The Church Socialist League 1906-1923 : Origins, development and disintegration.

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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

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

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

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

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

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