Spatiotemporal salient points for visual recognition of human actions

This paper addresses the problem of human action recognition by introducing a sparse representation of image sequences as a collection of spatiotemporal events that are localized at points that are salient both in space and time. We detect the spatiotemporal salient points by measuring the variations in the information content of pixel neighborhoods not only in space but also in time. We introduce an appropriate distance metric between two collections of spatiotemporal salient points that is based on the Chamfer distance and an iterative linear time warping technique that deals with time expansion or time compression issues. We propose a classification scheme that is based on Relevance Vector Machines and on the proposed distance measure. We present results on real image sequences from a small database depicting people performing 19 aerobic exercises

Optimization of human mesenchymal stem cell manufacturing: the effects of animal/xeno-free media.

Due to their immunosuppressive properties, mesenchymal stem cells (MSC) have been evaluated for the treatment of immunological diseases. However, the animal-derived growth supplements utilized for MSC manufacturing may lead to clinical complications. Characterization of alternative media formulations is imperative for MSC therapeutic application. Human BMMSC and AdMSC were expanded in media supplemented with either human platelet lysates (HPL), serum-free media/xeno-free FDA-approved culture medium (SFM/XF), or fetal bovine serum (FBS) and the effects on their properties were investigated. The immunophenotype of resting and IFN-γ primed BMMSC and AdMSC remained unaltered in all media. Both HPL and SFM/XF increased the proliferation of BMMSC and AdMSC. Expansion of BMMSC and AdMSC in HPL increased their differentiation, compared to SFM/XF and FBS. Resting BMMSC and AdMSC, expanded in FBS or SFM/XF, demonstrated potent immunosuppressive properties in both non-primed and IFN-γ primed conditions, whereas HPL-expanded MSC exhibited diminished immunosuppressive properties. Finally, IFN-γ primed BMMSC and AdMSC expanded in SFM/XF and HPL expressed attenuated levels of IDO-1 compared to FBS. Herein, we provide strong evidence supporting the use of the FDA-approved SFM/XF medium, in contrast to the HPL medium, for the expansion of MSC towards therapeutic applications

Stem cell therapy in inflammatory bowel disease: which, when and how?

Cellular mechanisms in basic and clinical gastroenterology and hepatolog

Multimodality in Pervasive Environment

Future pervasive environments are expected to immerse users in a consistent world of probes, sensors and actuators. Multimodal interfaces combined with social computing interactions and high-performance networking can foster a new generation of pervasive environments. However, much work is still needed to harness the full potential of multimodal interaction. In this paper we discuss some short-term research goals, including advanced techniques for joining and correlating multiple data flows, each with its own approximations and uncertainty models. Also, we discuss some longer term objectives, like providing users with a mental model of their own multimodal "aura", enabling them to collaborate with the network infrastructure toward inter-modal correlation of multimodal inputs, much in the same way as the human brain extracts a single self-conscious experience from multiple sensorial data flows

Hybrid Planning and Control for Multiple Fixed-Wing Aircraft under Input Constraints

This paper presents a novel hybrid control protocol for de-conflicting multiple vehicles with constraints on control inputs. We consider turning rate and linear speed constraints to represent fixed-wing or car-like vehicles. A set of state-feedback controllers along with a state-dependent switching logic are synthesized in a hybrid system to generate collision-free trajectories that converge to the desired destinations of the vehicles. The switching law is designed so that the safety can be guaranteed while no Zeno behavior can occur. A novel temporary goal assignment technique is also designed to guarantee convergence. We analyze the individual modes for safety and the closed-loop hybrid system for convergence. The theoretical developments are demonstrated via simulation results.Comment: Best Student Paper Finalist, AIAA-SciTech GNC Conference, 201

Extensive characterization of NF-κB binding uncovers non-canonical motifs and advances the interpretation of genetic functional traits

Background Genetic studies have provided ample evidence of the influence of non-coding DNA polymorphisms on trait variance, particularly those occurring within transcription factor binding sites. Protein binding microarrays and other platforms that can map these sites with great precision have enhanced our understanding of how a single nucleotide polymorphism can alter binding potential within an in vitro setting, allowing for greater predictive capability of its effect on a transcription factor binding site. Results We have used protein binding microarrays and electrophoretic mobility shift assay-sequencing (EMSA-Seq), a deep sequencing based method we developed to analyze nine distinct human NF-κB dimers. This family of transcription factors is one of the most extensively studied, but our understanding of its DNA binding preferences has been limited to the originally described consensus motif, GGRRNNYYCC. We highlight differences between NF-κB family members and also put under the spotlight non-canonical motifs that have so far received little attention. We utilize our data to interpret the binding of transcription factors between individuals across 1,405 genomic regions laden with single nucleotide polymorphisms. We also associated binding correlations made using our data with risk alleles of disease and demonstrate its utility as a tool for functional studies of single nucleotide polymorphisms in regulatory regions. Conclusions NF-κB dimers bind specifically to non-canonical motifs and these can be found within genomic regions in which a canonical motif is not evident. Binding affinity data generated with these different motifs can be used in conjunction with data from chromatin immunoprecipitation-sequencing (ChIP-Seq) to enable allele-specific analyses of expression and transcription factor-DNA interactions on a genome-wide scale.Wellcome Trust (London, England) (grant 075491/Z/04)European Commission (Seventh Framework Programme FP7/2007-2013: Model-In (222008))European Commission (Seventh Framework Programme FP7 ITN Network INTEGER (214902))Medical Research Council (Canada) (MRC project grant G0700818

Fusing R features and local features with context-aware kernels for action recognition

The performance of action recognition in video sequences depends significantly on the representation of actions and the similarity measurement between the representations. In this paper, we combine two kinds of features extracted from the spatio-temporal interest points with context-aware kernels for action recognition. For the action representation, local cuboid features extracted around interest points are very popular using a Bag of Visual Words (BOVW) model. Such representations, however, ignore potentially valuable information about the global spatio-temporal distribution of interest points. We propose a new global feature to capture the detailed geometrical distribution of interest points. It is calculated by using the 3D R transform which is defined as an extended 3D discrete Radon transform, followed by the application of a two-directional two-dimensional principal component analysis. For the similarity measurement, we model a video set as an optimized probabilistic hypergraph and propose a context-aware kernel to measure high order relationships among videos. The context-aware kernel is more robust to the noise and outliers in the data than the traditional context-free kernel which just considers the pairwise relationships between videos. The hyperedges of the hypergraph are constructed based on a learnt Mahalanobis distance metric. Any disturbing information from other classes is excluded from each hyperedge. Finally, a multiple kernel learning algorithm is designed by integrating the l2 norm regularization into a linear SVM classifier to fuse the R feature and the BOVW representation for action recognition. Experimental results on several datasets demonstrate the effectiveness of the proposed approach for action recognition