Improving our fitnesse: From concrete executions to partial specification

Fitnesse and FIT [5] allow systems tests to be written by non-programmers using a Wiki or HTML style of input. However, there is little support for syntactic and semantic checks as the tests are being designed. This paper describes a support tool for designing table-based test cases that gives deep semantic analysis about a set of test cases. It uses a variety of strategies such as pairwise analysis, boundary value analysis and test case subsumption to suggest missing test cases and to generalise concrete tests into more abstract tests. The goal is to interactively improve the quality of test suites during the test design phase

AR2, a novel automatic muscle artifact reduction software method for ictal EEG interpretation: Validation and comparison of performance with commercially available software.

Objective: To develop a novel software method (AR2) for reducing muscle contamination of ictal scalp electroencephalogram (EEG), and validate this method on the basis of its performance in comparison to a commercially available software method (AR1) to accurately depict seizure-onset location. Methods: A blinded investigation used 23 EEG recordings of seizures from 8 patients. Each recording was uninterpretable with digital filtering because of muscle artifact and processed using AR1 and AR2 and reviewed by 26 EEG specialists. EEG readers assessed seizure-onset time, lateralization, and region, and specified confidence for each determination. The two methods were validated on the basis of the number of readers able to render assignments, confidence, the intra-class correlation (ICC), and agreement with other clinical findings. Results: Among the 23 seizures, two-thirds of the readers were able to delineate seizure-onset time in 10 of 23 using AR1, and 15 of 23 using AR2 (

Generating High-Quality Surface Realizations Using Data Augmentation and Factored Sequence Models

This work presents a new state of the art in reconstruction of surface realizations from obfuscated text. We identify the lack of sufficient training data as the major obstacle to training high-performing models, and solve this issue by generating large amounts of synthetic training data. We also propose preprocessing techniques which make the structure contained in the input features more accessible to sequence models. Our models were ranked first on all evaluation metrics in the English portion of the 2018 Surface Realization shared task

Oriented tensor reconstruction: tracing neural pathways from diffusion tensor MRI

In this paper we develop a new technique for tracing anatomical fibers from 3D tensor fields. The technique extracts salient tensor features using a local regularization technique that allows the algorithm to cross noisy regions and bridge gaps in the data. We applied the method to human brain DT-MRI data and recovered identifiable anatomical structures that correspond to the white matter brain-fiber pathways. The images in this paper are derived from a dataset having 121x88x60 resolution. We were able to recover fibers with less than the voxel size resolution by applying the regularization technique, i.e., using a priori assumptions about fiber smoothness. The regularization procedure is done through a moving least squares filter directly incorporated in the tracing algorithm

Terminology server for improved resource discovery: analysis of model and functions

This paper considers the potential to improve distributed information retrieval via a terminologies server. The restriction upon effective resource discovery caused by the use of disparate terminologies across services and collections is outlined, before considering a DDC spine based approach involving inter-scheme mapping as a possible solution. The developing HILT model is discussed alongside other existing models and alternative approaches to solving the terminologies problem. Results from the current HILT pilot are presented to illustrate functionality and suggestions are made for further research and development

Filtered ends of infinite covers and groups

Let f:A-->B be a covering map. We say A has e filtered ends with respect to f (or B) if for some filtration {K_n} of B by compact subsets, A - f^{-1}(K_n) "eventually" has e components. The main theorem states that if Y is a (suitable) free H-space, if K < H has infinite index, and if Y has a positive finite number of filtered ends with respect to H\Y, then Y has one filtered end with respect to K\Y. This implies that if G is a finitely generated group and K < H < G are subgroups each having infinite index in the next, then 0 < {\tilde e}(G)(H) < \infty implies {\tilde e}(G)(K) = 1, where {\tilde e}(.)(.) is the number of filtered ends of a pair of groups in the sense of Kropholler and Roller.Comment: 6 pages, to appear in Journal of Pure and Applied Algebr

Charles M. Breder, Jr.: Hypothetical considerations, 1931-1937

Charles M. Breder Jr. “hypothesis” diary is a deviation from the field diaries that form part of the Breder collection housed at the Arthur Vining Davis Library, Mote Marine Laboratory. There are no notes or observations from specific scientific expeditions in the document. Instead, the contents provide an insight into the early meticulous scientific thoughts of this biologist, and how he examines and develops these ideas. It is apparent that among Dr. Breder’s passions was his continual search for knowledge about questions that still besieged many scientists. Topics discussed include symmetry, origin of the atmosphere, origin of life, mechanical analogies of organisms, aquaria as an organism, astrobiology, entropy, evolution of species, and other topics. The diary was transcribed as part of the Coastal Estuarine Data/Document Rescue and Archeology effort for South Florida. (PDF contains 33 pages

AI Dining Suggestion App

Trying to decide what to eat can sometimes be challenging and time-consuming for people. Google and Yelp have large scale data sets of restaurant information as well as Application Program Interfaces (APIs) for using them. This restaurant data includes time, price range, traffic, temperature, etc. The goal of this project is to build an app that eases the process of finding a restaurant to eat. This app has a Tinder-like user friendly User Interface (UI) design to change the common way that lists of restaurants are presented to users on mobile apps. It also uses the help of Artificial Intelligence (AI) with neural networks to train both supervised and unsupervised learning models that can learn from one\u27s dining pattern over time to make better suggestions at any time