Modern networked computing systems follow scenarios that differ from those modeled by classical Turing machines. For example, their architecture and functionality may change over time as components enter or disappear....
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Given an n-tape nondeterministic finite automaton (NFA) M with a one-way read-only head per tape and a right end marker $ on each tape, and a nonnegative integer k, we say that M is weakly k-synchronized if for every ...
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the growing demand for more efficient and effective healthcare services, coupled with an implicit requirement for supporting citizen mobility and continuity of care, is currently setting the stage for the exploitation...
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those who are not in computerscience background, the preparation of the tutorial of cyber security can be more complex and difficult to *** paper generates a degree level virtualforensics and security course in a com...
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the Transformer architecture has achieved remarkable success in a number of domains including natural language processing and computer vision. However, when it comes to graph-structured data, transformers have not ach...
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ISBN:
(纸本)9781713871088
the Transformer architecture has achieved remarkable success in a number of domains including natural language processing and computer vision. However, when it comes to graph-structured data, transformers have not achieved competitive performance, especially on large graphs. In this paper, we identify the main deficiencies of current graph transformers: (1) Existing node sampling strategies in Graph Transformers are agnostic to the graph characteristics and the training process. (2) Most sampling strategies only focus on local neighbors and neglect the long-range dependencies in the graph. We conduct experimental investigations on synthetic datasets to show that existing sampling strategies are sub-optimal. To tackle the aforementioned problems, we formulate the optimization strategies of node sampling in Graph Transformer as an adversary bandit problem, where the rewards are related to the attention weights and can vary in the training procedure. Meanwhile, we propose a hierarchical attention scheme with graph coarsening to capture the long-range interactions while reducing computational complexity. Finally, we conduct extensive experiments on real-world datasets to demonstrate the superiority of our method over existing graph transformers and popular GNNs.
this talk will give an overview of an interdisciplinary research project being developed at the University of Memphis, led by a team of computer scientists, psychologists, and educators. the project's goal is to r...
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the indexing and retrieval of multimedia items is difficult due to the semantic gap between the user's perception of the data and the descriptions we can derive automatically from the data using computer vision, s...
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We explore the use of projects to replace conventional problem sets as learning tools in a graduate-level digital signal processing (DSP) course. To help students draw strong connections between the theory and practic...
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We explore the use of projects to replace conventional problem sets as learning tools in a graduate-level digital signal processing (DSP) course. To help students draw strong connections between the theory and practice of DSP, we propose replacing weekly problem sets with four larger-scale projects. Each project is based on a real-world application of DSP and is designed to incorporate a subset of the core concepts covered in the course. In addition to theoretical components, the projects include hands-on MATLAB elements. We hypothesize that employing application-based projects rather than conventional problem sets will improve students' understanding of fundamental DSP concepts and their ability to make connections between theory and practice. the success of the project-based assignments will be evaluated by administering the Signals and Systems Concept Inventory, as well as a student survey
Single look-ahead unit resolution (SLUR) algorithm is a nondeterministic polynomial time algorithm which for a given input formula in a conjunctive normal form (CNF) either outputs its satisfying assignment or gives u...
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