Nitrate sources and dynamics in a salinized river and estuary : a δ15N-NO₃⁻ and δ18O-NO₃⁻ isotope approach

To trace NO3- sources and assess NO3- dynamics in salinized rivers and estuaries, three rivers (Haihe River: HH River, Chaobaixin River: CB River and Jiyun River: JY River) and two estuaries (HH Estuary and CJ Estuary) along the Bohai Bay (China) have been selected to determine dissolved inorganic nitrogen (DIN: NH4+, NO2- and NO3-. Upstream of the HH River, NO3- was removed 30.9 +/- 22.1% by denitrification, resulting from effects of the floodgate: limiting water exchange with downstream and prolonging water residence time to remove NO3-. Downstream of the HH River NO3- was removed 2.5 +/- 13.3% by NO3- turnover processes. Conversely, NO3- was increased 36.6 +/- 25.2% by external N source addition in the CB River and 34.6 +/- 35.1% by instream nitrification in the JY River. The HH and CY Estuaries behaved mostly conservatively excluding the sewage input in the CJ Estuary. Hydrodynamics in estuaries has been changed by the ongoing reclamation projects, aggravating the loss of the attenuation function of NO3- in the estuary

Globally Optimal Cell Tracking using Integer Programming

We propose a novel approach to automatically tracking cell populations in time-lapse images. To account for cell occlusions and overlaps, we introduce a robust method that generates an over-complete set of competing detection hypotheses. We then perform detection and tracking simultaneously on these hypotheses by solving to optimality an integer program with only one type of flow variables. This eliminates the need for heuristics to handle missed detections due to occlusions and complex morphology. We demonstrate the effectiveness of our approach on a range of challenging sequences consisting of clumped cells and show that it outperforms state-of-the-art techniques.Comment: Engin T\"uretken and Xinchao Wang contributed equally to this wor

Every Smile is Unique: Landmark-Guided Diverse Smile Generation

Each smile is unique: one person surely smiles in different ways (e.g., closing/opening the eyes or mouth). Given one input image of a neutral face, can we generate multiple smile videos with distinctive characteristics? To tackle this one-to-many video generation problem, we propose a novel deep learning architecture named Conditional Multi-Mode Network (CMM-Net). To better encode the dynamics of facial expressions, CMM-Net explicitly exploits facial landmarks for generating smile sequences. Specifically, a variational auto-encoder is used to learn a facial landmark embedding. This single embedding is then exploited by a conditional recurrent network which generates a landmark embedding sequence conditioned on a specific expression (e.g., spontaneous smile). Next, the generated landmark embeddings are fed into a multi-mode recurrent landmark generator, producing a set of landmark sequences still associated to the given smile class but clearly distinct from each other. Finally, these landmark sequences are translated into face videos. Our experimental results demonstrate the effectiveness of our CMM-Net in generating realistic videos of multiple smile expressions.Comment: Accepted as a poster in Conference on Computer Vision and Pattern Recognition (CVPR), 201

Consequence Based Procedure for Description Logics with Self Restriction

We present a consequence based classification procedure for the description logics with self restriction constructor. Due to the difficulty of constructing a concept inclusion model for self restriction, we use a different proof by showing that all the completion rules can simulate all the corresponding ordered resolution inferences