946 research outputs found
Numerical Analysis of Heat and Mass Transfer in a Fin-and-Tube Air Heat Exchanger under Full and Partial Dehumidification Conditions
Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)
In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model
for generating novel image captions. It directly models the probability
distribution of generating a word given previous words and an image. Image
captions are generated by sampling from this distribution. The model consists
of two sub-networks: a deep recurrent neural network for sentences and a deep
convolutional network for images. These two sub-networks interact with each
other in a multimodal layer to form the whole m-RNN model. The effectiveness of
our model is validated on four benchmark datasets (IAPR TC-12, Flickr 8K,
Flickr 30K and MS COCO). Our model outperforms the state-of-the-art methods. In
addition, we apply the m-RNN model to retrieval tasks for retrieving images or
sentences, and achieves significant performance improvement over the
state-of-the-art methods which directly optimize the ranking objective function
for retrieval. The project page of this work is:
www.stat.ucla.edu/~junhua.mao/m-RNN.html .Comment: Add a simple strategy to boost the performance of image captioning
task significantly. More details are shown in Section 8 of the paper. The
code and related data are available at https://github.com/mjhucla/mRNN-CR ;.
arXiv admin note: substantial text overlap with arXiv:1410.109
GW26-e1369 Noval Arg1114Cys, Glu1142Lys mutations in beta myosin heavy chain gene in Chinese pedigrees with familial hypertrophic cardiomyopathy
The strain softening model of rock damage under compression and tension
Deformation and failure of brittle material under compression are different from those under tension. The differences are characteristic for the brittle material such as rock, concrete and others. At present, few constitutive models for rock can reflect the differences. A damage-induced softening model for rock constitutive relations is presented based on statistical strength theory, continuum damage mechanics and elastic mechanics. The model can consider properties of rock mechanics such as strain softening, difference in strength between compression and tension, non-liner stress-strain relation, compressive hardening, brittleness and so on. The model is well-adapted, simple and practical as it is flexible and has only 7 parameters which can be easily obtained from uniaxial test under compression and tension. Under triaxial compression, uniaxial compression and uniaxial tension, the stress-strain relations obtained from the presented model are compared with those obtained from laboratory tests. The comparisons show that the differences between results obtained respectively from the presented model and laboratory tests are small. The presented model is rational
Dynamic Stochastic Multi-Criteria Decision Making Method Based on Prospect Theory and Conjoint Analysis
A method based on prospect theory and conjoint analysis is proposed for dynamic stochastic multi-criteria decision making problems, in which the information about criteria weight is unknown and criteria values follow some kinds of distributions. Decision-maker’s attitude towards risk is introduced into this paper. First, data is collected by investigation and criteria weights are derived by conjoint analysis. The prospect values of each alternative in different periods are calculated according to distribution function. Then, index distribution decides time sequence weight, and overall prospect values of each alternative are obtained and ranked by aggregating prospect values in different periods. Finally, an example of choosing the best product illustrates the feasibility and effectiveness of this method
Interfacial thermal conductance in graphene/black phosphorus heterogeneous structures
Graphene, as a passivation layer, can be used to protect the black phosphorus
from the chemical reaction with surrounding oxygen and water. However, black
phosphorus and graphene heterostructures have low efficiency of heat
dissipation due to its intrinsic high thermal resistance at the interfaces. The
accumulated energy from Joule heat has to be removed efficiently to avoid the
malfunction of the devices. Therefore, it is of significance to investigate the
interfacial thermal dissipation properties and manipulate the properties by
interfacial engineering on demand. In this work, the interfacial thermal
conductance between few-layer black phosphorus and graphene is studied
extensively using molecular dynamics simulations. Two critical parameters, the
critical power Pcr to maintain thermal stability and the maximum heat power
density Pmax with which the system can be loaded, are identified. Our results
show that interfacial thermal conductance can be effectively tuned in a wide
range with external strains and interracial defects. The compressive strain can
enhance the interfacial thermal conductance by one order of magnitude, while
interface defects give a two-fold increase. These findings could provide
guidelines in heat dissipation and interfacial engineering for thermal
conductance manipulation of black phosphorus-graphene heterostructure-based
devices.Comment: 33 pages, 22 figure
Mining Firm-level Uncertainty in Stock Market: A Text Mining Approach
The traditional finance paradigm seeks to understand uncertainty and their impact on stock market. However, most previous studies try to quantify uncertainty at macro-level such as the EPU index. There are few studies tapping into firm-level uncertainty. In this paper, we address this empirical anomaly by integrating text mining tools to measure the firm-level uncertainty score from news content. We focus on companies listed in S&P 1500. We crawled a total of 2,196,975 news articles from LexisNexis database from April 2007 to July 2017. We extracted uncertainty related information as features by using named entity extraction, LM dictionary, and other linguistic features. We employed nonlinear machine learning models to investigate the impact on stocks future returns by uncertainty-related features. To address the theoretical problem, we use traditional asset pricing techniques to test the relationship among information derived uncertainty and the financial market performance
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