Development of spatial coarse-to-fine processing in the visual pathway

The sequential analysis of information in a coarse-to-fine manner is a fundamental mode of processing in the visual pathway. Spatial frequency (SF) tuning, arguably the most fundamental feature of spatial vision, provides particular intuition within the coarse-to-fine framework: low spatial frequencies convey global information about an image (e.g., general orientation), while high spatial frequencies carry more detailed information (e.g., edges). In this paper, we study the development of cortical spatial frequency tuning. As feedforward input from the lateral geniculate nucleus (LGN) has been shown to have significant influence on cortical coarse-to-fine processing, we present a firing-rate based thalamocortical model which includes both feedforward and feedback components. We analyze the relationship between various model parameters (including cortical feedback strength) and responses. We confirm the importance of the antagonistic relationship between the center and surround responses in thalamic relay cell receptive fields (RFs), and further characterize how specific structural LGN RF parameters affect cortical coarse-to-fine processing. Our results also indicate that the effect of cortical feedback on spatial frequency tuning is age-dependent: in particular, cortical feedback more strongly affects coarse-to-fine processing in kittens than in adults. We use our results to propose an experimentally testable hypothesis for the function of the extensive feedback in the corticothalamic circuit.Comment: 20 pages, 7 figures; substantial restructuring from previous versio

Zjemňování zrna protlačované hořčíkové slitiny AZ31 procesem Ecap

Extruded Mg – 3%Al – 1% Zn (AZ31) magnesium alloy was subjected to ECAP (Equal Channel Angular Pressing) processing at 523 K (250 °C). At the processing temperature of 523 K, fine grains with the average grain size of 2 – 3 μm are formed as a result of dynamic recrystallization originated by fine Mg17Al12 (γ) phase particles having 200 nm diameter dynamically – precipitated during ECAP processing. Microstructural evolution during ECAP was studied systematically using optical microscope and transmission electron microscope.Protlačená hořčíková slitina Mg – 3%Al – 1%Zn (AZ31) byla zpracována procesem ECAP (úhlové protlačování rovnostranným kanálem) realizovaného při teplotě 523 K (250 °C). Při teplotě 523 K bylo dosaženo zjemnění průměrné velikosti zrna 2 - 3 μm, které bylo dosaženo dynamickou rekrystalizací jemné γ faze Mg17Al12 s průměrnou velikostí 200 nm. Vývoj mikrostruktury během ECAP byl systematicky studován na optickém mikroskopu a využitím transmisní elektronové mikroskopie

Commentary: Prestimulus theta oscillations and connectivity modulate pain perception

Pain experience includes the fine-grain integration of both attentive and automatic (bottom-up; Legrain et al., 2012), as well as affective and intentional (top-down; Buschman and Miller, 2007) processes. While the neural underpinnings of post-stimulus pain processing have been deeply explored (Hauck et al., 2008), the oscillatory brain activity preceding pain processing is less far investigated

SLS Processing Studies of Nylon 11 Nanocomposites

Selective Laser Sintering (SLS) is widely used for rapid prototyping/manufacturing of nylon 11 and nylon 12 parts. This processing technique has not been explored for nylon nanocomposites. This study investigates the technicalities of processing nylon 11-clay and nylon-carbon nanofiber nanocomposites with SLS. Microstructural analyses of the SLS powders and parts were conducted under SEM. Results suggest that SLS processing is possible with the new nylon 11 nanocomposites. Yet the SLS parts built have inferior properties relative to those of injection molding, suggesting that more fine tuning for the processing is required.Mechanical Engineerin

Development of a New Method of Storage and Maximum Separation of Chlorophils From Chlorophylcontaining Vegetables at Reception of Healthfull Nanoproducts

The aim of the work is the development of a new way of deep processing of chlorophyll-containing vegetables that gives a possibility not only to preserve chlorophylls a and b and other biologically active substances (BAS) of raw materials, but also to transform hidden bound (inactive) forms of chlorophyll in the free easy-digestible form at getting steam-thermally processed semi-products and healthy food products in the nanoform.For achieving the aim, the complex effect of steam-thermal processing and mechanolysis at fine-dyspersed comminution using the new equipment was applied as an innovation for thermal processing and comminution.There was developed the new method of getting healthy products of chlorophyll-containing vegetables (broccoli, spinach, Brussels cabbage, green leguminous haricot bean), steam-thermally processed (by hot steam) in the steam-convectional stove and fine-dyspersed with high contents of chlorophylls and other BAS and prebiotics. The method is based on the complex effect of processes of thermodestruction, mechanodestruction and non-enzymatic catalysis on raw materials at fine-dyspersed comminution. It was demonstrated, that at steam thermal processing of chlorophyll-containing vegetables (CCV) in the steam-convectomat during 5 minutes, there takes place not only preservation of chlorophylls a and b, but more full separation (in 1,33…1,4 times) from the hidden (bound) form, comparing with fresh vegetables. There was elucidated the mechanism of this process. The more full extraction of hidden forms of β-carotene (2 times more than in fresh CCV) takes place in parallel.The essentially more effect of transforming hidden forms was revealed at fine-dyspersed comminution of steam-thermally processed CCV. It was demonstrated, that thermally processed nanoproducts of CCV contain 2…2,1 more chlorophylls a and b, 2,0…3,3 times more carotenoids in the bound form than fresh vegetables.The quality of obtained new types of fine-dyspersated steam-thermally processed green products as puree and soups-purees of CCV exceeds one of known analogues by contents of chlorophylls a and b, β-carotene and other BAS, which are in nanosize easy-digestible form.Using new types of fine-dyspersated purees of CCV, there was developed the new green line of healthy nanoproducts: soups-purees, nanodrinks, nanosorbets, sauces-dressings, sauces-deeps, ice-cream, snacks and so on. It was demonstrated, that new products exceed existing analogues by BAS content (chlorophylls, β-carotene, L-ascorbic acid, phenol compounds)

Fine-grained visualization pipelines and lazy functional languages

The pipeline model in visualization has evolved from a conceptual model of data processing into a widely used architecture for implementing visualization systems. In the process, a number of capabilities have been introduced, including streaming of data in chunks, distributed pipelines, and demand-driven processing. Visualization systems have invariably built on stateful programming technologies, and these capabilities have had to be implemented explicitly within the lower layers of a complex hierarchy of services. The good news for developers is that applications built on top of this hierarchy can access these capabilities without concern for how they are implemented. The bad news is that by freezing capabilities into low-level services expressive power and flexibility is lost. In this paper we express visualization systems in a programming language that more naturally supports this kind of processing model. Lazy functional languages support fine-grained demand-driven processing, a natural form of streaming, and pipeline-like function composition for assembling applications. The technology thus appears well suited to visualization applications. Using surface extraction algorithms as illustrative examples, and the lazy functional language Haskell, we argue the benefits of clear and concise expression combined with fine-grained, demand-driven computation. Just as visualization provides insight into data, functional abstraction provides new insight into visualization

Image processing applications using a novel parallel computing machine based on reconfigurable logic

Zelig is a 32 physical node fine-grained computer employing field-programmable gate arrays. Its application to the high speed implementation of various image pre-processing operations (in particular binary morphology) is described together with typical speed-up result

Dual Skipping Networks

Inspired by the recent neuroscience studies on the left-right asymmetry of the human brain in processing low and high spatial frequency information, this paper introduces a dual skipping network which carries out coarse-to-fine object categorization. Such a network has two branches to simultaneously deal with both coarse and fine-grained classification tasks. Specifically, we propose a layer-skipping mechanism that learns a gating network to predict which layers to skip in the testing stage. This layer-skipping mechanism endows the network with good flexibility and capability in practice. Evaluations are conducted on several widely used coarse-to-fine object categorization benchmarks, and promising results are achieved by our proposed network model.Comment: CVPR 2018 (poster); fix typ

Fine-Pruning: Joint Fine-Tuning and Compression of a Convolutional Network with Bayesian Optimization

When approaching a novel visual recognition problem in a specialized image domain, a common strategy is to start with a pre-trained deep neural network and fine-tune it to the specialized domain. If the target domain covers a smaller visual space than the source domain used for pre-training (e.g. ImageNet), the fine-tuned network is likely to be over-parameterized. However, applying network pruning as a post-processing step to reduce the memory requirements has drawbacks: fine-tuning and pruning are performed independently; pruning parameters are set once and cannot adapt over time; and the highly parameterized nature of state-of-the-art pruning methods make it prohibitive to manually search the pruning parameter space for deep networks, leading to coarse approximations. We propose a principled method for jointly fine-tuning and compressing a pre-trained convolutional network that overcomes these limitations. Experiments on two specialized image domains (remote sensing images and describable textures) demonstrate the validity of the proposed approach.Comment: BMVC 2017 ora