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Data from functional trait databases have been increasingly used to address questions related to plant diversity and trait-environment relationships. However, such databases provide intraspecific data that combine individual records obtained from distinct populations at different sites and, hence, environmental conditions. This prevents distinguishing sources of variation (e.g., genetic-based variation vs. phenotypic plasticity), a necessary condition to test for adaptive processes and other determinants of plant phenotypic diversity. Consequently, individual traits measured under common growing conditions and encompassing within-species variation across the occupied geographic range have the potential to leverage trait databases with valuable data for functional and evolutionary ecology. Here, we recorded 16 functional traits and leaf hyperspectral reflectance (NIRS) data for 721 widely distributed Arabidopsis thaliana natural accessions grown in a common garden experiment. These data records, together with meteorological variables obtained during the experiment, were assembled to create the AraDiv dataset. AraDiv is a comprehensive dataset of A. thaliana's intraspecific variability that can be explored to address questions at the interface of genetics and ecology. [ABSTRACT FROM AUTHOR]

The purpose of this manuscript is to derive new convergence results for several subgradient methods applied to minimizing nonsmooth convex functions with Hölderian growth. The growth condition is satisfied in many applications and includes functions with quadratic growth and weakly sharp minima as special cases. To this end there are three main contributions. First, for a constant and sufficiently small stepsize, we show that the subgradient method achieves linear convergence up to a certain region including the optimal set, with error of the order of the stepsize. Second, if appropriate problem parameters are known, we derive a decaying stepsize which obtains a much faster convergence rate than is suggested by the classical O (1 / k) result for the subgradient method. Thirdly we develop a novel "descending stairs" stepsize which obtains this faster convergence rate and also obtains linear convergence for the special case of weakly sharp functions. We also develop an adaptive variant of the "descending stairs" stepsize which achieves the same convergence rate without requiring an error bound constant which is difficult to estimate in practice. [ABSTRACT FROM AUTHOR]

This paper is concerned with convex composite minimization problems in a Hilbert space. In these problems, the objective is the sum of two closed, proper, and convex functions where one is smooth and the other admits a computationally inexpensive proximal operator. We analyze a family of generalized inertial proximal splitting algorithms (GIPSA) for solving such problems. We establish weak convergence of the generated sequence when the minimum is attained. Our analysis unifies and extends several previous results. We then focus on $$\ell _1$$ -regularized optimization, which is the ubiquitous special case where the nonsmooth term is the $$\ell _1$$ -norm. For certain parameter choices, GIPSA is amenable to a local analysis for this problem. For these choices we show that GIPSA achieves finite 'active manifold identification', i.e. convergence in a finite number of iterations to the optimal support and sign, after which GIPSA reduces to minimizing a local smooth function. We prove local linear convergence under either restricted strong convexity or a strict complementarity condition. We determine the rate in terms of the inertia, stepsize, and local curvature. Our local analysis is applicable to certain recent variants of the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA), for which we establish active manifold identification and local linear convergence. Based on our analysis we propose a momentum restart scheme in these FISTA variants to obtain the optimal local linear convergence rate while maintaining desirable global properties. [ABSTRACT FROM AUTHOR]

Detecting fine-grained human action from video sequence is challenging. In this work, we propose to decompose this difficult analytic problem into two sequential tasks with increasing granularity. Firstly, we infer the coarse interaction status, i.e., which object is being manipulated and where the interaction occurs. To address the issue of frequent mutual occlusions during manipulation, we propose an interaction tracking framework in which hand (object) position and interaction status are jointly tracked by explicitly modeling the occlusion context. Secondly, for a given query sequence, the inferred interaction status is utilized to efficiently identify a small set of candidate matching sequences from the annotated training set. Frame-level action labels are then transferred to the query sequence by setting up the matching between the query and candidate sequences. Comprehensive experiments on two challenging fine-grained activity datasets show that: (1) the proposed interaction tracking approach achieves high tracking accuracy for multiple mutually occluded objects (hands) during manipulation action; and (2) the proposed multiple granularity analysis framework achieves superior action detection performance improvement over state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Ineffective spatial-temporal motion feature pooling has been a fundamental bottleneck for human action recognition/detection for decades. Previous pooling schemes such as global, spatial-temporal pyramid, or human and object centric pooling fail to capture discriminative motion patterns because informative movements only occur in specific regions of the human body, that depend on the type of action being performed. Global (holistic) motion feature pooling methods therefore often result in an action representation with limited discriminative capability. To address this fundamental limitation, we propose an adaptive motion feature pooling scheme that utilizes human poses as side information. Such poses can be detected for instance in assisted living and indoor smart surveillance scenarios. Taking both video sub-volumes for pooling and human pose types as hidden variables, we formulate the motion feature pooling problem as a latent structural learning problem where the relationship between the discriminative pooling video sub-volumes and the pose types is learned. The resulting pose adaptive motion feature pooling scheme is extensively tested on assisted living and smart surveillance datasets and on general action recognition benchmarks. Improved action recognition and detection performances are demonstrated. [ABSTRACT FROM AUTHOR]

Antibodies targeting IL-17A or its receptor IL-17RA show unprecedented efficacy in the treatment of autoimmune diseases such as psoriasis. These therapies, by neutralizing critical mediators of immunity, may increase susceptibility to infections. Here, we compared the effect of antibodies neutralizing IL-17A, IL-17F or TNFα on murine host responses to Mycobacterium tuberculosis infection by evaluating lung transcriptomic, microbiological and histological analyses. Coinciding with a significant increase of mycobacterial burden and pathological changes following TNFα blockade, gene array analyses of infected lungs revealed major changes of inflammatory and immune gene expression signatures 4 weeks post-infection. Specifically, gene expression associated with host-pathogen interactions, macrophage recruitment, activation and polarization, host-antimycobacterial activities, immunomodulatory responses, as well as extracellular matrix metallopeptidases, were markedly modulated by TNFα blockade. IL-17A or IL-17F neutralization elicited only mild changes of few genes without impaired host resistance four weeks after M. tuberculosis infection. Further, the absence of both IL-17RA and IL-22 pathways in genetically deficient mice did not profoundly compromise host control of M. tuberculosis over a 6-months period, ruling out potential compensation between these two pathways, while TNFα-deficient mice succumbed rapidly. These data provide experimental confirmation of the low clinical risk of mycobacterial infection under anti-IL-17A therapy, in contrast to anti-TNFα treatment. [ABSTRACT FROM AUTHOR]