2,109 research outputs found
The Church Socialist League 1906-1923 : Origins, development and disintegration.
SIGLELD:D49314/84 / BLDSC - British Library Document Supply CentreGBUnited Kingdo
HYDRA: Hybrid Deep Magnetic Resonance Fingerprinting
Purpose: Magnetic resonance fingerprinting (MRF) methods typically rely on
dictio-nary matching to map the temporal MRF signals to quantitative tissue
parameters. Such approaches suffer from inherent discretization errors, as well
as high computational complexity as the dictionary size grows. To alleviate
these issues, we propose a HYbrid Deep magnetic ResonAnce fingerprinting
approach, referred to as HYDRA.
Methods: HYDRA involves two stages: a model-based signature restoration phase
and a learning-based parameter restoration phase. Signal restoration is
implemented using low-rank based de-aliasing techniques while parameter
restoration is performed using a deep nonlocal residual convolutional neural
network. The designed network is trained on synthesized MRF data simulated with
the Bloch equations and fast imaging with steady state precession (FISP)
sequences. In test mode, it takes a temporal MRF signal as input and produces
the corresponding tissue parameters.
Results: We validated our approach on both synthetic data and anatomical data
generated from a healthy subject. The results demonstrate that, in contrast to
conventional dictionary-matching based MRF techniques, our approach
significantly improves inference speed by eliminating the time-consuming
dictionary matching operation, and alleviates discretization errors by
outputting continuous-valued parameters. We further avoid the need to store a
large dictionary, thus reducing memory requirements.
Conclusions: Our approach demonstrates advantages in terms of inference
speed, accuracy and storage requirements over competing MRF method
A B-Spline-Based Generative Adversarial Network Model for Fast Interactive Airfoil Aerodynamic Optimization
Airfoil aerodynamic optimization is of great importance in aircraft design; however, it relies on high-fidelity physics-based models that are computationally expensive to evaluate. In this work, we provide a methodology to reduce the computational cost for airfoil aerodynamic optimization. Firstly, we develop a B-spline based generative adversarial networks (BSplineGAN) parameterization method to automatically infer design space with sufficient shape variability. Secondly, we construct multi-layer neural network (MNN) surrogates for fast predictions on aerodynamic drag, lift, and pitching moment coefficients. The BSplineGAN has a relative error lower than 1% when fitting to UIUC database. Verification of MNN surrogates shows the root means square errors (RMSE) of all aerodynamic coefficients are within the range of 20%–40% standard deviation of testing points. Both normalized RMSE and relative errors are controlled within 1%. The proposed methodology is then demonstrated on an airfoil aerodynamic optimization. We also verified the baseline and optimized designs using a high-fidelity computational fluid dynamic solver. The proposed framework has the potential to enable web-based fast interactive airfoil aerodynamic optimization
LoANs: Weakly Supervised Object Detection with Localizer Assessor Networks
Recently, deep neural networks have achieved remarkable performance on the
task of object detection and recognition. The reason for this success is mainly
grounded in the availability of large scale, fully annotated datasets, but the
creation of such a dataset is a complicated and costly task. In this paper, we
propose a novel method for weakly supervised object detection that simplifies
the process of gathering data for training an object detector. We train an
ensemble of two models that work together in a student-teacher fashion. Our
student (localizer) is a model that learns to localize an object, the teacher
(assessor) assesses the quality of the localization and provides feedback to
the student. The student uses this feedback to learn how to localize objects
and is thus entirely supervised by the teacher, as we are using no labels for
training the localizer. In our experiments, we show that our model is very
robust to noise and reaches competitive performance compared to a
state-of-the-art fully supervised approach. We also show the simplicity of
creating a new dataset, based on a few videos (e.g. downloaded from YouTube)
and artificially generated data.Comment: To appear in AMV18. Code, datasets and models available at
https://github.com/Bartzi/loan
Scanner Invariant Representations for Diffusion MRI Harmonization
Purpose: In the present work we describe the correction of diffusion-weighted
MRI for site and scanner biases using a novel method based on invariant
representation.
Theory and Methods: Pooled imaging data from multiple sources are subject to
variation between the sources. Correcting for these biases has become very
important as imaging studies increase in size and multi-site cases become more
common. We propose learning an intermediate representation invariant to
site/protocol variables, a technique adapted from information theory-based
algorithmic fairness; by leveraging the data processing inequality, such a
representation can then be used to create an image reconstruction that is
uninformative of its original source, yet still faithful to underlying
structures. To implement this, we use a deep learning method based on
variational auto-encoders (VAE) to construct scanner invariant encodings of the
imaging data.
Results: To evaluate our method, we use training data from the 2018 MICCAI
Computational Diffusion MRI (CDMRI) Challenge Harmonization dataset. Our
proposed method shows improvements on independent test data relative to a
recently published baseline method on each subtask, mapping data from three
different scanning contexts to and from one separate target scanning context.
Conclusion: As imaging studies continue to grow, the use of pooled multi-site
imaging will similarly increase. Invariant representation presents a strong
candidate for the harmonization of these data
Adversarial Personalized Ranking for Recommendation
Item recommendation is a personalized ranking task. To this end, many
recommender systems optimize models with pairwise ranking objectives, such as
the Bayesian Personalized Ranking (BPR). Using matrix Factorization (MF) ---
the most widely used model in recommendation --- as a demonstration, we show
that optimizing it with BPR leads to a recommender model that is not robust. In
particular, we find that the resultant model is highly vulnerable to
adversarial perturbations on its model parameters, which implies the possibly
large error in generalization.
To enhance the robustness of a recommender model and thus improve its
generalization performance, we propose a new optimization framework, namely
Adversarial Personalized Ranking (APR). In short, our APR enhances the pairwise
ranking method BPR by performing adversarial training. It can be interpreted as
playing a minimax game, where the minimization of the BPR objective function
meanwhile defends an adversary, which adds adversarial perturbations on model
parameters to maximize the BPR objective function. To illustrate how it works,
we implement APR on MF by adding adversarial perturbations on the embedding
vectors of users and items. Extensive experiments on three public real-world
datasets demonstrate the effectiveness of APR --- by optimizing MF with APR, it
outperforms BPR with a relative improvement of 11.2% on average and achieves
state-of-the-art performance for item recommendation. Our implementation is
available at: https://github.com/hexiangnan/adversarial_personalized_ranking.Comment: SIGIR 201
Class reconstruction driven adversarial domain adaptation for hyperspectral image classification
We address the problem of cross-domain classification of hyperspectral image (HSI) pairs under the notion of unsupervised domain adaptation (UDA). The UDA problem aims at classifying the test samples of a target domain by exploiting the labeled training samples from a related but different source domain. In this respect, the use of adversarial training driven domain classifiers is popular which seeks to learn a shared feature space for both the domains. However, such a formalism apparently fails to ensure the (i) discriminativeness, and (ii) non-redundancy of the learned space. In general, the feature space learned by domain classifier does not convey any meaningful insight regarding the data. On the other hand, we are interested in constraining the space which is deemed to be simultaneously discriminative and reconstructive at the class-scale. In particular, the reconstructive constraint enables the learning of category-specific meaningful feature abstractions and UDA in such a latent space is expected to better associate the domains. On the other hand, we consider an orthogonality constraint to ensure non-redundancy of the learned space. Experimental results obtained on benchmark HSI datasets (Botswana and Pavia) confirm the efficacy of the proposal approach
Laboratory support during and after the Ebola virus endgame: Towards a sustained laboratory infrastructure
The Ebola virus epidemic in West Africa is on the brink of entering a second phase in which the (inter)national efforts to slow down virus transmission will be engaged to end the epidemic. The response community must consider the longevity of their current laboratory support, as it is essential that diagnostic capacity in the affected countries be supported beyond the end of the epidemic. The emergency laboratory response should be used to support building structural diagnostic and outbreak surveillance capacity
Variational Deep Semantic Hashing for Text Documents
As the amount of textual data has been rapidly increasing over the past
decade, efficient similarity search methods have become a crucial component of
large-scale information retrieval systems. A popular strategy is to represent
original data samples by compact binary codes through hashing. A spectrum of
machine learning methods have been utilized, but they often lack expressiveness
and flexibility in modeling to learn effective representations. The recent
advances of deep learning in a wide range of applications has demonstrated its
capability to learn robust and powerful feature representations for complex
data. Especially, deep generative models naturally combine the expressiveness
of probabilistic generative models with the high capacity of deep neural
networks, which is very suitable for text modeling. However, little work has
leveraged the recent progress in deep learning for text hashing.
In this paper, we propose a series of novel deep document generative models
for text hashing. The first proposed model is unsupervised while the second one
is supervised by utilizing document labels/tags for hashing. The third model
further considers document-specific factors that affect the generation of
words. The probabilistic generative formulation of the proposed models provides
a principled framework for model extension, uncertainty estimation, simulation,
and interpretability. Based on variational inference and reparameterization,
the proposed models can be interpreted as encoder-decoder deep neural networks
and thus they are capable of learning complex nonlinear distributed
representations of the original documents. We conduct a comprehensive set of
experiments on four public testbeds. The experimental results have demonstrated
the effectiveness of the proposed supervised learning models for text hashing.Comment: 11 pages, 4 figure
Fluid Inclusion Petrography and Microthermometry of the Middle Valley Hydrothermal System, Northern Juan de Fuca Ridge
Middle Valley is a hydrothermally active, sediment-covered rift at the northernmost end of the Juan de Fuca Ridge. Two
hydrothermal centers are known from previous work: (1) a 60-m-high sediment mound with a 35-m-high inactive sulfide mound
and two 20-m-high sulfide mounds 330 m to the south, one of which is known to be active, and (2) several mounds with attendant
active hydrothermal chimneys. These sites (Sites 856 and 858, respectively), as well as other adjacent areas (Sites 857 and 855),
were drilled during Leg 139 of the Ocean Drilling Program. Fluid inclusion petrographic observations and microthermometric
measurements were made on a variety of samples and minerals recovered from these cores: (1) quartz from hydrothermally altered
sediment; (2) low iron sphalerite and interstitial dolomite in massive sulfide; (3) calcite-sulfide veins cross-cutting sediment; (4)
calcite and anhydrite concretions in sediment; (5) anhydrite veins cross-cutting sediment; and (6) wairakite and quartz veins
cross-cutting mafic sills and sediment. Trapping temperatures of fluid inclusions in hydrothermal alteration minerals precipitated
with massive sulfides range between 90° and 338°C. Fluid inclusions in calcite in carbonate concretions indicate these concretions
formed between 112° and 192°C. Anhydrite in veins and concretions was precipitated between 137° and 311 °C. Quartz-wairakiteepidote
veins in mafic sills and hydrothermally altered sediment were precipitated between 210° and 350°C. For all inclusions,
there is a general increase in minimum trapping temperatures with increasing subsurface depth for all sites, with temperatures
ranging from around 100°C at 2400 meters below sea level to around 275°C at 3100 mbsl. Eutectic and hydrohalite melting
temperatures indicate that Ca, Na, and Cl are the dominant ionic species present in the inclusion fluids. Salinities for most inclusion
fluids range between 2.5 and 7.0 equivalent weight percent NaCl. Most analyses are between 3 and 4.5 eq. wt% NaCl and similar
to ambient bottom water, pore fluids, and vent fluid from Site 858. Trapped fluids are modified seawater, and there is no evidence
for a significant magmatic fluid component. Oxygen isotopic compositions for fluids from which calcite concretions were
precipitated, calculated from isotopic analyses of carbonates formed at low temperatures (133° to 158°C from fluid inclusions),
are significantly enriched in 18O (δ1 8θ = +9.3‰ to +13.2‰), likely due to reaction with subsurface sediments at low water/rock
ratios. Calcite that formed at higher temperatures (233°C) in hydrothermally altered sediment was precipitated from fluid only
slightly enriched in 18O (δ1 8θ = +0.4%o). Estimated carbon isotope compositions of the fluid vary between δ13C = -7.0%e and
-35.4‰ and are similar to the measured range for vent fluids
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