Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval

This paper presents a new state-of-the-art for document image classification and retrieval, using features learned by deep convolutional neural networks (CNNs). In object and scene analysis, deep neural nets are capable of learning a hierarchical chain of abstraction from pixel inputs to concise and descriptive representations. The current work explores this capacity in the realm of document analysis, and confirms that this representation strategy is superior to a variety of popular hand-crafted alternatives. Experiments also show that (i) features extracted from CNNs are robust to compression, (ii) CNNs trained on non-document images transfer well to document analysis tasks, and (iii) enforcing region-specific feature-learning is unnecessary given sufficient training data. This work also makes available a new labelled subset of the IIT-CDIP collection, containing 400,000 document images across 16 categories, useful for training new CNNs for document analysis

Segmentation-Aware Convolutional Networks Using Local Attention Masks

We introduce an approach to integrate segmentation information within a convolutional neural network (CNN). This counter-acts the tendency of CNNs to smooth information across regions and increases their spatial precision. To obtain segmentation information, we set up a CNN to provide an embedding space where region co-membership can be estimated based on Euclidean distance. We use these embeddings to compute a local attention mask relative to every neuron position. We incorporate such masks in CNNs and replace the convolution operation with a "segmentation-aware" variant that allows a neuron to selectively attend to inputs coming from its own region. We call the resulting network a segmentation-aware CNN because it adapts its filters at each image point according to local segmentation cues. We demonstrate the merit of our method on two widely different dense prediction tasks, that involve classification (semantic segmentation) and regression (optical flow). Our results show that in semantic segmentation we can match the performance of DenseCRFs while being faster and simpler, and in optical flow we obtain clearly sharper responses than networks that do not use local attention masks. In both cases, segmentation-aware convolution yields systematic improvements over strong baselines. Source code for this work is available online at http://cs.cmu.edu/~aharley/segaware

Two-stage, low noise advanced technology fan. 4: Aerodynamic final report

A two-stage research fan was tested to provide technology for designing a turbofan engine for an advanced, long range commercial transport having a cruise Mach number of 0.85 -0.9 and a noise level 20 EPNdB below current requirements. The fan design tip speed was 365.8m/sec (1200ft/sec);the hub/tip ratio was 0.4; the design pressure ratio was 1.9; and the design specific flow was 209.2 kg/sec/sq m(42.85lbm/sec/sq ft). Two fan-versions were tested: a baseline configuration, and an acoustically treated configuration with a sonic inlet device. The baseline version was tested with uniform inlet flow and with tip-radial and hub-radial inlet flow distortions. The baseline fan with uniform inlet flow attained an efficiency of 86.4% at design speed, but the stall margin was low. Tip-radial distortion increased stall margin 4 percentage points at design speed and reduced peak efficiency one percentage point. Hub-radial distortion decreased stall margin 4 percentage points at all speeds and reduced peak efficiency at design speed 8 percentage points. At design speed, the sonic inlet in the cruise position reduced stall margin one percentage point and efficiency 1.5 to 4.5 percentage points. The sonic inlet in the approach position reduced stall margin 2 percentage points

Experimental evaluation of transonic stators, data and performance report, multiple- circular-arc stator A

Transonic stator with multiple circular arc airfoils and minimum curvature tested over range of flow angles and velocities - stator

Will buffer zones around schools in agricultural areas be adequate to protect children from the potential adverse effects of pesticide exposure?

California has proposed limiting agricultural pesticide use within 0.4 km of schools and childcare facilities. However, the 0.4-km buffer may not be appropriate for all pesticides because of differing toxicities, fate, and application methods. Living near pesticide use has been associated with poorer birth outcomes, neurodevelopment, and respiratory function in children. More research about exposures in schools, childcare facilities, and homes is needed. Despite incomplete science, this regulation is an important step to reduce potential exposures to children. The most vulnerable exposure period may be in utero, and future regulations should also aim to reduce exposures to pregnant women

Decline and fall:a biological, developmental, and psycholinguistic account of deliberative language processes and ageing

Background: This paper reviews the role of deliberative processes in language: those language processes that require central resources, in contrast to the automatic processes of lexicalisation, word retrieval, and parsing. 10 Aims: We describe types of deliberative processing, and show how these processes underpin high-level processes that feature strongly in language. We focus on metalin- guistic processing, strategic processing, inhibition, and planning. We relate them to frontal-lobe function and the development of the fronto-striate loop. We then focus on the role of deliberative processes in normal and pathological development and ageing, 15 and show how these processes are particularly susceptible to deterioration with age. In particular, many of the commonly observed language impairments encountered in ageing result from a decline in deliberative processing skills rather than in automatic language processes. Main Contribution: We argue that central processing plays a larger and more important 20 role in language processing and acquisition than is often credited. Conclusions: Deliberative language processes permeate language use across the lifespan. They are particularly prone to age-related loss. We conclude by discussing implications for therapy

Numerical computation of an Evans function for travelling waves

We demonstrate a geometrically inspired technique for computing Evans functions for the linearised operators about travelling waves. Using the examples of the F-KPP equation and a Keller-Segel model of bacterial chemotaxis, we produce an Evans function which is computable through several orders of magnitude in the spectral parameter and show how such a function can naturally be extended into the continuous spectrum. In both examples, we use this function to numerically verify the absence of eigenvalues in a large region of the right half of the spectral plane. We also include a new proof of spectral stability in the appropriate weighted space of travelling waves of speed $c \geq 2 \sqrt{\delta}$ in the F-KPP equation.Comment: 37 pages, 11 figure