Learning RGB-D Salient Object Detection using background enclosure, depth contrast, and top-down features

Recently, deep Convolutional Neural Networks (CNN) have demonstrated strong performance on RGB salient object detection. Although, depth information can help improve detection results, the exploration of CNNs for RGB-D salient object detection remains limited. Here we propose a novel deep CNN architecture for RGB-D salient object detection that exploits high-level, mid-level, and low level features. Further, we present novel depth features that capture the ideas of background enclosure and depth contrast that are suitable for a learned approach. We show improved results compared to state-of-the-art RGB-D salient object detection methods. We also show that the low-level and mid-level depth features both contribute to improvements in the results. Especially, F-Score of our method is 0.848 on RGBD1000 dataset, which is 10.7% better than the second place

nbodykit: an open-source, massively parallel toolkit for large-scale structure

We present nbodykit, an open-source, massively parallel Python toolkit for analyzing large-scale structure (LSS) data. Using Python bindings of the Message Passing Interface (MPI), we provide parallel implementations of many commonly used algorithms in LSS. nbodykit is both an interactive and scalable piece of scientific software, performing well in a supercomputing environment while still taking advantage of the interactive tools provided by the Python ecosystem. Existing functionality includes estimators of the power spectrum, 2 and 3-point correlation functions, a Friends-of-Friends grouping algorithm, mock catalog creation via the halo occupation distribution technique, and approximate N-body simulations via the FastPM scheme. The package also provides a set of distributed data containers, insulated from the algorithms themselves, that enable nbodykit to provide a unified treatment of both simulation and observational data sets. nbodykit can be easily deployed in a high performance computing environment, overcoming some of the traditional difficulties of using Python on supercomputers. We provide performance benchmarks illustrating the scalability of the software. The modular, component-based approach of nbodykit allows researchers to easily build complex applications using its tools. The package is extensively documented at http://nbodykit.readthedocs.io, which also includes an interactive set of example recipes for new users to explore. As open-source software, we hope nbodykit provides a common framework for the community to use and develop in confronting the analysis challenges of future LSS surveys.Comment: 18 pages, 7 figures. Feedback very welcome. Code available at https://github.com/bccp/nbodykit and for documentation, see http://nbodykit.readthedocs.i

Early Verification of Legal Compliance via Bounded Satisfiability Checking

Legal properties involve reasoning about data values and time. Metric first-order temporal logic (MFOTL) provides a rich formalism for specifying legal properties. While MFOTL has been successfully used for verifying legal properties over operational systems via runtime monitoring, no solution exists for MFOTL-based verification in early-stage system development captured by requirements. Given a legal property and system requirements, both formalized in MFOTL, the compliance of the property can be verified on the requirements via satisfiability checking. In this paper, we propose a practical, sound, and complete (within a given bound) satisfiability checking approach for MFOTL. The approach, based on satisfiability modulo theories (SMT), employs a counterexample-guided strategy to incrementally search for a satisfying solution. We implemented our approach using the Z3 SMT solver and evaluated it on five case studies spanning the healthcare, business administration, banking and aviation domains. Our results indicate that our approach can efficiently determine whether legal properties of interest are met, or generate counterexamples that lead to compliance violations

The Impact of Processing Stages on Stability of Vitamin D3 fortified into Corn Flakes Using Nanoemulsions

Explore the potential for using soy-based nanoemulsions to fortify corn flakes with vitamin D3. More specifically, we propose to evaluate the impact of processing on stability of vitamin D3 at various stages of corn flake production process.Ope

Exploring Generative Adversarial Networks for Image-to-Image Translation in STEM Simulation

The use of accurate scanning transmission electron microscopy (STEM) image simulation methods require large computation times that can make their use infeasible for the simulation of many images. Other simulation methods based on linear imaging models, such as the convolution method, are much faster but are too inaccurate to be used in application. In this paper, we explore deep learning models that attempt to translate a STEM image produced by the convolution method to a prediction of the high accuracy multislice image. We then compare our results to those of regression methods. We find that using the deep learning model Generative Adversarial Network (GAN) provides us with the best results and performs at a similar accuracy level to previous regression models on the same dataset. Codes and data for this project can be found in this GitHub repository, https://github.com/uw-cmg/GAN-STEM-Conv2MultiSlice

Early mobilisation after hip fracture surgery is associated with improved patient outcomes:a systematic review and meta-analysis

Introduction:- The aims of this systematic review and meta-analysis were to determine if after hip fracture surgery 1) early mobilisation is associated with improved clinical outcomes, and if so 2) are benefits directly proportional to how soon after surgery the patient mobilisesMethods:- A PRISMA systematic review was conducted using four databases to identify all studies that compared postoperative early mobilisation with delayed mobilisation in patients after hip fracture surgery. The Critical Appraisal Skills Programme checklist was employed for critical appraisal and evaluation of all studies that met the inclusion criteria. Results:- A total of thirteen studies including 297,435 patients were identified, of which 235,275 patients were mobilised early and 62,160 were mobilised late. Six studies assessed 30- day mortality, of which two also investigated 30-day complication rates. Pooled meta-analysis demonstrated that there were significantly lower 30-day mortality rates (OR 0.35, 95% CI 0.31 - 0.41, p<0.001) and complication rates (OR 0.43, 95% CI 0.36 - 0.51, p<0.001) in patients mobilising early after hip fracture surgery. Five studies investigated length of stay and metaanalysis revealed no difference between groups (mean difference -0.57 days, 95%CI -1.89 - 0.74, p=0.39). Conclusion:- Early mobilisation in hip fracture patients is associated with a reduction in 30-day mortality and complication rates compared to delayed mobilisation, but no difference in length of stay. These findings illustrate that early mobilisation is associated with superior post operative outcomes. However, a direct casual effect remains to be demonstrated, and further work on the factors underlying delayed mobilisation is required

Examining the Impact of Provenance-Enabled Media on Trust and Accuracy Perceptions

In recent years, industry leaders and researchers have proposed to use technical provenance standards to address visual misinformation spread through digitally altered media. By adding immutable and secure provenance information such as authorship and edit date to media metadata, social media users could potentially better assess the validity of the media they encounter. However, it is unclear how end users would respond to provenance information, or how to best design provenance indicators to be understandable to laypeople. We conducted an online experiment with 595 participants from the US and UK to investigate how provenance information altered users' accuracy perceptions and trust in visual content shared on social media. We found that provenance information often lowered trust and caused users to doubt deceptive media, particularly when it revealed that the media was composited. We additionally tested conditions where the provenance information itself was shown to be incomplete or invalid, and found that these states have a significant impact on participants' accuracy perceptions and trust in media, leading them, in some cases, to disbelieve honest media. Our findings show that provenance, although enlightening, is still not a concept well-understood by users, who confuse media credibility with the orthogonal (albeit related) concept of provenance credibility. We discuss how design choices may contribute to provenance (mis)understanding, and conclude with implications for usable provenance systems, including clearer interfaces and user education.Comment: Accepted to CSCW 202

Poleward migration of western North Pacific tropical cyclones related to changes in cyclone seasonality

The average location of observed western North Pacific (WNP) tropical cyclones (TCs) has shifted north over the last several decades, but the cause remains not fully understood. Here we show that, for the annual average, the observed northward migration of WNP TCs is related to changes in TC seasonality, not to a northward migration in all seasons. Normally, peak-season (July–September) TCs form and travel further north than late-season (October–December) TCs. In recent decades, related to less frequent late-season TCs, seasonally higher-latitude TCs contribute relatively more to the annual-average location and seasonally lower-latitude TCs contribute less. We show that the change in TC seasonality is related to the different responses of late-season and peak-season TC occurrence to a stronger Pacific Walker Circulation. Our findings provide a perspective on long-term trends in TC activity, by decomposing the annual-average statistics into seasonal components, which could respond differently to anthropogenic forcing

Seasonal and interannual variations of upper ocean heat balance off the west coast of Australia

The Leeuwin Current, a warm, poleward flowing eastern boundary current, dominates the surface circulation off the west coast of Australia and has profound influence on regional marine ecosystem and fisheries recruitment. In this study, the seasonal and interannual variations of upper ocean heat balance in the Leeuwin Current region are analyzed by using an eddy-resolving numerical model simulation, as a first step to quantify the climate impacts on regional ocean thermodynamics and marine ecosystem. The volume transport and heat advection of the Leeuwin Current are stronger during the austral winter on the seasonal cycle and are stronger during a La Nina event on the interannual scale. On both seasonal and interannual timescales, the mixed layer heat budget off the west coast of Australia is predominantly balanced between the variations of the Leeuwin Current heat advection and heat flux across the air-sea interface. On the interannual timescale, the variation of the Leeuwin Current heat advection tends to lead that of the air-sea (latent) heat flux by two months, which is likely a reflection of advection timescales of the Leeuwin Current and its eddy field. The interannual variation of the average February–April sea surface temperature off the west coast of Australia, which is crucial for the larval settlement of western rock lobster, is mostly influenced by the Leeuwin Current heat advection as well as the ocean memory from the previous austral winter, with the air-sea heat exchange playing a buffering role

Evaluating source-sink relationships of the western rock lobster fishery using oceanographic modelling: Final FRDC Report – Project 2008/087

Objectives 1. To determine the relative contribution of larval production from different areas to the abundance and spatial distribution of puerulus settlement over 15 years using a larval advection mode