Legal information retrieval for understanding statutory terms

In this work we study, design, and evaluate computational methods to support interpretation of statutory terms. We propose a novel task of discovering sentences for argumentation about the meaning of statutory terms. The task models the analysis of past treatment of statutory terms, an exercise lawyers routinely perform using a combination of manual and computational approaches. We treat the discovery of sentences as a special case of ad hoc document retrieval. The specifics include retrieval of short texts (sentences), specialized document types (legal case texts), and, above all, the unique definition of document relevance provided in detailed annotation guidelines. To support our experiments we assembled a data set comprising 42 queries (26,959 sentences) which we plan to release to the public in the near future in order to support further research. Most importantly, we investigate the feasibility of developing a system that responds to a query with a list of sentences that mention the term in a way that is useful for understanding and elaborating its meaning. This is accomplished by a systematic assessment of different features that model the sentences’ usefulness for interpretation. We combine features into a compound measure that accounts for multiple aspects. The definition of the task, the assembly of the data set, and the detailed task analysis provide a solid foundation for employing a learning-to-rank approach.

     

Amplification‐Free Multi‐RNA‐Type Profiling for Cancer Risk Stratification via Alternating Current Electrohydrodynamic Nanomixing

Simultaneous analysis of messenger RNA (mRNA), microRNA (miRNA), and long noncoding RNA (lncRNA)—multi‐RNA‐type profiling—is increasingly crucial in cancer diagnostics. Yet, rapid multi‐RNA‐type profiling is challenging due to enzymatic amplification reliance and RNA‐type‐dependent characteristics. Here, a nanodevice is reported to uniquely use alterable alternating current electrohydrodynamic (ac‐EHD) forces to enhance probe–target hybridization prior to direct native RNA target detection, without target amplification or surface functionalization. To exemplify clinical applicability, noninvasive screening of next‐generation prostate cancer (PCa) RNA biomarkers (of different types) in patient urine samples is performed. A strong correlation between multi‐RNA‐type expression and aggressive PCa is found, and the nanodevice performance is statistically evaluated. It is believed that this miniaturized system exhibits great potential for cancer risk stratification via multi‐RNA‐type profiling.     

Developments in quantitative dimensional synthesis (1970-present): four-bar motion generation

Dimensional synthesis is a type of inverse problem in linkage kinematics where the objective is to calculate the linkage dimensions required to achieve prescribed linkage output motion. Motion generation is a particular category of dimensional synthesis where the objective is to calculate the linkage dimensions required to achieve a group of prescribed link positions. In motion generation for a four-bar linkage, positions are prescribed for the coupler link. While early motion generation methods were primarily qualitative, ongoing advancements in computing hardware and software continue to make quantitative motion generation more practical. By providing overviews of works representative of developments in quantitative four-bar motion generation since 1970, this work is essentially an overview that spans over 40 years of developments in quantitative four-bar motion generation.     

Adaptive autonomous underwater vehicles for littoral surveillance

Autonomous underwater vehicles (AUVs) have gained more interest in recent years for military as well as civilian applications. One potential application of AUVs is for the purpose of undersea surveillance. As research into undersea surveillance using AUVs progresses, issues arise as to how an AUV acquires, acts on, and shares information about the undersea battle space. These issues naturally touch on aspects of vehicle autonomy and underwater communications, and need to be resolved through a spiral development process that includes at sea experimentation. This paper presents a recent AUV implementation for active anti-submarine warfare tested at sea in the summer of 2010. On-board signal processing capabilities and an adaptive behavior are discussed in both a simulation and experimental context. The implications for underwater surveillance using AUVs are discussed.     

A comparative evaluation of interactive segmentation algorithms

In this paper we present a comparative evaluation of four popular interactive segmentation algorithms. The evaluation was carried out as a series of user-experiments, in which participants were tasked with extracting 100 objects from a common dataset: 25 with each algorithm, constrained within a time limit of 2 min for each object. To facilitate the experiments, a “scribble-driven” segmentation tool was developed to enable interactive image segmentation by simply marking areas of foreground and background with the mouse. As the participants refined and improved their respective segmentations, the corresponding updated segmentation mask was stored along with the elapsed time. We then collected and evaluated each recorded mask against a manually segmented ground truth, thus allowing us to gauge segmentation accuracy over time. Two benchmarks were used for the evaluation: the well-known Jaccard index for measuring object accuracy, and a new fuzzy metric, proposed in this paper, designed for measuring boundary accuracy. Analysis of the experimental results demonstrates the effectiveness of the suggested measures and provides valuable insights into the performance and characteristics of the evaluated algorithms.     

Flow map layout via spiral trees

Flow maps are thematic maps that visualize the movement of objects, such as people or goods, between geographic regions. One or more sources are connected to several targets by lines whose thickness corresponds to the amount of flow between a source and a target. Good flow maps reduce visual clutter by merging (bundling) lines smoothly and by avoiding self-intersections. Most flow maps are still drawn by hand and only few automated methods exist. Some of the known algorithms do not support edge-bundling and those that do, cannot guarantee crossing-free flows. We present a new algorithmic method that uses edge-bundling and computes crossing-free flows of high visual quality. Our method is based on so-called spiral trees, a novel type of Steiner tree which uses logarithmic spirals. Spiral trees naturally induce a clustering on the targets and smoothly bundle lines. Our flows can also avoid obstacles, such as map features, region outlines, or even the targets. We demonstrate our approach with extensive experiments.     

Marking student programs using graph similarity

We present a novel approach to the automated marking of student programming assignments. Our technique quantifies the structural similarity between unmarked student submissions and marked solutions, and is the basis by which we assign marks. This is accomplished through an efficient novel graph similarity measure (AssignSim). Our experiments show good correlation of assigned marks with that of a human marker.