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- Cloud computing (1)
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- Feedforward neural network (1)
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Articles 1 - 6 of 6
Full-Text Articles in Physical Sciences and Mathematics
Rationality And Efficient Verifiable Computation, Matteo Campanelli
Rationality And Efficient Verifiable Computation, Matteo Campanelli
Dissertations, Theses, and Capstone Projects
In this thesis, we study protocols for delegating computation in a model where one of the parties is rational. In our model, a delegator outsources the computation of a function f on input x to a worker, who receives a (possibly monetary) reward. Our goal is to design very efficient delegation schemes where a worker is economically incentivized to provide the correct result f(x). In this work we strive for not relying on cryptographic assumptions, in particular our results do not require the existence of one-way functions.
We provide several results within the framework of rational proofs introduced by Azar …
List, Sample, And Count, Ali Assarpour
List, Sample, And Count, Ali Assarpour
Dissertations, Theses, and Capstone Projects
Counting plays a fundamental role in many scientific fields including chemistry, physics, mathematics, and computer science. There are two approaches for counting, the first relies on analytical tools to drive closed form expression, while the second takes advantage of the combinatorial nature of the problem to construct an algorithm whose output is the number of structures. There are many algorithmic techniques for counting, they cover the explicit approach of counting by listing to the approximate approach of counting by sampling.
This thesis looks at counting three sets of objects. First, we consider a subclass of boolean functions that are monotone. …
Multiple Sclerosis Identification Based On Fractional Fourier Entropy And A Modified Jaya Algorithm, Shui-Hua Wang, Hong Cheng, Preetha Phillips, Yu-Dong Zhang
Multiple Sclerosis Identification Based On Fractional Fourier Entropy And A Modified Jaya Algorithm, Shui-Hua Wang, Hong Cheng, Preetha Phillips, Yu-Dong Zhang
Publications and Research
Aim: Currently, identifying multiple sclerosis (MS) by human experts may come across the problem of “normal-appearing white matter”, which causes a low sensitivity. Methods: In this study, we presented a computer vision based approached to identify MS in an automatic way. This proposed method first extracted the fractional Fourier entropy map from a specified brain image. Afterwards, it sent the features to a multilayer perceptron trained by a proposed improved parameter-free Jaya algorithm. We used cost-sensitivity learning to handle the imbalanced data problem. Results: The 10 × 10-fold cross validation showed our method yielded a sensitivity of 97.40 ± 0.60%, …
Relating Justification Logic Modality And Type Theory In Curry–Howard Fashion, Konstantinos Pouliasis
Relating Justification Logic Modality And Type Theory In Curry–Howard Fashion, Konstantinos Pouliasis
Dissertations, Theses, and Capstone Projects
This dissertation is a work in the intersection of Justification Logic and Curry--Howard Isomorphism. Justification logic is an umbrella of modal logics of knowledge with explicit evidence. Justification logics have been used to tackle traditional problems in proof theory (in relation to Godel's provability) and philosophy (Gettier examples, Russel's barn paradox). The Curry--Howard Isomorphism or proofs-as-programs is an understanding of logic that places logical studies in conjunction with type theory and -- in current developments -- category theory. The point being that understanding a system as a logic, a typed calculus and, a language of a class of categories constitutes …
Cryptosystems Using Subgroup Distortion, Indira Chatterji, Delaram Kahrobaei, Ni Yen Lu
Cryptosystems Using Subgroup Distortion, Indira Chatterji, Delaram Kahrobaei, Ni Yen Lu
Publications and Research
In this paper we propose cryptosystems based on subgroup distortion in hyperbolic groups. We also include concrete examples of hyperbolic groups as possible platforms.
Gradient Estimation For Attractor Networks, Thomas Flynn
Gradient Estimation For Attractor Networks, Thomas Flynn
Dissertations, Theses, and Capstone Projects
It has been hypothesized that neural network models with cyclic connectivity may be more powerful than their feed-forward counterparts. This thesis investigates this hypothesis in several ways. We study the gradient estimation and optimization procedures for several variants of these networks. We show how the convergence of the gradient estimation procedures are related to the properties of the networks. Then we consider how to tune the relative rates of gradient estimation and parameter adaptation to ensure successful optimization in these models. We also derive new gradient estimators for stochastic models. First, we port the forward sensitivity analysis method to the …