Large-scale knowledge transfer for object localization in ImageNet

ImageNet is a large-scale database of object classes with millions of images. Unfortunately only a small fraction of them is manually annotated with bounding-boxes. This prevents useful developments, such as learning reliable object detectors for thousands of classes. In this paper we propose to automatically populate ImageNet with many more bounding-boxes, by leveraging existing manual annotations. The key idea is to localize objects of a target class for which annotations are not available, by transferring knowledge from related source classes with available annotations. We distinguish two kinds of source classes: ancestors and siblings. Each source provides knowledge about the plausible location, appearance and context of the target objects, which induces a probability distribution over windows in images of the target class. We learn to combine these distributions so as to maximize the location accuracy of the most probable window. Finally, we employ the combined distribution in a procedure to jointly localize objects in all images of the target class. Through experiments on 0.5 million images from 219 classes we show that our technique (i) annotates a wide range of classes with boundingboxes; (ii) effectively exploits the hierarchical structure of ImageNet, since all sources and types of knowledge we propose contribute to the results; (iii) scales efficiently. 1

Influence of Dichromate Ions on Corrosion Processes on Pure Magnesium

The corrosion behavior of Mg is of interest because of its growing use as an alloy in the transportation industry and also because it is a major component of some intermetallic phases in Al alloys, such as the deleterious S (Al2CuMg)-phase found in AA2024-T3. Pure Mg corrodes rapidly in a chloride-containing solution and even dissolves in water if the surface hydroxide is damaged by scratching the surface, for example. Uniform dissolution is drastically reduced in NaCl solutions (from 0.01 to 0.5 M) with the addition of very dilute concentrations of dichromate (10-4 M). However, it is replaced by a strong localized attack in the form of fast filiform-like attack. On a large-grained sample with a defined defect structure, the attack can be seen to propagate at twin boundaries. Orientation imaging microscopy analysis found that corrosion was limited to planes near {0001} orientations with propagation being in prismatic directions. Auger electron spectroscopy analysis shows that interaction of chromate with the Mg hydroxide results in incorporation of reduced chromium ions in the hydroxide surface layer. Formation of a more resistant surface film could explain the very local nature of the corrosion in this case. The interaction between dichromate ions and Mg hydroxide can also explain the higher corrosion resistance of S-phase particles in chloride solutions containing dilute dichromate, although differences in the surface film formed compared to pure Mg are observed. Sputter-etching of the surface in order to assess the depth of the attack revealed that very hard or isolating corrosion products difficult to sputter are produced along the filiform path and that chromium compounds are not integrated in the corrosion products. Focused ion beam sectioning followed by scanning electron microscopy investigation of the sectioned area, demonstrates the presence of a continuous protective surface film. Adhesion between the Mg hydroxide and the metal is lost at the location of the corrosion filament, suggesting that the mechanism of propagation is similar to filiform corrosion under a coating. The depth of attack is a couple of micrometers with large cracks present within the corroded area that could induce severe surface damage.This work was supported by the Air Force Office of Scientific Research under contract no. F49620-96-1-0479

Fast Energy Minimization Using Learned State Filters

Pairwise discrete energies defined over graphs are ubiquitous in computer vision. Many algorithms have been proposed to minimize such energies, often concentrating on sparse graph topologies or specialized classes of pairwise potentials. However, when the graph is fully connected and the pairwise potentials are arbitrary, the complexity of even approximate minimization algorithms such as TRW-S grows quadratically both in the number of nodes and in the number of states a node can take. Moreover, recent applications are using more and more computationally expensive pairwise potentials. These factors make it very hard to employ fully connected models. In this paper we propose a novel, generic algorithm to approximately minimize any discrete pairwise energy function. Our method exploits tractable sub-energies to filter the domain of the function. The parameters of the filter are learnt from instances of the same class of energies with good candidate solutions. Compared to existing methods, it efficiently handles fully connected graphs, with many states per node, and arbitrary pairwise potentials, which might be expensive to compute. We demonstrate experimentally on two applications that our algorithm is much more efficient than other generic minimization algorithms such as TRW-S, while returning essentially identical solutions. 1

Neuronal-spiking-based closed-loop stimulation during cortical ON- and OFF-states in freely moving mice.

The slow oscillation is a central neuronal dynamic during sleep, and is generated by alternating periods of high and low neuronal activity (ON- and OFF-states). Mounting evidence causally links the slow oscillation to sleep's functions, and it has recently become possible to manipulate the slow oscillation non-invasively and phase-specifically. These developments represent promising clinical avenues, but they also highlight the importance of improving our understanding of how ON/OFF-states affect incoming stimuli and what role they play in neuronal plasticity. Most studies using closed-loop stimulation rely on the electroencephalogram and local field potential signals, which reflect neuronal ON- and OFF-states only indirectly. Here we develop an online detection algorithm based on spiking activity recorded from laminar arrays in mouse motor cortex. We find that online detection of ON- and OFF-states reflects specific phases of spontaneous local field potential slow oscillation. Our neuronal-spiking-based closed-loop procedure offers a novel opportunity for testing the functional role of slow oscillation in sleep-related restorative processes and neural plasticity

Characterization of Corrosion Interfaces by the Scanning Kelvin Probe Force Microscopy Technique

A variety of interfaces relevant to corrosion processes were examined by the scanning Kelvin probe force microscopy (SKPFM) technique in order to study the influences of various parameters on the measured potential. SKPFM measurements performed on AA2024-T3 after solution exposure showed that surface composition is not the only parameter that controls the Volta potential difference, which is measured by SKPFM. The influence of surface oxide structure and adsorption at the oxide surface can be probed by SKPFM and lateral potential gradients can be observed in the absence of significant differences in oxide composition. The influence of tip-sample separation distance on the measured Volta potential difference was studied for different pure oxide-covered metals. SKPFM measurements were made in air on pure Ni and Pt samples withdrawn from solution at open circuit or under potential control. The Volta potential difference was found to be composed of a transient component that slowly discharged and a more permanent component associated with the charge of adsorbed species. The Volta potential difference transients measured on the samples emersed under potential control decayed much slower than the open-circuit potential transient measured in solution upon release of the potential control. These different measurements validate the use of SKPFM for the prediction of local corrosion sites and the study of surface modification during solution exposure

Somnotate: a probabilistic sleep stage classifier for studying vigilance state transitions

Electrophysiological recordings from freely behaving animals are a widespread and powerful mode of investigation in sleep research. These recordings generate large amounts of data that require sleep stage annotation (polysomnography), in which the data is parcellated according to three vigilance states: awake, rapid eye movement (REM) sleep, and non-REM (NREM) sleep. Manual and current computational annotation methods ignore intermediate states because the classification features become ambiguous, even though intermediate states contain important information regarding vigilance state dynamics. To address this problem, we have developed "Somnotate"—a probabilistic classifier based on a combination of linear discriminant analysis (LDA) with a hidden Markov model (HMM). First we demonstrate that Somnotate sets new standards in polysomnography, exhibiting annotation accuracies that exceed human experts on mouse electrophysiological data, remarkable robustness to errors in the training data, compatibility with different recording configurations, and an ability to maintain high accuracy during experimental interventions. However, the key feature of Somnotate is that it quantifies and reports the certainty of its annotations. We leverage this feature to reveal that many intermediate vigilance states cluster around state transitions, whereas others correspond to failed attempts to transition. This enables us to show for the first time that the success rates of different types of transition are differentially affected by experimental manipulations and can explain previously observed sleep patterns. Somnotate is open-source and has the potential to both facilitate the study of sleep stage transitions and offer new insights into the mechanisms underlying sleep-wake dynamics

Corrosion behavior of friction stir welded lap joints of AA6061-T6 aluminum alloy

In this work, the corrosion behaviors of friction-stir lap welding of 6061-T6 Al-alloy are studied. The friction-stir lap welding was performed under different welding conditions (rotation speed and welding speed). The corrosion behavior of the parent alloy, the weld nugget zone (WNZ), and the heat affected zone (HAZ) of each welded sample working as an electrode, were investigated by the Tafel polarization test in 3.5 wt. (%) NaCl at ambient temperature. The morphology of the corroded surface of each region was analyzed by scanning electron microscopy together with energy dispersive spectroscopy (SEM-EDS). The results showed that the corrosion resistance of the parent alloy was better than the WNZ and the HAZ in both welding conditions. Localized pit dissolution and intergranular corrosion were the dominant corrosion types observed in the parent alloy, WNZ, and HAZ. The parent alloy, WNZ, and HAZ exhibited similar corrosion potentials (Ecorr) after T6 heat treatment. This treatment had a better effect on the corrosion resistance of the welded regions than the parent alloy

Sparse Kernel Learning for Image Annotation

In this paper we introduce a sparse kernel learning frame-work for the Continuous Relevance Model (CRM). State-of-the-art image annotation models linearly combine evidence from several different feature types to improve image anno-tation accuracy. While previous authors have focused on learning the linear combination weights for these features, there has been no work examining the optimal combination of kernels. We address this gap by formulating a sparse kernel learning framework for the CRM, dubbed the SKL-CRM, that greedily selects an optimal combination of ker-nels. Our kernel learning framework rapidly converges to an annotation accuracy that substantially outperforms a host of state-of-the-art annotation models. We make two surprising conclusions: firstly, if the kernels are chosen correctly, only a very small number of features are required so to achieve superior performance over models that utilise a full suite of feature types; and secondly, the standard default selection of kernels commonly used in the literature is sub-optimal, and it is much better to adapt the kernel choice based on the feature type and image dataset