Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN

Discriminative localization is essential for fine-grained image classification task, which devotes to recognizing hundreds of subcategories in the same basic-level category. Reflecting on discriminative regions of objects, key differences among different subcategories are subtle and local. Existing methods generally adopt a two-stage learning framework: The first stage is to localize the discriminative regions of objects, and the second is to encode the discriminative features for training classifiers. However, these methods generally have two limitations: (1) Separation of the two-stage learning is time-consuming. (2) Dependence on object and parts annotations for discriminative localization learning leads to heavily labor-consuming labeling. It is highly challenging to address these two important limitations simultaneously. Existing methods only focus on one of them. Therefore, this paper proposes the discriminative localization approach via saliency-guided Faster R-CNN to address the above two limitations at the same time, and our main novelties and advantages are: (1) End-to-end network based on Faster R-CNN is designed to simultaneously localize discriminative regions and encode discriminative features, which accelerates classification speed. (2) Saliency-guided localization learning is proposed to localize the discriminative region automatically, avoiding labor-consuming labeling. Both are jointly employed to simultaneously accelerate classification speed and eliminate dependence on object and parts annotations. Comparing with the state-of-the-art methods on the widely-used CUB-200-2011 dataset, our approach achieves both the best classification accuracy and efficiency.Comment: 9 pages, to appear in ACM MM 201

Spectral and Energy Efficiency of IRS-Assisted MISO Communication with Hardware Impairments

In this letter, we analyze the spectral and energy efficiency of an intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) downlink system with hardware impairments. An extended error vector magnitude (EEVM) model is utilized to characterize the impact of radio-frequency (RF) impairments at the access point (AP) and phase noise is considered at the IRS. We show that the spectral efficiency is limited due to the hardware impairments even when the numbers of AP antennas and IRS elements grow infinitely large, which is in contrast with the conventional case with ideal hardware. Moreover, the performance degradation at high SNR is shown to be mainly affected by the AP hardware impairments rather than by the phase noise at the IRS. We further obtain in closed form the optimal transmit power for energy efficiency maximization. Simulation results are provided to verify the obtained results

Multi-Sem Fusion: Multimodal Semantic Fusion for 3D Object Detection

LiDAR-based 3D Object detectors have achieved impressive performances in many benchmarks, however, multisensors fusion-based techniques are promising to further improve the results. PointPainting, as a recently proposed framework, can add the semantic information from the 2D image into the 3D LiDAR point by the painting operation to boost the detection performance. However, due to the limited resolution of 2D feature maps, severe boundary-blurring effect happens during re-projection of 2D semantic segmentation into the 3D point clouds. To well handle this limitation, a general multimodal fusion framework MSF has been proposed to fuse the semantic information from both the 2D image and 3D points scene parsing results. Specifically, MSF includes three main modules. First, SOTA off-the-shelf 2D/3D semantic segmentation approaches are employed to generate the parsing results for 2D images and 3D point clouds. The 2D semantic information is further re-projected into the 3D point clouds with calibrated parameters. To handle the misalignment between the 2D and 3D parsing results, an AAF module is proposed to fuse them by learning an adaptive fusion score. Then the point cloud with the fused semantic label is sent to the following 3D object detectors. Furthermore, we propose a DFF module to aggregate deep features in different levels to boost the final detection performance. The effectiveness of the framework has been verified on two public large-scale 3D object detection benchmarks by comparing with different baselines. The experimental results show that the proposed fusion strategies can significantly improve the detection performance compared to the methods using only point clouds and the methods using only 2D semantic information. Most importantly, the proposed approach significantly outperforms other approaches and sets new SOTA results on the nuScenes testing benchmark.Comment: Submitted to T-ITS Journa

Sodium butyrate ameliorates gut dysfunction and motor deficits in a mouse model of Parkinson’s disease by regulating gut microbiota

BackgroundA growing body of evidence showed that gut microbiota dysbiosis might be associated with the pathogenesis of Parkinson’s disease (PD). Microbiota-targeted interventions could play a protective role in PD by regulating the gut microbiota-gut-brain axis. Sodium butyrate (NaB) could improve gut microbiota dysbiosis in PD and other neuropsychiatric disorders. However, the potential mechanism associated with the complex interaction between NaB and gut microbiota-gut-brain communication in PD needs further investigation.MethodsC57BL/6 mice were subjected to a rotenone-induced PD model and were treated intragastrically with NaB for 4 weeks. The gut function and motor function were evaluated. The α-synuclein expression in colon and substantia nigra were detected by western blotting. Tyrosine hydroxylase (TH)-positive neurons in substantia nigra were measured by immunofluorescence. Moreover, gut microbiota composition was analyzed by 16S rRNA sequencing. Fecal short chain fatty acids (SCFAs) levels were determined by liquid chromatography tandem mass spectrometry (LC–MS). The levels of glucagon like peptide-1 (GLP-1) in tissues and serum were evaluated using enzyme-linked immunosorbent assay (ELISA).ResultsNaB ameliorated gut dysfunction and motor deficits in rotenone-induced mice. Meanwhile, NaB protected against rotenone-induced α-synuclein expression in colon and substantia nigra, and prevented the loss of TH-positive neurons. In addition, NaB could remodel gut microbiota composition, and regulate gut SCFAs metabolism, and restore GLP-1 levels in colon, serum, and substantia nigra in PD mice.ConclusionNaB could ameliorate gut dysfunction and motor deficits in rotenone-induced PD mice, and the mechanism might be associated with the regulation of gut microbiota dysbiosis

Ergodic Rate Analysis of Cooperative Ambient Backscatter Communication

Ambient backscatter communication has shown great potential in the development of future wireless networks. It enables a backscatter transmitter (BTx) to send information directly to an adjacent receiver by modulating over ambient radio frequency (RF) carriers. In this paper, we consider a cooperative ambient backscatter communication system where a multi-antenna cooperative receiver separately decodes signals from an RF source and a BTx. Upper bounds of the ergodic rates of both links are derived. The power scaling laws are accordingly characterized for both the primary cellular transmission and the cooperative backscatter. The impact of additional backscatter link is also quantitatively analyzed. Simulation results are provided to verify the derived results

Principal Component Regression Analysis of Nutrition Factors and Physical Activities with Diabetes

The associations of nutrition factors and physical activities with adult diabetes are inconsistent; while most of these factors are inter correlated. The aims of this study are to overcome the disturbance of the multicollinearity of the risk factors and examine the associations of these factors with diabetes using the principal component analysis (PCA) and regression analysis with principal component scores (PCS). Totally, 659 adults with diabetes and 2827 non-diabetic were selected from the 2012 Health Information National Trends Survey (HINTS 4, Cycle 2). PCA was utilized to deal with multicollinearity of the risk factors. Weighted univariate and multiple logistic regression analyses were used to estimate the associations of potential factors and PCS with diabetes. The odds ratios (ORs) with 95% confidence intervals (CIs) were estimated. The first 3 PCs for nutrition factors and physical activities could explain 70% variances. The first principal component (PC1) is a measure of nutrition factors (fruit and vegetables consumption), PC2 is a measure for physical activities (moderate exercise and strength training), and PC3 is about calorie information use and soda use. Weighted multiple logistic regression showed that African Americans, middle aged adults (45-64 years), elderly (65+), never married, and with lower education were associated with increased odds of diabetes. After adjusting for others factors, the PC1 showed marginal association with diabetes (OR=0.84, 95% CI=0.70-1.01); while PC2 and PC3 revealed significant associations with diabetes (OR=0.73, 95% CI=0.61-0.86 and OR=0.85, 95% CI=0.74-0.99, respectively). In conclusion, PCA can be used to reduce the indicators in complex survey data. The first 3 PCs of nutrition factors and physical activities were associated with diabetes. Promotion of health food and physical activities should be encouraged to help decrease the prevalence of diabetes

SYENet: A Simple Yet Effective Network for Multiple Low-Level Vision Tasks with Real-time Performance on Mobile Device

With the rapid development of AI hardware accelerators, applying deep learning-based algorithms to solve various low-level vision tasks on mobile devices has gradually become possible. However, two main problems still need to be solved: task-specific algorithms make it difficult to integrate them into a single neural network architecture, and large amounts of parameters make it difficult to achieve real-time inference. To tackle these problems, we propose a novel network, SYENet, with only $~$6K parameters, to handle multiple low-level vision tasks on mobile devices in a real-time manner. The SYENet consists of two asymmetrical branches with simple building blocks. To effectively connect the results by asymmetrical branches, a Quadratic Connection Unit(QCU) is proposed. Furthermore, to improve performance, a new Outlier-Aware Loss is proposed to process the image. The proposed method proves its superior performance with the best PSNR as compared with other networks in real-time applications such as Image Signal Processing(ISP), Low-Light Enhancement(LLE), and Super-Resolution(SR) with 2K60FPS throughput on Qualcomm 8 Gen 1 mobile SoC(System-on-Chip). Particularly, for ISP task, SYENet got the highest score in MAI 2022 Learned Smartphone ISP challenge

Systematically characterizing dysfunctional long intergenic noncoding RNAs in multiple brain regions of major psychosis

Alzheimer\u27s disease (AD), the most common form of dementia, is a chronic neurodegenerative disease. The HECT domain and ankyrin repeat containing E3 ubiquitin protein ligase 1 (HACE1) gene is expressed in human brain and may play a role in the pathogenesis of neurodegenerative disorders. Till now, no previous study has reported the association of the HACE1 gene with the risk and age at onset (AAO) of AD; while few studies have checked the proportional hazards assumption in the survival analysis of AAO of AD using Cox proportional hazards model. In this study, we examined the associations of 14 single nucleotide polymorphisms (SNPs) in the HACE1 gene with the risk and the AAO of AD using 791 AD patients and 782 controls. Multiple logistic regression model identified one SNP (rs9499937 with p = 1.8×10-3) to be associated with the risk of AD. For survival analysis of AAO, both classic Cox regression model and Bayesian survival analysis using the Cox proportional hazards model were applied to examine the association of each SNP with the AAO. The hazards ratio (HR) with its 95% confidence interval (CI) was estimated. Survival analysis using the classic Cox regression model showed that 4 SNPs were significantly associated with the AAO (top SNP rs9499937 with HR=1.33, 95%CI=1.13-1.57, p=5.0×10-4). Bayesian Cox regression model showed similar but a slightly stronger associations (top SNP rs9499937 with HR=1.34, 95%CI=1.11-1.55) compared with the classic Cox regression model. Using an independent family-based sample, one SNP rs9486018 was associated with the risk of AD (p=0.0323) and the T-T-G haplotype from rs9786015, rs9486018 and rs4079063 showed associations with both the risk and AAO of AD (p=2.27×10-3 and 0.0487, respectively). The findings of this study provide first evidence that several genetic variants in the HACE1 gene were associated with the risk and AAO of AD