We present the results of the evaluation we carried out concerning the cache memory simulation programs available on the Internet. We also present the results of a research, which we conducted, concerning students'...
详细信息
We present the results of the evaluation we carried out concerning the cache memory simulation programs available on the Internet. We also present the results of a research, which we conducted, concerning students' understanding of the cache memory internal operation and implementation and the role of the cache memory in the communication between the CPU and the main memory. Based on the evaluation results and the research results, we designed a cache memory simulation program, which implements a realistic model of the cache operation. The main results of the program's evaluation pointed out that, after using the simulation program the students had a better understanding of the cache memory operations (identification, placement, replacement) for a direct mapped cache and a set associative cache.
This paper presents a modification with further experiments of a segmentation algorithm based on feature selection in wavelet space of ours [9]. The aim is to automatically separate in postal envelopes the regions rel...
详细信息
This paper presents a modification with further experiments of a segmentation algorithm based on feature selection in wavelet space of ours [9]. The aim is to automatically separate in postal envelopes the regions related to background, stamps, rubber stamps, and the address blocks. First, a typical image of a postal envelope is decomposed using Mallat algorithm and Haar basis. High frequency channel outputs are analyzed to locate salient points in order to separate the background. A statistical hypothesis test is taken to decide upon more consistent regions in order to clean out some noise left. The selected points are projected back to the original gray level image, where the evidence from the wavelet space is used to start a growing process to include the pixels more likely to belong to the regions of stamps, rubber stamps, and written area. We have modified the growing process controlled by the salient points and the results were greatly improved reaching success rate of over 97%. Experiments are run using original postal envelopes from the Brazilian Post Office Agency, and here we report results on 440 images with many different layouts and backgrounds.
Presents two algorithms for LC unconstrained optimization problems which use the second order Dini upper directional derivative. Simplicity of the methods to use and perform; Discussion of related properties of the it...
详细信息
Presents two algorithms for LC unconstrained optimization problems which use the second order Dini upper directional derivative. Simplicity of the methods to use and perform; Discussion of related properties of the iteration function.
In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and s...
In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data—from patient records to imaging—graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters and with minimal to no retraining. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pretraining, facilitate interactive feedback loops, and foster human–AI collaboration, paving the way toward clinically meaningful predictions.
暂无评论