2,150 research outputs found
On Lightweight Privacy-Preserving Collaborative Learning for IoT Objects
The Internet of Things (IoT) will be a main data generation infrastructure
for achieving better system intelligence. This paper considers the design and
implementation of a practical privacy-preserving collaborative learning scheme,
in which a curious learning coordinator trains a better machine learning model
based on the data samples contributed by a number of IoT objects, while the
confidentiality of the raw forms of the training data is protected against the
coordinator. Existing distributed machine learning and data encryption
approaches incur significant computation and communication overhead, rendering
them ill-suited for resource-constrained IoT objects. We study an approach that
applies independent Gaussian random projection at each IoT object to obfuscate
data and trains a deep neural network at the coordinator based on the projected
data from the IoT objects. This approach introduces light computation overhead
to the IoT objects and moves most workload to the coordinator that can have
sufficient computing resources. Although the independent projections performed
by the IoT objects address the potential collusion between the curious
coordinator and some compromised IoT objects, they significantly increase the
complexity of the projected data. In this paper, we leverage the superior
learning capability of deep learning in capturing sophisticated patterns to
maintain good learning performance. Extensive comparative evaluation shows that
this approach outperforms other lightweight approaches that apply additive
noisification for differential privacy and/or support vector machines for
learning in the applications with light data pattern complexities.Comment: 12 pages,IOTDI 201
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
Generative Invertible Networks (GIN): Pathophysiology-Interpretable Feature Mapping and Virtual Patient Generation
Machine learning methods play increasingly important roles in pre-procedural
planning for complex surgeries and interventions. Very often, however,
researchers find the historical records of emerging surgical techniques, such
as the transcatheter aortic valve replacement (TAVR), are highly scarce in
quantity. In this paper, we address this challenge by proposing novel
generative invertible networks (GIN) to select features and generate
high-quality virtual patients that may potentially serve as an additional data
source for machine learning. Combining a convolutional neural network (CNN) and
generative adversarial networks (GAN), GIN discovers the pathophysiologic
meaning of the feature space. Moreover, a test of predicting the surgical
outcome directly using the selected features results in a high accuracy of
81.55%, which suggests little pathophysiologic information has been lost while
conducting the feature selection. This demonstrates GIN can generate virtual
patients not only visually authentic but also pathophysiologically
interpretable
Emulating computer models with step-discontinuous outputs using Gaussian processes
In many real-world applications, we are interested in approximating functions that are analytically unknown. An emulator provides a "fast" approximation of such functions relying on a limited number of evaluations. Gaussian processes (GPs) are commonplace emulators due to their properties such as the ability to quantify uncertainty. GPs are essentially developed to emulate smooth, continuous functions. However, the assumptions of continuity and smoothness is unwarranted in many situations. For example, in computer models where bifurcation, tipping points occur in their systems of equations, the outputs can be discontinuous. This paper examines the capacity of GPs for emulating step-discontinuous functions using two approaches. The first approach is based on choosing covariance functions/kernels, namely neural network and Gibbs, that are most appropriate for modelling discontinuities. The predictive performance of these two kernels is illustrated using several examples. The results show that they have superior performance to standard covariance functions, such as the Mat\'ern family, in capturing sharp jumps. The second approach is to transform the input space such that in the new space a GP with a standard kernel is able to predict the function well. A parametric transformation function is used whose parameters are estimated by maximum likelihood.Engineering and Physical Sciences Research Council (EPSRC
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
The Supportive Care Needs of Cancer Patients: a Systematic Review
Cancer, and the complex nature of treatment, has a profound impact on lives of patients and their families. Subsequently, cancer patients have a wide range of needs. This study aims to identify and synthesise cancer patients' views about areas where they need support throughout their care. A systematic search of the literature from PsycInfo, Embase and Medline databases was conducted, and a narrative. Synthesis of results was carried out using the Corbin & Strauss "3 lines of work" framework. For each line of work, a group of key common needs were identified. For illness-work, the key needs idenitified were; understanding their illness and treatment options, knowing what to expect, communication with healthcare professionals, and staying well. In regards to everyday work, patients wanted to maintain a sense of normalcy and look after their loved ones. For biographical work, patients commonly struggled with the emotion impact of illness and a lack of control over their lives. Spiritual, sexual and financial problems were less universal. For some types of support, demographic factors influenced the level of need reported. While all patients are unique, there are a clear set of issues that are common to a majority of cancer journeys. To improve care, these needs should be prioritised by healthcare practitioners
Vibrotactile Signal Generation from Texture Images or Attributes using Generative Adversarial Network
Providing vibrotactile feedback that corresponds to the state of the virtual
texture surfaces allows users to sense haptic properties of them. However,
hand-tuning such vibrotactile stimuli for every state of the texture takes much
time. Therefore, we propose a new approach to create models that realize the
automatic vibrotactile generation from texture images or attributes. In this
paper, we make the first attempt to generate the vibrotactile stimuli
leveraging the power of deep generative adversarial training. Specifically, we
use conditional generative adversarial networks (GANs) to achieve generation of
vibration during moving a pen on the surface. The preliminary user study showed
that users could not discriminate generated signals and genuine ones and users
felt realism for generated signals. Thus our model could provide the
appropriate vibration according to the texture images or the attributes of
them. Our approach is applicable to any case where the users touch the various
surfaces in a predefined way.Comment: accepted for EuroHaptics 2018: Haptics: Science, Technology, and
Applications, pp.25-3
Spontaneous pregnancy loss mediated by abnormal maternal inflammation in rats is linked to deficient uteroplacental perfusion
Abnormal maternal inflammation during pregnancy is associated with spontaneous pregnancy loss and intrauterine fetal growth restriction. However, the mechanisms responsible for these pregnancy outcomes are not well understood. In this study, we used a rat model to demonstrate that pregnancy loss resulting from aberrant maternal inflammation is closely linked to deficient placental perfusion. Administration of LPS to pregnantWistar rats on gestational day 14.5, to induce maternal inflammation, caused fetal loss in a dose-dependent manner 3-4 h later, and surviving fetuses were significantly growth restricted. Pregnancy loss was associated with coagulopathy, structural abnormalities in the uteroplacental vasculature, decreased placental blood flow, and placental and fetal hypoxia within 3 h of LPS administration. This impairment in uteroplacental hemodynamics in LPS-treated rats was linked to increased uterine artery resistance and reduced spiral arteriole flow velocity. Pregnancy loss induced by LPS was prevented by maternal administration of the immunoregulatory cytokine IL-10 or by blocking TNF-α activity after treatment with etanercept (Enbrel). These results indicate that alterations in placental perfusion are responsible for fetal morbidities associated with aberrant maternal inflammation and support a rationale for investigating a potential use of immunomodulatory agents in the prevention of spontaneous pregnancy loss. Copyright © 2011 by The American Association of Immunologists, Inc
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