Efficient MRF Energy Propagation for Video Segmentation via Bilateral Filters

Segmentation of an object from a video is a challenging task in multimedia applications. Depending on the application, automatic or interactive methods are desired; however, regardless of the application type, efficient computation of video object segmentation is crucial for time-critical applications; specifically, mobile and interactive applications require near real-time efficiencies. In this paper, we address the problem of video segmentation from the perspective of efficiency. We initially redefine the problem of video object segmentation as the propagation of MRF energies along the temporal domain. For this purpose, a novel and efficient method is proposed to propagate MRF energies throughout the frames via bilateral filters without using any global texture, color or shape model. Recently presented bi-exponential filter is utilized for efficiency, whereas a novel technique is also developed to dynamically solve graph-cuts for varying, non-lattice graphs in general linear filtering scenario. These improvements are experimented for both automatic and interactive video segmentation scenarios. Moreover, in addition to the efficiency, segmentation quality is also tested both quantitatively and qualitatively. Indeed, for some challenging examples, significant time efficiency is observed without loss of segmentation quality.Comment: Multimedia, IEEE Transactions on (Volume:16, Issue: 5, Aug. 2014

A Search for pair production of the LSP ντ~\tilde{\nu_{\tau}} at the CLIC via RPV Decays

In this work we consider pair production of LSP tau-sneutrinos at the Compact Lineer Collider. We assume that tau-sneutrinos decays in to e\textmu pair via RPV interactions. Backgroundless subprocess $e{}^{-}e^{+}\rightarrow\tilde{\nu}\bar{\tilde{\nu}}\rightarrow\mu^{+}\mu^{+}e^{-}e^{-}(\mu^{-}\mu^{-}e^{+}e^{+})$ is analyzed in details. Achievable limits on $Br\,(\tilde{\nu}_{\tau}\rightarrow\mu e)$ at $3\sigma$ and $5\sigma$ CL are obtained depending on $\tilde{\nu_{\tau}}$ mass.Comment: 8 pages, 5 figure

Generalized Sum Pooling for Metric Learning

A common architectural choice for deep metric learning is a convolutional neural network followed by global average pooling (GAP). Albeit simple, GAP is a highly effective way to aggregate information. One possible explanation for the effectiveness of GAP is considering each feature vector as representing a different semantic entity and GAP as a convex combination of them. Following this perspective, we generalize GAP and propose a learnable generalized sum pooling method (GSP). GSP improves GAP with two distinct abilities: i) the ability to choose a subset of semantic entities, effectively learning to ignore nuisance information, and ii) learning the weights corresponding to the importance of each entity. Formally, we propose an entropy-smoothed optimal transport problem and show that it is a strict generalization of GAP, i.e., a specific realization of the problem gives back GAP. We show that this optimization problem enjoys analytical gradients enabling us to use it as a direct learnable replacement for GAP. We further propose a zero-shot loss to ease the learning of GSP. We show the effectiveness of our method with extensive evaluations on 4 popular metric learning benchmarks. Code is available at: GSP-DML FrameworkComment: Accepted as a conference paper at International Conference on Computer Vision (ICCV) 202

Maxillary Tuberosity Reconstruction with Transport Distraction Osteogenesis

Severe bone loss due to pathology in the maxillary tuberosity region is a challenging problem both surgically and prosthetically. Large bone grafts have a poor survival rate due to the delicate bony architecture in this area and presence of the maxillary sinus. Our case presentation describes a new technique for reconstructing severe bony defect in the maxillary tuberosity with horizontal distraction osteogenesis in a 45-year-old man. A 4 × 6 × 3 cm cyst was discovered in the left maxillary molar region and enucleated. Three months postoperatively, the area had a severe bone defect extending to the zygomatic buttress superiorly and hamular notch posteriorly. Three months later, a bone segment including the right upper second premolar was osteotomised and distracted horizontally. The bone segment was distracted 15 mm distally. After consolidation, implants were placed when the distractor was removed. A fixed denture was loaded over the implants after 3 months. Complete alveolar bone loss extending to the cranial base can be reconstructed with transport distraction osteogenesis. Distalisation of the alveolar bone segment adjacent to the bony defect is an easy method for reconstructing such severe defects

Double cantilever indirect tension testing for fracture of quasibrittle materials

The Double Cantilever Beam (DCB) Mode I fracture testing has been widely used in fracture testing of especially fiber reinforced polymer composites and adhesive joints. Application of classical DCB testing to plain concrete or unreinforced ceramic specimens is not straightforward and cannot be carried out as in fiber reinforced polymer composites. Instead, an indirect tension approach is proposed in this study. Tests of notched geometrically similar DCB specimens made of normal and high strength concretes loaded eccentrically at the cantilever beam-column ends in compression have been carried out. Classical Type II size effect analyses of peak loads obtained from these tests are performed. The Microplane Model M7 is calibrated independently using uniaxial compression tests and employed to predict the peak loads of both tested and virtual geometrically similar DCB specimens. The same size effect analyses are performed on the predicted peak loads and the errors in the fracture parameters of the classical size effect analysis are determined.Peer ReviewedPostprint (author's final draft

Preparation and Structure of the Ion-Conducting Mixed Molecular Glass Ga2I3.17

Modern functional glasses have been prepared from a wide range of precursors, combining the benefits of their isotropic disordered structures with the innate functional behavior of their atomic or molecular building blocks. The enhanced ionic conductivity of glasses compared to their crystalline counterparts has attracted considerable interest for their use in solid-state batteries. In this study, we have prepared the mixed molecular glass Ga2I3.17 and investigated the correlations between the local structure, thermal properties, and ionic conductivity. The novel glass displays a glass transition at 60 °C, and its molecular make-up consists of GaI4– tetrahedra, Ga2I62– heteroethane ions, and Ga+ cations. Neutron diffraction was employed to characterize the local structure and coordination geometries within the glass. Raman spectroscopy revealed a strongly localized nonmolecular mode in glassy Ga2I3.17, coinciding with the observation of two relaxation mechanisms below Tg in the AC admittance spectra

Preparation of Ion Imprinted SPR Sensor for Real-Time Detection of Silver(I) Ion from Aqueous Solution

The aim of the submitted study is to develop molecular imprinting based surface plasmon resonance (SPR) sensor for real-time silver ion detection. For this purpose polymeric nanofilm layer on the gold SPR chip surface was prepared via UV polymerization of acrylic acid at 395 nm for 30 minutes. N-methacryloyl- L cysteine used as the functional monomer to recognize the silver(I) ions from the aqueous solutions and methylene bisacrylamide used as the crosslinker for obtaining structural rigidity of the formed cavities. Silver(I) solutions with different concentrations were applied to SPR system to investigate the efficiency of the imprinted SPR sensor in real time. For the control experiments, non-imprinted SPR sensor was also prepared as described above without addition of template “silver(I) ions”. Prepared SPR sensors were characterized with atomic force microscopy (AFM). In order to show the selectivity of the silver(I) imprinted SPR sensor, competitive adsorption of Cu(II), Pb(II), Ni(II) ions was investigated. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3489

RanDumb: a simple approach that questions the efficacy of continual representation learning

We propose RanDumb to examine the efficacy of continual representation learning. RanDumb embeds raw pixels using a fixed random transform which approximates an RBF-Kernel, initialized before seeing any data, and learns a simple linear classifier on top. We present a surprising and consistent finding: RanDumb significantly outperforms the continually learned representations using deep networks across numerous continual learning benchmarks, demonstrating the poor performance of representation learning in these scenarios. RanDumb stores no exemplars and performs a single pass over the data, processing one sample at a time. It complements GDumb [39], operating in a lowexemplar regime where GDumb has especially poor performance. We reach the same consistent conclusions when RanDumb is extended to scenarios with pretrained models replacing the random transform with pretrained feature extractor. Our investigation is both surprising and alarming as it questions our understanding of how to effectively design and train models that require efficient continual representation learning, and necessitates a principled reinvestigation of the widely explored problem formulation itself. Our code is available here

Universality for orthogonal and symplectic Laguerre-type ensembles

We give a proof of the Universality Conjecture for orthogonal (beta=1) and symplectic (beta=4) random matrix ensembles of Laguerre-type in the bulk of the spectrum as well as at the hard and soft spectral edges. Our results are stated precisely in the Introduction (Theorems 1.1, 1.4, 1.6 and Corollaries 1.2, 1.5, 1.7). They concern the appropriately rescaled kernels K_{n,beta}, correlation and cluster functions, gap probabilities and the distributions of the largest and smallest eigenvalues. Corresponding results for unitary (beta=2) Laguerre-type ensembles have been proved by the fourth author in [23]. The varying weight case at the hard spectral edge was analyzed in [13] for beta=2: In this paper we do not consider varying weights. Our proof follows closely the work of the first two authors who showed in [7], [8] analogous results for Hermite-type ensembles. As in [7], [8] we use the version of the orthogonal polynomial method presented in [25], [22] to analyze the local eigenvalue statistics. The necessary asymptotic information on the Laguerre-type orthogonal polynomials is taken from [23].Comment: 75 page