The ISO/IEC 8802-11:1999(E) specification uses a 32-bit CRC for error detection and whole-packet retransmissions for recovery. In long-distance orhigh-interference links where the probability of a bit error is high,this strategy results in excessive losses, because any erroneous bitcauses an entire packet to be discarded. By ignoring the CRC andadding redundancy to 802.11 payloads in software, we achievedsubstantially reduced loss rates on indoor and outdoor long-distancelinks and extended line-of-sight range outdoors by 70 percent.

Given a set of images containing multiple object categories,we seek to discover those categories and their image locations withoutsupervision. We achieve this using generative modelsfrom the statistical text literature: probabilistic Latent SemanticAnalysis (pLSA), and Latent Dirichlet Allocation (LDA). In text analysisthese are used to discover topics in a corpus using the bag-of-wordsdocument representation. Here we discover topics as object categories, sothat an image containing instances of several categories is modelled as amixture of topics.The models are applied to images by using avisual analogue of a word, formed by vector quantizing SIFT like regiondescriptors. We investigate a set of increasingly demanding scenarios,starting with image sets containing only two object categories through tosets containing multiple categories (including airplanes, cars, faces,motorbikes, spotted cats) and background clutter. The object categoriessample both intra-class and scale variation, and both the categories andtheir approximate spatial layout are found without supervision.We also demonstrate classification of unseen images and images containingmultiple objects. Performance of the proposed unsupervised method is compared tothe semi-supervised approach of Fergus et al.

We describe a system in which simple, identical, autonomous robots assemble two-dimensional structures out of identical building blocks. We show that, in a system divided in this way into mobile units and structural units, giving the blocks limited communication abilities enables robots to have sufficient global structural knowledge to rapidly build elaborate pre-designed structures. In this way we extend the principle of stigmergy (storing information in the environment) used by social insects, by increasing the capabilities of the blocks that represent that environmental information. As a result, arbitrary solid structures can be built using a few fixed, local behaviors, without requiring construction to be planned out in detail.

Partially observable Markov decision processes (POMDPs) are a well studied paradigm for programming autonomous robots, where the robot sequentially chooses actions to achieve long term goals efficiently. Unfortunately, for real world robots and other similar domains, the uncertain outcomes of the actions and the fact that the true world state may not be completely observable make learning of models of the world extremely difficult, and using them algorithmically infeasible. In this paper we show that learning POMDP models and planning with them can become significantly easier when we incorporate into our algorithms the notions of spatial and tempral abstraction. We demonstrate the superiority of our algorithms by comparing them with previous flat approaches for large scale robot navigation.

This paper introduces algorithms for learning how to trade usinginsider (superior) information in Kyle's model of financial markets.Prior results in finance theory relied on the insider having perfectknowledge of the structure and parameters of the market. I show herethat it is possible to learn the equilibrium trading strategy whenits form is known even without knowledge of the parameters governingtrading in the model. However, the rate of convergence toequilibrium is slow, and an approximate algorithm that does notconverge to the equilibrium strategy achieves better utility whenthe horizon is limited. I analyze this approximate algorithm fromthe perspective of reinforcement learning and discuss the importanceof domain knowledge in designing a successful learning algorithm.

The purpose of this study was to investigate athletic participation and the development of disordered eating behaviors in women by comparing collegiate varsity athletes and the general university populations on three variables: disordered eating behaviors, body satisfaction, and self-esteem. Possible differences between athletes who participate in sports in which a lean figure is conducive to success and athletes who participate in sports in which a lean figure is not conducive to success were also investigated. As disordered eating includes many types of behavior, a disordered eating continuum was used to illustrate. Three existing inventories used to measure eating behaviors, body satisfaction, and self-esteem, respectively, were distributed to college women at a Division I university in the southeastern United States.

In this report, a face recognition system that is capable of detecting and recognizing frontal and rotated faces was developed. Two face recognition methods focusing on the aspect of pose invariance are presented and evaluated - the whole face approach and the component-based approach. The main challenge of this project is to develop a system that is able to identify faces under different viewing angles in realtime. The development of such a system will enhance the capability and robustness of current face recognition technology. The whole-face approach recognizes faces by classifying a single feature vector consisting of the gray values of the whole face image. The component-based approach first locates the facial components and extracts them. These components are normalized and combined into a single feature vector for classification. The Support Vector Machine (SVM) is used as the classifier for both approaches. Extensive tests with respect to the robustness against pose changes are performed on a database that includes faces rotated up to about 40 degrees in depth. The component-based approach clearly outperforms the whole-face approach on all tests. Although this approach isproven to be more reliable, it is still too slow for real-time applications. That is the reason why a real-time face recognition system using the whole-face approach is implemented to recognize people in color video sequences.

No description available.

Hannover, Universiẗat, Diss., 1999.

In this dissertation, I investigate the existing smart contract problems that limit cognitive abilities. I use Taylor's serious expansion, polynomial equation, and fraction-based computations to overcome the limitations of calculations in smart contracts. To prove the hypothesis, I use these mathematical models to compute complex operations of naive Bayes, linear regression, decision trees, and neural network algorithms on Ethereum public test networks. The smart contracts achieve 95\% prediction accuracy compared to traditional programming language models, proving the soundness of the numerical derivations. Many non-real-time applications can use our solution for trusted and secure prediction services.