1,720,957 research outputs found
Learning-Based Communication Systems
No full textConnecting people offers opportunities to build communities of any size and consequently, brings the world closer together. Conventionally, the connectivity has happened through traditional radio-frequency communication methods. However, the ever-increasing demand for higher data-rate communications and the explosion of advanced wireless applications such as virtual reality, augmented reality, and internet of things, reduce the effectiveness of these method. Therefore, developing next-generation technologies, such as learning-based communication systems, that can satisfy the large data and ultra-high rate communication requirements would be of interest. To address the challenging problem of connectivity, our research focuses on developing a learning-based framework for the next-generation communication systems. These systems can proactively adapt their communication and networking strategies to the dynamic of the environment, thereby maximizing their end-to-end performance in terms of data-rates, energy-efficiency, and link-reliability. Toward this goal, first information-theoretical tools are used to establish the fundamental limits (including bounds on the end-to-end performance). These performance limits are the keys for building reliable and efficient systems. Then, powerful machine learning techniques, such as deep learning, are employed for the implementation of such systems. In particular, a simple and cost-effective system with near-optimal performance can be implemented by merely taking off-the-shelf deep learning models, applying them to communication design problems, and tuning them based on the training data.masters, M.S., Electrical and Computer Engineering -- University of Idaho - College of Graduate Studies, 2019-0
Recommended from our members
Formal Verification of AI-Controlled Cyber-Physical Systems Using Polynomial Approximations: Constraints Solver, Model Checkers, and Applications
Get PDFThe last decade's advancement in machine learning (ML) for controlling cyber-physical systems has heralded a new era in autonomous technology, driving innovations from self-driving cars to smart infrastructure. However, these systems often grapple with challenges related to safety, reliability, and the ability to generalize across different scenarios. This dissertation aims to bridge the gap between the scalability and flexibility of ML-based control systems and the rigorous safety and reliability guarantees provided by formal methods and control theory. It introduces novel methodologies that leverage machine learning to enhance the design, verification, and optimization of AI-controlled cyber-physical systems, ensuring they meet high-level specifications while managing such systems' inherent complexity and non-linearity. The contributions of this thesis are multi-fold. 1) We proposed a highly efficient and parallelizable solver called PolyAR, which aims to solve general multivariate polynomial inequality constraints. PolyAR uses convex polynomials as an abstraction for highly nonlinear polynomials. Such abstractions were previously shown to be powerful to prune the search space and restrict the usage of sound and complete solvers to small search space. We compared the scalability of PolyAR against state-of-the-art solvers such as Z3 8.9 and Yices 2.6 on complex design and verification problems. The experiment results show that the PolyAR solver drastically outperformed Z3 8.9 and Yices 2.6 regarding execution time. 2) We developed PolyARBerNN, an enhancement to PolyAR that employs neural networks (NN) to guide the abstraction refinement procedure that helps to select the right abstraction out of a set of pre-defined abstractions and a Bernstein polynomial-based search space pruning mechanism. These enhancements together made PolyARBerNN capable of solving complex instances and scaling more favorably compared to the state-of-the-art nonlinear real arithmetic solvers while maintaining the soundness and completeness of the resulting solver. In addition, we proposed an efficient optimizer called PolyAROpt that transforms polynomial objective functions into polynomial constraints (on the gradient of the objective function) whose solutions are guaranteed to be close to the global optima. PolyAROpt optimizer uses PolyARBerNN to solve constrained polynomial optimization problems. Numerical results show that PolyAROpt can solve high-dimensional and high-order polynomial optimization problems faster than the built-in optimizer in the Z3 8.9 solver. 3) We proposed an efficient algorithm called BERN-NN that employs polynomial interval arithmetic, where tight over/under approximations of the NN's activation functions are computed using Bernstein polynomials. These polynomials have several interesting mathematical proprieties. One particular property is called the sharpness propriety, which allows us to obtain extremely tight bounds that are tighter than those currently exist in the literature (e.g., interval arithmetic, crowns, linear programming, and many centered forms). Moreover, we exploited the mathematical proprieties of Bernstein polynomials to convert the proposed polynomial interval arithmetic operations into add-and-multiply operations, which are easily implemented using GPUs. Thanks to those GPUs, our tool's execution time is drastically reduced. Experimental results show that our method approximates NN's outputs tighter than state-of-the-art NN verification tools by several orders of magnitude. 4) We proposed BERN-NN-IBF, a significant enhancement of the Bernstein-polynomial-based bound propagation algorithms. BERN-NN-IBF offers three main contributions: (i) a memory-efficient encoding of Bernstein-polynomials to scale the bound propagation algorithms, (ii) optimized tensor operations for the new polynomial encoding to maintain the integrity of the bounds while enhancing computational efficiency, and (iii) tighter under-approximations of the ReLU activation function using quadratic polynomials tailored to minimize approximation errors. Through comprehensive testing, we demonstrate that BERN-NN-IBF achieves tighter bounds and higher computational efficiency than the original BERN-NN and state-of-the-art methods, including linear programming and convex used within the winner of the VNN-COMPETITION
Formal Verification of AI-Controlled Cyber-Physical Systems Using Polynomial Approximations: Constraints Solver, Model Checkers, and Applications
No full textFormal Verification of AI-Controlled Cyber-Physical Systems Using Polynomial Approximations: Constraints Solver, Model Checkers, and Applications
No full text
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
- …
