Spectrum of Fractal Interpolation Functions

In this paper we compute the Fourier spectrum of the Fractal Interpolation Functions FIFs as introduced by Michael Barnsley. We show that there is an analytical way to compute them. In this paper we attempt to solve the inverse problem of FIF by using the spectru

Fluorescence Polarization Immunoassay of Mycotoxins: A Review

Immunoassays are routinely used in the screening of commodities and foods for fungal toxins (mycotoxins). Demands to increase speed and lower costs have lead to continued improvements in such assays. Because many reported mycotoxins are low molecular weight (below 1 kDa), immunoassays for their detection have generally been constructed in competitive heterogeneous formats. An exception is fluorescence polarization immunoassay (FPIA), a homogeneous format that does not require the separation of bound and free labels (tracer). The potential for rapid, solution phase, immunoassays has been realized in the development of FPIA for many of the major groups of mycotoxins, including aflatoxins, fumonisins, group B trichothecenes (primarily deoxynivalenol), ochratoxin A, and zearalenone. This review describes the basic principles of FPIA and summarizes recent research in this area with regard to mycotoxins

Multimodal Visual Concept Learning with Weakly Supervised Techniques

Despite the availability of a huge amount of video data accompanied by descriptive texts, it is not always easy to exploit the information contained in natural language in order to automatically recognize video concepts. Towards this goal, in this paper we use textual cues as means of supervision, introducing two weakly supervised techniques that extend the Multiple Instance Learning (MIL) framework: the Fuzzy Sets Multiple Instance Learning (FSMIL) and the Probabilistic Labels Multiple Instance Learning (PLMIL). The former encodes the spatio-temporal imprecision of the linguistic descriptions with Fuzzy Sets, while the latter models different interpretations of each description's semantics with Probabilistic Labels, both formulated through a convex optimization algorithm. In addition, we provide a novel technique to extract weak labels in the presence of complex semantics, that consists of semantic similarity computations. We evaluate our methods on two distinct problems, namely face and action recognition, in the challenging and realistic setting of movies accompanied by their screenplays, contained in the COGNIMUSE database. We show that, on both tasks, our method considerably outperforms a state-of-the-art weakly supervised approach, as well as other baselines.Comment: CVPR 201