theoretical work focusing o...
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With the remarkable empirical success of neural networks across diverse scientific disciplines,rigorous error and convergence analysis are also being developed and ***,there has been little theoretical work focusing on neural networks in solving interface *** this paper,we perform a convergence analysis of physics-informed neural networks(PINNs)for solving second-order elliptic interface ***,we consider PINNs with domain decomposition technologies and introduce gradient-enhanced strategies on the interfaces to deal with boundary and interface jump *** is shown that the neural network sequence obtained by minimizing a Lipschitz regularized loss function converges to the unique solution to the interface problem in H2 as the number of samples *** experiments are provided to demonstrate our theoretical analysis.
With the development of the nonvolatile memory(NVM),using NVM in the design of the cache and scratchpad memory(SPM)has been *** paper presents a data variable allocation(DVA)algorithm based on the genetic algorithm fo...
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With the development of the nonvolatile memory(NVM),using NVM in the design of the cache and scratchpad memory(SPM)has been *** paper presents a data variable allocation(DVA)algorithm based on the genetic algorithm for NVM-based SPM to prolong the *** lifetime can be formulated indirectly as the write counts on each SPM *** the differences between global variables and stack variables,our optimization model has three *** constraints of the central processing unit(CPU)utilization and size are used for all variables,while no-overlay constraint is only used for stack *** satisfy the constraints of the optimization model,we use the greedy strategy to generate the initial population which can determine whether data variables are allocated to SPM and distribute them evenly on SPM ***,we use the Mälardalen worst case executive time(WCET)benchmark to evaluate our *** experimental results show that the DVA algorithm can not only obtain close-to-optimal solutions,but also prolong the lifetime by 9.17% on average compared with SRAM-based SPM.
with the growth of network scale, present researches on the attack path discovery often encounter the problem of search space explosion that results in fails. To tackle the problem of attack path discovery for large-s...
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This paper mainly studies the optimal scheme of dynamic adjustment of active mirror in FAST system by establishing mathematical model. According to the constraints such as distance variation range, a mathematical mode...
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ISBN:
(纸本)9781450384155
This paper mainly studies the optimal scheme of dynamic adjustment of active mirror in FAST system by establishing mathematical model. According to the constraints such as distance variation range, a mathematical model describing the dynamic trajectory of the position and angle of the active mirror is established, and the correctness of the solution of the model is verified by simulation. The optimal surface model is selected from all the results. When the measured object is directly above the fast system, it is modeled based on the ideal parabola to ensure that the expansion constraints and threshold conditions of the actuator are met. Because the distribution of actuator and cable in the whole fast system is not a uniform sphere, the change of radial distance of cable can be considered as the expansion of actuator; In addition, the model is optimized from two aspects: adjusting the position of focus on the horizontal plane and changing the size of zoom alignment. From these two aspects, the parabolic focusing range satisfying the expansion range is obtained. Finally, the optimization strategy of dynamic adjustment of active mirror in fast system is given.
Virtual staining has shown great promise in realizing a rapid and low-cost clinical alternative for pathological examinations, eliminating the need for chemical reagents and laborious staining procedures. However, mos...
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Knowledge graph representation learning provides a lot of help for subsequent tasks such as knowledge graph completion, information retrieval, and intelligent question answering. By representing the knowledge graph as...
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ISBN:
(纸本)9781450384094
Knowledge graph representation learning provides a lot of help for subsequent tasks such as knowledge graph completion, information retrieval, and intelligent question answering. By representing the knowledge graph as low-dimensional dense vectors, these tasks can improve efficiency significantly. However, limited by the sparseness and huge scale of the actual structure, representation learning only from a structural perspective can no longer meet the research *** improve the performance, researchers introduced auxiliary information into representation learning. This paper focuses on models using text as auxiliary information, dividing all text-combined models into two categories: Closed-world assumption models and Open-world assumption models. The former is limited by the model's demand for the structural representation of the graph itself, and can only predict the entities and relationships that already exist in the knowledge graph. The latter can handle entities that have not previously been seen during model training, and connect brand-new entities to the knowledge graph, which is more in line with the dynamic trend of the knowledge graph in real world. Open-world assumption models can be further subdivided into multiple types according different joint functions, such as alignment function, fusion function, score function and transformation function. This paper summarizes existing methods in detail and looks forward to future possible research directions.
With the proliferation of data-intensive industrial applications, the collaboration of computing powers among standalone edge servers is vital to provision such services for smart devices. In this paper, we propose an...
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Software vulnerability mining is an important component of network attack and defense technology. To address the problems of high leakage rate and false positive rate of existing static analysis methods, this paper pr...
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Ever-growing CNN size incurs a significant amount of redundancy in model parameters, which in turn, puts considerable burden on hardware. Unstructured pruning is widely used to reduce model sparsity. While, the irregu...
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ISBN:
(纸本)9781450397339
Ever-growing CNN size incurs a significant amount of redundancy in model parameters, which in turn, puts considerable burden on hardware. Unstructured pruning is widely used to reduce model sparsity. While, the irregularity introduced by unstructured pruning makes it difficult to accelerate sparse CNNs on systolic array. To address this issue, a variety of accelerators have been proposed. SIGMA, the state-of-the-art sparse GEMM accelerator, achieves significant speedup over systolic array. However, SIGMA suffers from two disadvantages: 1) it only supports one-side sparsity, leaving potential for further performance gains; 2) SIGMA improves utilization of large-sized systolic arrays at the cost of extra overhead. In this paper, we propose DSSA, a dual-side sparse systolic array, to accelerate CNN training. DSSA bases its designs on a small-sized systolic array, which naturally achieves higher cell utilization without additional overhead. To facilitate dual-side sparsity processing, DSSA utilizes a cross-cycle reduction module to accumulate partial sum that belongs to the same column but being processed in different cycles. A comprehensive design space exploration is performed to seek the local optimal configurations for DSSA. We implement the logic design of DSSA using Verilog in RTL and evaluate its performance using a C++-based cycle-accurate performance simulator we built. Experimental results show that DSSA delivers, on average, a speedup of 2.13x and 13.81x over SIGMA and a basic systolic array with the same number of cells. Compared to SIGMA, DSSA incurs 16.59% area overhead and 25.49% power overhead when sparse filter is excluded, as SIGMA did.
Recently, a novel form of audio partial forgery has posed challenges to its forensics, requiring advanced countermeasures to detect subtle forgery manipulations within long-duration audio. However, existing countermea...
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