Risk Factors of Suicide Ideation in Chinese Graduate Students: CHAID Tree Analysis

The present study aims to identify the risk factors and develop a decision tree model of suicide ideation in Chinese graduate students. A chi-square automatic interaction detection tree analysis was conducted in a graduate students sample (N=1036). Measurements included University Personality Inventory (UPI), Symptom Checklist 90 (SCL-90), and Eysenck Personality Questionnaire (EPQ). Results showed that suicide incidence of Chinese graduate students was 1.15%, with males’ was higher than females. Seventeen potential variables were considered and only three of them (depression, obsession, and neuroticism) were found to be risk factors of suicide ideation in Chinese graduate students, and the interactions between them constructed a decision tree model. These findings should be helpful for school and mental health providers to detect graduate students with a high possibility of suicide ideation, which will aid in planning of early suicide intervention and prevention for at risk students

Electrical transport across metal/two-dimensional carbon junctions: Edge versus side contacts

Metal/two-dimensional carbon junctions are characterized by using a nanoprobe in an ultrahigh vacuum environment. Significant differences were found in bias voltage (V) dependence of differential conductance (dI/dV) between edge- and side-contact; the former exhibits a clear linear relationship (i.e., dI/dV \propto V), whereas the latter is characterized by a nonlinear dependence, dI/dV \propto V3/2. Theoretical calculations confirm the experimental results, which are due to the robust two-dimensional nature of the carbon materials under study. Our work demonstrates the importance of contact geometry in graphene-based electronic devices

Representation Disparity-aware Distillation for 3D Object Detection

In this paper, we focus on developing knowledge distillation (KD) for compact 3D detectors. We observe that off-the-shelf KD methods manifest their efficacy only when the teacher model and student counterpart share similar intermediate feature representations. This might explain why they are less effective in building extreme-compact 3D detectors where significant representation disparity arises due primarily to the intrinsic sparsity and irregularity in 3D point clouds. This paper presents a novel representation disparity-aware distillation (RDD) method to address the representation disparity issue and reduce performance gap between compact students and over-parameterized teachers. This is accomplished by building our RDD from an innovative perspective of information bottleneck (IB), which can effectively minimize the disparity of proposal region pairs from student and teacher in features and logits. Extensive experiments are performed to demonstrate the superiority of our RDD over existing KD methods. For example, our RDD increases mAP of CP-Voxel-S to 57.1% on nuScenes dataset, which even surpasses teacher performance while taking up only 42% FLOPs.Comment: Accepted by ICCV2023. arXiv admin note: text overlap with arXiv:2205.15156 by other author

DCP-NAS: Discrepant Child-Parent Neural Architecture Search for 1-bit CNNs

Neural architecture search (NAS) proves to be among the effective approaches for many tasks by generating an application-adaptive neural architecture, which is still challenged by high computational cost and memory consumption. At the same time, 1-bit convolutional neural networks (CNNs) with binary weights and activations show their potential for resource-limited embedded devices. One natural approach is to use 1-bit CNNs to reduce the computation and memory cost of NAS by taking advantage of the strengths of each in a unified framework, while searching the 1-bit CNNs is more challenging due to the more complicated processes involved. In this paper, we introduce Discrepant Child-Parent Neural Architecture Search (DCP-NAS) to efficiently search 1-bit CNNs, based on a new framework of searching the 1-bit model (Child) under the supervision of a real-valued model (Parent). Particularly, we first utilize a Parent model to calculate a tangent direction, based on which the tangent propagation method is introduced to search the optimized 1-bit Child. We further observe a coupling relationship between the weights and architecture parameters existing in such differentiable frameworks. To address the issue, we propose a decoupled optimization method to search an optimized architecture. Extensive experiments demonstrate that our DCP-NAS achieves much better results than prior arts on both CIFAR-10 and ImageNet datasets. In particular, the backbones achieved by our DCP-NAS achieve strong generalization performance on person re-identification and object detection.Comment: Accepted by International Journal of Computer Visio

Fabrication of astaxanthin-loaded electrospun nanofiber-based mucoadhesive patches with water‐insoluble backing for the treatment of oral premalignant lesions

Oral premalignant lesions (OPL) are one of the most common oral diseases, affecting the quality of life and even leading to oral cancer. Current treatments commonly use steroids/retinoids in mouthwashes, films, or ointments. However, conventional drugs/formulations have significant side effects/limitations. Herein, astaxanthin-loaded polycaprolactone (PCL)/gelatin (GT) nanofiber-based mucoadhesive patches (PGA) with the water‐insoluble PCL nanofiber backing (PCL/PGA) are developed via electrospinning for the management of OPL. The saliva-insoluble PCL backing could greatly prevent drug loss after application in the oral cavity. The prepared PCL/PGA patches exhibit a suitable astaxanthin release rate for achieving high local drug concentration, which permeated into buccal mucosa. In addition, the developed thin patches display excellent wet tissue adhesion and great air permeability due to their high porosity. Notably, the in vivo experiment shows that the bioactive mucoadhesive patches significantly promote the recovery of OPL by suppressing the expression of Ki67 and cyclooxygenase-2 (COX-2), comparable to clinical tretinoin cream formulation. Also, the patches did not induce any side effects (i.e., hair loss and oral ulcers) compared to clinical tretinoin cream formulation. The results demonstrate that this novel electrospun mucoadhesive bilayer patch holds great potential for the treatment of OPL

IDa-Det: An Information Discrepancy-aware Distillation for 1-bit Detectors

Knowledge distillation (KD) has been proven to be useful for training compact object detection models. However, we observe that KD is often effective when the teacher model and student counterpart share similar proposal information. This explains why existing KD methods are less effective for 1-bit detectors, caused by a significant information discrepancy between the real-valued teacher and the 1-bit student. This paper presents an Information Discrepancy-aware strategy (IDa-Det) to distill 1-bit detectors that can effectively eliminate information discrepancies and significantly reduce the performance gap between a 1-bit detector and its real-valued counterpart. We formulate the distillation process as a bi-level optimization formulation. At the inner level, we select the representative proposals with maximum information discrepancy. We then introduce a novel entropy distillation loss to reduce the disparity based on the selected proposals. Extensive experiments demonstrate IDa-Det's superiority over state-of-the-art 1-bit detectors and KD methods on both PASCAL VOC and COCO datasets. IDa-Det achieves a 76.9% mAP for a 1-bit Faster-RCNN with ResNet-18 backbone. Our code is open-sourced on https://github.com/SteveTsui/IDa-Det

Implicit Diffusion Models for Continuous Super-Resolution

Image super-resolution (SR) has attracted increasing attention due to its wide applications. However, current SR methods generally suffer from over-smoothing and artifacts, and most work only with fixed magnifications. This paper introduces an Implicit Diffusion Model (IDM) for high-fidelity continuous image super-resolution. IDM integrates an implicit neural representation and a denoising diffusion model in a unified end-to-end framework, where the implicit neural representation is adopted in the decoding process to learn continuous-resolution representation. Furthermore, we design a scale-controllable conditioning mechanism that consists of a low-resolution (LR) conditioning network and a scaling factor. The scaling factor regulates the resolution and accordingly modulates the proportion of the LR information and generated features in the final output, which enables the model to accommodate the continuous-resolution requirement. Extensive experiments validate the effectiveness of our IDM and demonstrate its superior performance over prior arts.Comment: 8 pages, 9 figures, published to CVPR202

Simultaneous Extraction and Identification of Phenolic Compounds in Anoectochilus roxburghii Using Microwave-Assisted Extraction Combined with UPLC-Q-TOF-MS/MS and Their Antioxidant Activities

This study used MAE and RSM to extract phenolic compounds from Anoectochilus roxburghii, and the optimum conditions defined by the model to give an optimum yield of 1.31%. The antioxidant activity in vitro showed when the concentration of phenolic compounds was reached 1 mg mL-1, the clearance rates were 82.58% for DPPH and 97.62% for ABTS+. In vivo antioxidant experiments used D-galactose to build oxidative damage in healthy Kunming mice. The result showed that the extractions of A. roxburghii can improve the antioxidant ability and the medium and low dose groups had better ability to scavenge free radicals. The UPLC-Q-TOF-MS/MS was developed to identify 21 kinds of phenolic compounds by molecular mass, ms/ms fragmentation, as well as retention time. The result showed that the phenolic compounds of A. roxburghii had significant potential as a natural antioxidant to promote health and to reduce the risk of disease

Memory-enhancing effect of Rhodiola rosea L extract on aged mice

Purpose: The memory-enhancing effects of Rhodiola rosea L. extract (RRLE) on normal aged mice were assessed.Methods: In the open-field test, the effect of RRLE (150 and 300 mg/kg) on mouse locomotive activities was evaluated by investigating the extract’s influence on CAT and AchE activities in the brain tissue of mice.Results: Compared with aged group, high dose of RRLE reduced the total distance (3212.4 ± 123.1 cm, p < 0.05) significantly, increased catalase (CAT) activity (101.4 ± 12.2 U/mg pro, p < 0.05), and inhibited acetyl cholinesterase (AChE) activity (0.94 ± 0.12 U/mg pro, p < 0.05) in the brain tissue of aged mice.Conclusion: The results show that RRLE improves the memory functions of aged mice probably by increasing CAT activity while decreasing AChE activity.Keywords: Rhodiola rosea, Memory function, Catalase, Acetyl cholinesterase, Open-field tes

Joint UL/DL Resource Allocation for UAV-Aided Full-Duplex NOMA Communications

This paper proposes an unmanned aerial vehicle (UAV)-aided full-duplex non-orthogonal multiple access (FD-NOMA) method to improve spectrum efficiency. Here, UAV is utilized to partially relay uplink data and achieve channel differentiation. Successive interference cancellation algorithm is used to eliminate the interference from different directions in FD-NOMA systems. Firstly, a joint optimization problem is formulated for the uplink and downlink resource allocation of transceivers and UAV relay. The receiver determination is performed using an access-priority method. Based on the results of the receiver determination, the initial power of ground users (GUs), UAV, and base station is calculated. According to the minimum sum of the uplink transmission power, the Hungarian algorithm is utilized to pair the users. Secondly, the subchannels are assigned to the paired GUs and the UAV by a message-passing algorithm. Finally, the transmission power of the GUs and the UAV is jointly fine-tuned using the proposed access control methods. Simulation results confirm that the proposed method achieves higher performance than state-of-the-art orthogonal frequency division multiple-access method in terms of spectrum efficiency, energy efficiency, and access ratio of the ground users