There is a significant medical need for tough biodegradable polymer adhesives that can adapt to or recover from various mechanical deformations while remaining strongly attached to the underlying tissue. We approached this problem by using a polymer poly(glycerol-co-sebacate acrylate) and modifying the surface to mimic the nanotopography of gecko feet, which allows attachment to vertical surfaces. Translation of existing gecko-inspired adhesives for medical applications is complex, as multiple parameters must be optimized, including: biocompatibility, biodegradation, strong adhesive tissue bonding, as well as compliance and conformability to tissue surfaces. Ideally these adhesives would also have the ability to deliver drugs or growth factors to promote healing. As a first demonstration, we have created a gecko-inspired tissue adhesive from a biocompatible and biodegradable elastomer combined with a thin tissue-reactive biocompatible surface coating. Tissue adhesion was optimized by varying dimensions of the nanoscale pillars, including the ratio of tip diameter to pitch and the ratio of tip diameter to base diameter. Coating these nanomolded pillars of biodegradable elastomers with a thin layer of oxidized dextran significantly increased the interfacial adhesion strength on porcine intestine tissue in vitro and in the rat abdominal subfascial in vivo environment. This gecko-inspired medical adhesive may have potential applications for sealing wounds and for replacement or augmentation of sutures or staples.chemical cross-link ͉ medical adhesive ͉ nanotopography ͉ surgical suture

The stated goal for visual data exploration is to operate at a rate that matches the pace of human data analysts, but the ever increasing amount of data has led to a fundamental problem: datasets are often too large to process within interactive time frames. Progressive analytics and visualizations have been proposed as potential solutions to this issue. By processing data incrementally in small chunks, progressive systems provide approximate query answers at interactive speeds that are then refined over time with increasing precision. We study how progressive visualizations affect users in exploratory settings in an experiment where we capture user behavior and knowledge discovery through interaction logs and think-aloud protocols. Our experiment includes three visualization conditions and different simulated dataset sizes. The visualization conditions are: (1) blocking, where results are displayed only after the entire dataset has been processed; (2) instantaneous, a hypothetical condition where results are shown almost immediately; and (3) progressive, where approximate results are displayed quickly and then refined over time. We analyze the data collected in our experiment and observe that users perform equally well with either instantaneous or progressive visualizations in key metrics, such as insight discovery rates and dataset coverage, while blocking visualizations have detrimental effects.

Data analytics has recently grown to include increasingly sophisticated techniques, such as machine learning and advanced statistics. Users frequently express these complex analytics tasks as workflows of user-defined functions (UDFs) that specify each algorithmic step. However, given typical hardware configurations and dataset sizes, the core challenge of complex analytics is no longer sheer data volume but rather the computation itself, and the next generation of analytics frameworks must focus on optimizing for this computation bottleneck. While query compilation has gained widespread popularity as a way to tackle the computation bottleneck for traditional SQL workloads, relatively little work addresses UDF-centric workflows in the domain of complex analytics. In this paper, we describe a novel architecture for automatically compiling workflows of UDFs. We also propose several optimizations that consider properties of the data, UDFs, and hardware together in order to generate different code on a case-by-case basis. To evaluate our approach, we implemented these techniques in TUPLEWARE, a new high-performance distributed analytics system, and our benchmarks show performance improvements of up to three orders of magnitude compared to alternative systems.

Visual data exploration tools allow users to quickly gather insights from new datasets. As dataset sizes continue to increase, though, new techniques will be necessary to maintain the interactivity guarantees that these tools require. Approximate query processing (AQP) attempts to tackle this problem and allows systems to return query results at "human speed." However, existing AQP techniques start to break down when confronted with ad hoc queries that target the tails of the distribution. We therefore present an AQP formulation that can provide low-error approximate results at interactive speeds, even for queries over rare subpopulations. In particular, our formulation treats query results as random variables in order to leverage the ample opportunities for result reuse inherent in interactive data exploration. As part of our approach, we apply a variety of optimization techniques that are based on probability theory, including new query rewrite rules and index structures. We implemented these techniques in a prototype system and show that they can achieve interactivity where alternative approaches cannot.