24 research outputs found
Sharing deep generative representation for perceived image reconstruction from human brain activity
Decoding human brain activities via functional magnetic resonance imaging
(fMRI) has gained increasing attention in recent years. While encouraging
results have been reported in brain states classification tasks, reconstructing
the details of human visual experience still remains difficult. Two main
challenges that hinder the development of effective models are the perplexing
fMRI measurement noise and the high dimensionality of limited data instances.
Existing methods generally suffer from one or both of these issues and yield
dissatisfactory results. In this paper, we tackle this problem by casting the
reconstruction of visual stimulus as the Bayesian inference of missing view in
a multiview latent variable model. Sharing a common latent representation, our
joint generative model of external stimulus and brain response is not only
"deep" in extracting nonlinear features from visual images, but also powerful
in capturing correlations among voxel activities of fMRI recordings. The
nonlinearity and deep structure endow our model with strong representation
ability, while the correlations of voxel activities are critical for
suppressing noise and improving prediction. We devise an efficient variational
Bayesian method to infer the latent variables and the model parameters. To
further improve the reconstruction accuracy, the latent representations of
testing instances are enforced to be close to that of their neighbours from the
training set via posterior regularization. Experiments on three fMRI recording
datasets demonstrate that our approach can more accurately reconstruct visual
stimuli
Analytical considerations of flow boiling heat transfer in metal-foam filled tubes
Flow boiling in metal-foam filled tube was analytically investigated based on a modified microstructure model, an original boiling heat transfer model and fin analysis for metal foams. Microstructure model of metal foams was established, by which fiber diameter and surface area density were precisely predicted. The heat transfer model for flow boiling in metal foams was based on annular pattern, in which two phase fluid was composed by vapor region in the center of the tube and liquid region near the wall. However, it was assumed that nucleate boiling performed only in the liquid region. Fin analysis and heat transfer network for metal foams were integrated to obtain the convective heat transfer coefficient at interface. The analytical solution was verified by its good agreement with experimental data. The parametric study on heat transfer coefficient and boiling mechanism was also carried out
A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users
Spatial item recommendation has become an important means to help people
discover interesting locations, especially when people pay a visit to
unfamiliar regions. Some current researches are focusing on modelling
individual and collective geographical preferences for spatial item
recommendation based on users' check-in records, but they fail to explore the
phenomenon of user interest drift across geographical regions, i.e., users
would show different interests when they travel to different regions. Besides,
they ignore the influence of public comments for subsequent users' check-in
behaviors. Specifically, it is intuitive that users would refuse to check in to
a spatial item whose historical reviews seem negative overall, even though it
might fit their interests. Therefore, it is necessary to recommend the right
item to the right user at the right location. In this paper, we propose a
latent probabilistic generative model called LSARS to mimic the decision-making
process of users' check-in activities both in home-town and out-of-town
scenarios by adapting to user interest drift and crowd sentiments, which can
learn location-aware and sentiment-aware individual interests from the contents
of spatial items and user reviews. Due to the sparsity of user activities in
out-of-town regions, LSARS is further designed to incorporate the public
preferences learned from local users' check-in behaviors. Finally, we deploy
LSARS into two practical application scenes: spatial item recommendation and
target user discovery. Extensive experiments on two large-scale location-based
social networks (LBSNs) datasets show that LSARS achieves better performance
than existing state-of-the-art methods.Comment: Accepted by KDD 201
Semi-supervised Deep Generative Modelling of Incomplete Multi-Modality Emotional Data
There are threefold challenges in emotion recognition. First, it is difficult
to recognize human's emotional states only considering a single modality.
Second, it is expensive to manually annotate the emotional data. Third,
emotional data often suffers from missing modalities due to unforeseeable
sensor malfunction or configuration issues. In this paper, we address all these
problems under a novel multi-view deep generative framework. Specifically, we
propose to model the statistical relationships of multi-modality emotional data
using multiple modality-specific generative networks with a shared latent
space. By imposing a Gaussian mixture assumption on the posterior approximation
of the shared latent variables, our framework can learn the joint deep
representation from multiple modalities and evaluate the importance of each
modality simultaneously. To solve the labeled-data-scarcity problem, we extend
our multi-view model to semi-supervised learning scenario by casting the
semi-supervised classification problem as a specialized missing data imputation
task. To address the missing-modality problem, we further extend our
semi-supervised multi-view model to deal with incomplete data, where a missing
view is treated as a latent variable and integrated out during inference. This
way, the proposed overall framework can utilize all available (both labeled and
unlabeled, as well as both complete and incomplete) data to improve its
generalization ability. The experiments conducted on two real multi-modal
emotion datasets demonstrated the superiority of our framework.Comment: arXiv admin note: text overlap with arXiv:1704.07548, 2018 ACM
Multimedia Conference (MM'18
Online Bayesian Multiple Kernel Bipartite Ranking
Abstract Bipartite ranking aims to maximize the area under the ROC curve (AUC) of a decision function. To tackle this problem when the data appears sequentially, existing online AUC maximization methods focus on seeking a point estimate of the decision function in a linear or predefined single kernel space, and cannot learn effective kernels automatically from the streaming data. In this paper, we first develop a Bayesian multiple kernel bipartite ranking model, which circumvents the kernel selection problem by estimating a posterior distribution over the model weights. To make our model applicable to streaming data, we then present a kernelized online Bayesian passive-aggressive learning framework by maintaining a variational approximation to the posterior based on data augmentation. Furthermore, to efficiently deal with large-scale data, we design a fixed budget strategy which can effectively control online model complexity. Extensive experimental studies confirm the superiority of our Bayesian multi-kernel approach
Hexaaquamanganese(II) dipicrate dihydrate
In the title compound, [Mn(H2O)6](C6H2N3O7)2·2H2O, the manganese cation, on an inversion centre, is coordinated by six water molecules, but the picrate anion has no coordinative interaction with the manganese cation. The anions in the stack are linked via short intermolecular O⋯C (3.013 and 2.973 Å) and C⋯C (3.089 and 3.065 Å) contacts and hydrogen bonds
Atrasentan and renal events in patients with type 2 diabetes and chronic kidney disease (SONAR): a double-blind, randomised, placebo-controlled trial
Background: Short-term treatment for people with type 2 diabetes using a low dose of the selective endothelin A receptor antagonist atrasentan reduces albuminuria without causing significant sodium retention. We report the long-term effects of treatment with atrasentan on major renal outcomes. Methods: We did this double-blind, randomised, placebo-controlled trial at 689 sites in 41 countries. We enrolled adults aged 18–85 years with type 2 diabetes, estimated glomerular filtration rate (eGFR)25–75 mL/min per 1·73 m 2 of body surface area, and a urine albumin-to-creatinine ratio (UACR)of 300–5000 mg/g who had received maximum labelled or tolerated renin–angiotensin system inhibition for at least 4 weeks. Participants were given atrasentan 0·75 mg orally daily during an enrichment period before random group assignment. Those with a UACR decrease of at least 30% with no substantial fluid retention during the enrichment period (responders)were included in the double-blind treatment period. Responders were randomly assigned to receive either atrasentan 0·75 mg orally daily or placebo. All patients and investigators were masked to treatment assignment. The primary endpoint was a composite of doubling of serum creatinine (sustained for ≥30 days)or end-stage kidney disease (eGFR <15 mL/min per 1·73 m 2 sustained for ≥90 days, chronic dialysis for ≥90 days, kidney transplantation, or death from kidney failure)in the intention-to-treat population of all responders. Safety was assessed in all patients who received at least one dose of their assigned study treatment. The study is registered with ClinicalTrials.gov, number NCT01858532. Findings: Between May 17, 2013, and July 13, 2017, 11 087 patients were screened; 5117 entered the enrichment period, and 4711 completed the enrichment period. Of these, 2648 patients were responders and were randomly assigned to the atrasentan group (n=1325)or placebo group (n=1323). Median follow-up was 2·2 years (IQR 1·4–2·9). 79 (6·0%)of 1325 patients in the atrasentan group and 105 (7·9%)of 1323 in the placebo group had a primary composite renal endpoint event (hazard ratio [HR]0·65 [95% CI 0·49–0·88]; p=0·0047). Fluid retention and anaemia adverse events, which have been previously attributed to endothelin receptor antagonists, were more frequent in the atrasentan group than in the placebo group. Hospital admission for heart failure occurred in 47 (3·5%)of 1325 patients in the atrasentan group and 34 (2·6%)of 1323 patients in the placebo group (HR 1·33 [95% CI 0·85–2·07]; p=0·208). 58 (4·4%)patients in the atrasentan group and 52 (3·9%)in the placebo group died (HR 1·09 [95% CI 0·75–1·59]; p=0·65). Interpretation: Atrasentan reduced the risk of renal events in patients with diabetes and chronic kidney disease who were selected to optimise efficacy and safety. These data support a potential role for selective endothelin receptor antagonists in protecting renal function in patients with type 2 diabetes at high risk of developing end-stage kidney disease. Funding: AbbVie