Face Recognition from Sequential Sparse 3D Data via Deep Registration

Previous works have shown that face recognition with high accurate 3D data is more reliable and insensitive to pose and illumination variations. Recently, low-cost and portable 3D acquisition techniques like ToF(Time of Flight) and DoE based structured light systems enable us to access 3D data easily, e.g., via a mobile phone. However, such devices only provide sparse(limited speckles in structured light system) and noisy 3D data which can not support face recognition directly. In this paper, we aim at achieving high-performance face recognition for devices equipped with such modules which is very meaningful in practice as such devices will be very popular. We propose a framework to perform face recognition by fusing a sequence of low-quality 3D data. As 3D data are sparse and noisy which can not be well handled by conventional methods like the ICP algorithm, we design a PointNet-like Deep Registration Network(DRNet) which works with ordered 3D point coordinates while preserving the ability of mining local structures via convolution. Meanwhile we develop a novel loss function to optimize our DRNet based on the quaternion expression which obviously outperforms other widely used functions. For face recognition, we design a deep convolutional network which takes the fused 3D depth-map as input based on AMSoftmax model. Experiments show that our DRNet can achieve rotation error 0.95{\deg} and translation error 0.28mm for registration. The face recognition on fused data also achieves rank-1 accuracy 99.2% , FAR-0.001 97.5% on Bosphorus dataset which is comparable with state-of-the-art high-quality data based recognition performance.Comment: To be appeared in ICB201

SAM-PARSER: Fine-tuning SAM Efficiently by Parameter Space Reconstruction

Segment Anything Model (SAM) has received remarkable attention as it offers a powerful and versatile solution for object segmentation in images. However, fine-tuning SAM for downstream segmentation tasks under different scenarios remains a challenge, as the varied characteristics of different scenarios naturally requires diverse model parameter spaces. Most existing fine-tuning methods attempt to bridge the gaps among different scenarios by introducing a set of new parameters to modify SAM's original parameter space. Unlike these works, in this paper, we propose fine-tuning SAM efficiently by parameter space reconstruction (SAM-PARSER), which introduce nearly zero trainable parameters during fine-tuning. In SAM-PARSER, we assume that SAM's original parameter space is relatively complete, so that its bases are able to reconstruct the parameter space of a new scenario. We obtain the bases by matrix decomposition, and fine-tuning the coefficients to reconstruct the parameter space tailored to the new scenario by an optimal linear combination of the bases. Experimental results show that SAM-PARSER exhibits superior segmentation performance across various scenarios, while reducing the number of trainable parameters by $\approx 290$ times compared with current parameter-efficient fine-tuning methods

Inequalities for Permanents and Permanental Minors of Row Substochastic Matrices

In this paper, some inequalities for permanents and permanental minors of row substochastic matrices are proved. The convexity of the permanent function on the interval between the identity matrix and an arbitrary row substochastic matrix is also proved. In addition, a conjecture about the permanent and permanental minors of square row substochastic matrices with fixed row and column sums is formulated

Machine Learning for Predictive Deployment of UAVs with Multiple Access

In this paper, a machine learning based deployment framework of unmanned aerial vehicles (UAVs) is studied. In the considered model, UAVs are deployed as flying base stations (BS) to offload heavy traffic from ground BSs. Due to time-varying traffic distribution, a long short-term memory (LSTM) based prediction algorithm is introduced to predict the future cellular traffic. To predict the user service distribution, a KEG algorithm, which is a joint K-means and expectation maximization (EM) algorithm based on Gaussian mixture model (GMM), is proposed for determining the service area of each UAV. Based on the predicted traffic, the optimal UAV positions are derived and three multi-access techniques are compared so as to minimize the total transmit power. Simulation results show that the proposed method can reduce up to 24\% of the total power consumption compared to the conventional method without traffic prediction. Besides, rate splitting multiple access (RSMA) has the lower required transmit power compared to frequency domain multiple access (FDMA) and time domain multiple access (TDMA)

A106: Aerobic Exercise Modulates GPCR/cAMP/PKA Signaling Pathway and Complement-Microglia Axis to Prevent Synaptic Loss in APP/PS1 Mice

Purpose: Synaptic failure serves as a primary contributor to memory dysfunction in Alzheimer\u27s disease (AD). Physical exercise has demonstrated the potential to thwart and delay degenerative alterations in memory functions linked to AD. Investigating the underlying mechanisms may unveil crucial insights into early pathological changes, offering breakthroughs for both understanding and treating AD. Methods: We utilized 3-month-old APP/PS1 mice and subjected them to a 12-week aerobic exercise intervention. The spatial learning and memory functions of the mice were assessed using the Morris water maze test, while Golgi staining was employed to determine dendritic spine density in each mouse group. To analyze the potential mechanisms mediating the effects of exercise intervention in the AD brain, we conducted RNA sequencing. Subsequently, pathway enrichment analysis, immunofluorescence, real-time quantitative PCR, and western blotting were employed to elucidate the impact of regular aerobic exercise on the GPCR/cAMP/PKA signaling pathway and complement-microglia axis. Results: Our findings reveal that a 12-week aerobic exercise intervention significantly enhanced spatial learning and memory function in APP/PS1 mice. Moreover, it led to a substantial increase in dendritic spine density and elevated expression of postsynaptic density protein 95 (PSD-95) in the cortex and hippocampus. Aerobic exercise demonstrated the ability to improve the expression of certain genes and enhance synaptic pathways in the brains of APP/PS1 mice. This suggests that aerobic exercise facilitates synaptic growth in APP/PS1 mice by modulating G protein-coupled receptors (GPCRs) and activating the cAMP signaling pathway, with significant alterations observed in the expressions of Hcar1 and Vipr2 genes. Furthermore, exercise intervention resulted in the significant down-regulation (P \u3c 0.05 or P \u3c 0.01) of cAMP, p-PKA/PKA, GluA1, and CaMKII protein expressions in the brain tissue of APP/PS1 mice, which were subsequently up-regulated after exercise (P \u3c 0.01). Notably, regular aerobic exercise effectively suppressed the activation of IBA-1+ microglia cells (P \u3c 0.01), reversed changes in M1 phenotype markers (Cd86 and iNOS) and M2 phenotype markers (Arg-1) of microglia cells (P \u3c 0.05), reduced the production of promoters C1q and central factor C3 in the macrosomatic cascade (P \u3c 0.05), and prevented the colocalization of microglia and PSD-95 (P \u3c 0.01). Conclusion: In conclusion, our results indicate that physical exercise plays a pivotal role in fostering early synaptic growth and averting synaptic loss in Alzheimer\u27s disease (AD). This effect may be attributed to the regulation of the G protein-coupled receptors (GPCRs)/cAMP/PKA signaling pathway and the suppression of complement-mediated microglial phagocytosis of synapses. This mechanistic insight underscores the inherent contribution of exercise to health promotion, offering potential avenues for synaptic-focused interventions in the early stages of AD treatment

Boosting Unsupervised Contrastive Learning Using Diffusion-Based Data Augmentation From Scratch

Unsupervised contrastive learning methods have recently seen significant improvements, particularly through data augmentation strategies that aim to produce robust and generalizable representations. However, prevailing data augmentation methods, whether hand designed or based on foundation models, tend to rely heavily on prior knowledge or external data. This dependence often compromises their effectiveness and efficiency. Furthermore, the applicability of most existing data augmentation strategies is limited when transitioning to other research domains, especially science-related data. This limitation stems from the paucity of prior knowledge and labeled data available in these domains. To address these challenges, we introduce DiffAug-a novel and efficient Diffusion-based data Augmentation technique. DiffAug aims to ensure that the augmented and original data share a smoothed latent space, which is achieved through diffusion steps. Uniquely, unlike traditional methods, DiffAug first mines sufficient prior semantic knowledge about the neighborhood. This provides a constraint to guide the diffusion steps, eliminating the need for labels, external data/models, or prior knowledge. Designed as an architecture-agnostic framework, DiffAug provides consistent improvements. Specifically, it improves image classification and clustering accuracy by 1.6%~4.5%. When applied to biological data, DiffAug improves performance by up to 10.1%, with an average improvement of 5.8%. DiffAug shows good performance in both vision and biological domains.Comment: arXiv admin note: text overlap with arXiv:2302.07944 by other author

Pan-cancer analysis of the prevalence and associated factors of lung metastasis and the construction of the lung metastatic classification system

This study first presents an analysis of the prevalence and associated factors of the lung metastasis (LM) database and then uses this analysis to construct an LM classification system. Using cancer patient data gathered from the surveillance, epidemiology, and end results (SEER) database, this study shows that the prevalence of LM is not consistent among different cancers; that is, the prevalence of LM ranges from 0.0013 [brain; 95% confidence interval (95% CI); 0.0010–0.0018] to 0.234 (“other digestive organs”; 95% CI; 0.221–0.249). This study finds that advanced age, poor grade, higher tumor or node stage, and metastases including bone, brain, and liver are positively related to LM occurrence, while female gender, income, marital status, and insured status are negatively related. Then, this study generates four categories from 58 cancer types based on prevalence and influence factors and satisfactorily validates these. This classification system reflects the LM risk of different cancers. It can guide individualized treatment and the management of these synchronous metastatic cancer patients and help clinicians better distribute medical resources

A Survey on Label-efficient Deep Image Segmentation: Bridging the Gap between Weak Supervision and Dense Prediction

The rapid development of deep learning has made a great progress in image segmentation, one of the fundamental tasks of computer vision. However, the current segmentation algorithms mostly rely on the availability of pixel-level annotations, which are often expensive, tedious, and laborious. To alleviate this burden, the past years have witnessed an increasing attention in building label-efficient, deep-learning-based image segmentation algorithms. This paper offers a comprehensive review on label-efficient image segmentation methods. To this end, we first develop a taxonomy to organize these methods according to the supervision provided by different types of weak labels (including no supervision, inexact supervision, incomplete supervision and inaccurate supervision) and supplemented by the types of segmentation problems (including semantic segmentation, instance segmentation and panoptic segmentation). Next, we summarize the existing label-efficient image segmentation methods from a unified perspective that discusses an important question: how to bridge the gap between weak supervision and dense prediction -- the current methods are mostly based on heuristic priors, such as cross-pixel similarity, cross-label constraint, cross-view consistency, and cross-image relation. Finally, we share our opinions about the future research directions for label-efficient deep image segmentation.Comment: Accepted to IEEE TPAM

Competitive voting-based multi-class prediction for ore selection

Sensor-based intelligent sorting technology is a mineral separation technology with the merits of high-efficiency, energy-saving and water-saving. However, the prediction accuracy of conventional machine learning methods is unstable in multi-class selection of ores. The purpose of this study is to propose a competitive voting method to improve the multi-class prediction accuracy of ores in machine vision-based sorting system by combining the classification advantages of various machine learning methods. The operations of image segmentation, feature extraction and feature selection are presented to obtain the multi-class datasets. Three ones of traditional machine learning models with higher classification accuracies are used to establish competitive voting classification models. A case study using the image data of a gas coal shows the merits of the proposed approach. Results derived using this competitive voting approach reveal that it outperforms pre-existing approaches