University of Massachusetts Amherst

About: University of Massachusetts Amherst is a education organization based out in Amherst Center, Massachusetts, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 37274 authors who have published 83965 publications receiving 3834996 citations. The organization is also known as: UMass Amherst & Massachusetts State College.

Renewable Chemical Commodity Feedstocks from Integrated Catalytic Processing of Pyrolysis Oils

Abstract: Fast pyrolysis of lignocellulosic biomass produces a renewable liquid fuel called pyrolysis oil that is the cheapest liquid fuel produced from biomass today Here we show that pyrolysis oils can be converted into industrial commodity chemical feedstocks using an integrated catalytic approach that combines hydroprocessing with zeolite catalysis The hydroprocessing increases the intrinsic hydrogen content of the pyrolysis oil, producing polyols and alcohols The zeolite catalyst then converts these hydrogenated products into light olefins and aromatic hydrocarbons in a yield as much as three times higher than that produced with the pure pyrolysis oil The yield of aromatic hydrocarbons and light olefins from the biomass conversion over zeolite is proportional to the intrinsic amount of hydrogen added to the biomass feedstock during hydroprocessing The total product yield can be adjusted depending on market values of the chemical feedstocks and the relative prices of the hydrogen and biomass

Bilinear CNN Models for Fine-grained Visual Recognition

Abstract: We propose bilinear models, a recognition architecture that consists of two feature extractors whose outputs are multiplied using outer product at each location of the image and pooled to obtain an image descriptor. This architecture can model local pairwise feature interactions in a translationally invariant manner which is particularly useful for fine-grained categorization. It also generalizes various orderless texture descriptors such as the Fisher vector, VLAD and O2P. We present experiments with bilinear models where the feature extractors are based on convolutional neural networks. The bilinear form simplifies gradient computation and allows end-to-end training of both networks using image labels only. Using networks initialized from the ImageNet dataset followed by domain specific fine-tuning we obtain 84.1% accuracy of the CUB-200-2011 dataset requiring only category labels at training time. We present experiments and visualizations that analyze the effects of fine-tuning and the choice two networks on the speed and accuracy of the models. Results show that the architecture compares favorably to the existing state of the art on a number of fine-grained datasets while being substantially simpler and easier to train. Moreover, our most accurate model is fairly efficient running at 8 frames/sec on a NVIDIA Tesla K40 GPU. The source code for the complete system will be made available at this http URL

A control theoretic analysis of RED

Abstract: We use a previously developed nonlinear dynamic model of TCP to analyze and design active queue management (AQM) control systems using random early detection (RED). First, we linearize the interconnection of TCP and a bottlenecked queue and discuss its feedback properties in terms of network parameters such as link capacity, load and round-trip time. Using this model, we next design an AQM control system using the RED scheme by relating its free parameters such as the low-pass filter break point and loss probability profile to the network parameters. We present guidelines for designing linearly stable systems subject to network parameters like propagation delay and load level. Robustness to variations in system loads is a prime objective. We present no simulations to support our analysis.