Natural Language Processing for Foreign Language Learning

This research presents novel algorithms which generate sentences in a natural language, using natural language generation techniques. The purpose of the algorithms is to benefit foreign language learning. As far as we can tell, ours is the first such research being done in the field. In creating the algorithms, we also developed a piece of software to showcase the work and allow testing by users. The main algorithm begins by generating sentence models by using one of two methods, namely modeled sentence generation and semantic sentence generation. Each of these have benefits and drawbacks, which the user must take into consideration when generating sentences. When the models are generated, they are filled in word by word using a conjugation algorithm. The completed sentences are then returned to the user and may then be translated. There is still much work to do before we will be satisfied with the algorithms, but our research shows that it is possible to use natural language generation techniques to benefit foreign language learning

Tracking-by-Assignment as a Probabilistic Graphical Model with Applications in Developmental Biology

This thesis presents a novel approach for tracking a varying number of divisible objects with similar appearance in the presence of a non-negligible number of false positive detections (more than 10%). It is applied to the reconstruction of cell lineages in developing zebrafish and fruit fly embryos from 3d time-lapse record- ings. The model takes the form of a chain graph—a mixed directed-undirected probabilistic graphical model—and a tracking is obtained simultaneously over all time slices from the maximum a-posteriori configuration. The tracking model is used as the second step in a two-step pipeline to produce digital embryos—maps of cell nuclei in an embryo and their ancestral fate; the first step being the segmentation of the fluorescently-stained cell nuclei in light sheet microscopy images. The pipeline is implemented as a software with an intuitive graphical user interface. It is the first freely available program of its kind and makes the presented methods accessible to a broad audience of users from the life sciences

Evaluation of String Constraint Solvers Using Dynamic Symbolic Execution

Symbolic execution is a path sensitive program analysis technique used for error detection and test case generation. Symbolic execution tools rely on constraint solvers to determine the feasibility of program paths and generate concrete inputs for feasible paths. Therefore, the effectiveness of such tools depends on their constraint solvers. Most modern constraint solvers for primitive data types, such as integers, are both efficient and accurate. However, the research on constraint solvers for complex data types, such as strings, is ongoing and less converged. For example, there are several types of string constraint solvers provided by researchers. However, a potential user of a string constraint solver likely has no comprehensive means to identify which solver would work best for a particular problem. In order to help the user with selecting a solver, in addition to the commonly used performance criterion, we introduce two criteria: modeling cost and accuracy. Using these selection criteria, we evaluate four string constraint solvers in the context of symbolic execution. Our results show that, depending on the needs of the user, one solver might be more appropriate than another, yet no solver exhibits the best overall results. Hence, we suggest that the preferred approach to solving constraints for complex types is to execute all solvers in parallel and enable communication between solvers

Temporal and visual source memory deficits among ecstasy/polydrug users

Objectives: The present paper seeks to investigate whether source memory judgements are adversely affected by recreational illicit drug use. Method: Sixty-two ecstasy/polydrug users and 75 non ecstasy users completed a source memory task, in which they tried to determine whether or not a word had been previously presented and if so, attempted to recall the format, location and temporal position in which the word had occurred. Results: While not differing in terms of the number of hits and false positive responses, ecstasy/polydrug users adopted a more liberal decision criterion when judging if a word had been presented previously. With regard to source memory, users were less able to determine the format in which words had been presented (upper versus lower case). Female users did worse than female nonusers in determining which list (first or second) a word was from. Unexpectedly, the current frequency of cocaine use was negative associated with list and case source memory performance. Conclusions: Given the role that source memory plays in everyday cognition, those who use cocaine more frequently might have more difficulty in everyday tasks such as recalling the sources of crucial information or making use of contextual information as an aid to learning

What you know can influence what you are going to know (especially for older adults)

Stimuli related to an individual's knowledge/experience are often more memorable than abstract stimuli, particularly for older adults. This has been found when material that is congruent with knowledge is contrasted with material that is incongruent with knowledge, but there is little research on a possible graded effect of congruency. The present study manipulated the degree of congruency of study material with participants’ knowledge. Young and older participants associated two famous names to nonfamous faces, where the similarity between the nonfamous faces and the real famous individuals varied. These associations were incrementally easier to remember as the name-face combinations became more congruent with prior knowledge, demonstrating a graded congruency effect, as opposed to an effect based simply on the presence or absence of associations to prior knowledge. Older adults tended to show greater susceptibility to the effect than young adults, with a significant age difference for extreme stimuli, in line with previous literature showing that schematic support in memory tasks particularly benefits older adults

Automated processing of zebrafish imaging data: a survey

Due to the relative transparency of its embryos and larvae, the zebrafish is an ideal model organism for bioimaging approaches in vertebrates. Novel microscope technologies allow the imaging of developmental processes in unprecedented detail, and they enable the use of complex image-based read-outs for high-throughput/high-content screening. Such applications can easily generate Terabytes of image data, the handling and analysis of which becomes a major bottleneck in extracting the targeted information. Here, we describe the current state of the art in computational image analysis in the zebrafish system. We discuss the challenges encountered when handling high-content image data, especially with regard to data quality, annotation, and storage. We survey methods for preprocessing image data for further analysis, and describe selected examples of automated image analysis, including the tracking of cells during embryogenesis, heartbeat detection, identification of dead embryos, recognition of tissues and anatomical landmarks, and quantification of behavioral patterns of adult fish. We review recent examples for applications using such methods, such as the comprehensive analysis of cell lineages during early development, the generation of a three-dimensional brain atlas of zebrafish larvae, and high-throughput drug screens based on movement patterns. Finally, we identify future challenges for the zebrafish image analysis community, notably those concerning the compatibility of algorithms and data formats for the assembly of modular analysis pipelines

ilastik: interactive machine learning for (bio)image analysis

We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains pre-defined workflows for image segmentation, object classification, counting and tracking. Users adapt the workflows to the problem at hand by interactively providing sparse training annotations for a nonlinear classifier. ilastik can process data in up to five dimensions (3D, time and number of channels). Its computational back end runs operations on-demand wherever possible, allowing for interactive prediction on data larger than RAM. Once the classifiers are trained, ilastik workflows can be applied to new data from the command line without further user interaction. We describe all ilastik workflows in detail, including three case studies and a discussion on the expected performance