How is Gaze Influenced by Image Transformations? Dataset and Model

Data size is the bottleneck for developing deep saliency models, because collecting eye-movement data is very time consuming and expensive. Most of current studies on human attention and saliency modeling have used high quality stereotype stimuli. In real world, however, captured images undergo various types of transformations. Can we use these transformations to augment existing saliency datasets? Here, we first create a novel saliency dataset including fixations of 10 observers over 1900 images degraded by 19 types of transformations. Second, by analyzing eye movements, we find that observers look at different locations over transformed versus original images. Third, we utilize the new data over transformed images, called data augmentation transformation (DAT), to train deep saliency models. We find that label preserving DATs with negligible impact on human gaze boost saliency prediction, whereas some other DATs that severely impact human gaze degrade the performance. These label preserving valid augmentation transformations provide a solution to enlarge existing saliency datasets. Finally, we introduce a novel saliency model based on generative adversarial network (dubbed GazeGAN). A modified UNet is proposed as the generator of the GazeGAN, which combines classic skip connections with a novel center-surround connection (CSC), in order to leverage multi level features. We also propose a histogram loss based on Alternative Chi Square Distance (ACS HistLoss) to refine the saliency map in terms of luminance distribution. Extensive experiments and comparisons over 3 datasets indicate that GazeGAN achieves the best performance in terms of popular saliency evaluation metrics, and is more robust to various perturbations. Our code and data are available at: https://github.com/CZHQuality/Sal-CFS-GAN

A New Ensemble Adversarial Attack Powered by Long-term Gradient Memories

Deep neural networks are vulnerable to adversarial attacks.Comment: Accepted by AAAI202

Sodium glucose co-transporter 2 (SGLT2) inhibition via dapagliflozin improves diabetic kidney disease (DKD) over time associatied with increasing effect on the gut microbiota in db/db mice

BackgroundThe intestinal microbiota disorder gradually aggravates during the progression of diabetes. Dapagliflozin (DAPA) can improve diabetes and diabetic kidney disease(DKD). However, whether the gut microbiota plays a role in the protection of DAPA for DKD remains unclear.MethodsTo investigate the effects of DAPA on DKD and gut microbiota composition during disease progression, in our study, we performed 16S rRNA gene sequencing on fecal samples from db/m mice (control group), db/db mice (DKD model group), and those treated with DAPA (treat group) at three timepoints of 14weeks\18weeks\22weeks.ResultsWe found that DAPA remarkably prevented weight loss and lowered fasting blood glucose in db/db mice during disease progression, eventually delaying the progression of DKD. Intriguingly, the study strongly suggested that there is gradually aggravated dysbacteriosis and increased bile acid during the development of DKD. More importantly, comparisons of relative abundance at the phylum level and partial least squares-discriminant analysis (PLS-DA) plots roughly reflected that the effect of DAPA on modulating the flora of db/db mice increased with time. Specifically, the relative abundance of the dominant Firmicutes and Bacteroidetes was not meaningfully changed among groups at 14 weeks as previous studies described. Interestingly, they were gradually altered in the treat group compared to the model group with a more protracted intervention of 18 weeks and 22 weeks. Furthermore, the decrease of Lactobacillus and the increase of norank_f:Muribaculaceae could account for the differences at the phylum level observed between the treat group and the model group at 18 weeks and 22 weeks.ConclusionWe firstly found that the protective effect of DAPA on DKD may be related to the dynamic improvement of the gut microbiota over time, possibly associated with the impact of DAPA on the bile acid pool and its antioxidation effect

Clinical Outcome of Twice-Weekly Hemodialysis Patients with Long-Term Dialysis Vintage

Background/Aims: Twice-weekly hemodialysis(HD) is prevalent in the developing countries, scarce data are available for this treatment in patients with long-term dialysis vintage. Methods: 106 patients with more than 5 years HD vintage undergoing twice-weekly HD or thrice-weekly HD in a hemodialysis center in Shanghai between December 1, 2013 and December 31, 2013 were enrolled into the cohort study with 3 years follow-up. Kaplan–Meier analysis and Cox proportional hazards models were used to compare patient survival between the two groups. Subgroup analysis of 62 patients more than 10 years HD vintage was also performed according to their different dialysis frequency. Results: Compared with patients on thrice-weekly HD, twice-weekly HD patients had significantly longer HD session time and higher single-pool Kt/V (spKt/V) (session time, 4.59±0.45 vs 4.14±0.31 hours/per session, P< 0.001; spKt/V, 2.12±0.31 vs 1.83±0.30, P< 0.001). Kaplan–Meier survival analysis indicated that the two groups had similar survival (P=0.983). Multivariate Cox regression analysis showed that age and time-dependent serum albumin were predictors of patient mortality. Subgroup analysis of 62 patients more than 10 years HD vintage also indicated that the two groups had similar survival. During the follow-up, 4 patients dropped out from the twice-weekly HD group and transferred to thrice-weekly HD. Conclusion: The similar survival between twice-weekly HD and thrice-weekly HD in patients with long-term dialysis vintage is likely relating to patient selection, individualized treatment for dialysis patients based on clinical features and socioeconomic factors remains a tough task for the clinicians