Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification

Multiple kernel learning (MKL) method is generally believed to perform better than single kernel method. However, some empirical studies show that this is not always true: the combination of multiple kernels may even yield an even worse performance than using a single kernel. There are two possible reasons for the failure: (i) most existing MKL methods assume that the optimal kernel is a linear combination of base kernels, which may not hold true; and (ii) some kernel weights are inappropriately assigned due to noises and carelessly designed algorithms. In this paper, we propose a novel MKL framework by following two intuitive assumptions: (i) each kernel is a perturbation of the consensus kernel; and (ii) the kernel that is close to the consensus kernel should be assigned a large weight. Impressively, the proposed method can automatically assign an appropriate weight to each kernel without introducing additional parameters, as existing methods do. The proposed framework is integrated into a unified framework for graph-based clustering and semi-supervised classification. We have conducted experiments on multiple benchmark datasets and our empirical results verify the superiority of the proposed framework.Comment: Accepted by IJCAI 2018, Code is availabl

Bilateral-Fuser: A Novel Multi-cue Fusion Architecture with Anatomical-aware Tokens for Fovea Localization

Accurate localization of fovea is one of the primary steps in analyzing retinal diseases since it helps prevent irreversible vision loss. Although current deep learning-based methods achieve better performance than traditional methods, there still remain challenges such as utilizing anatomical landmarks insufficiently, sensitivity to diseased retinal images and various image conditions. In this paper, we propose a novel transformer-based architecture (Bilateral-Fuser) for multi-cue fusion. This architecture explicitly incorporates long-range connections and global features using retina and vessel distributions for robust fovea localization. We introduce a spatial attention mechanism in the dual-stream encoder for extracting and fusing self-learned anatomical information. This design focuses more on features distributed along blood vessels and significantly decreases computational costs by reducing token numbers. Our comprehensive experiments show that the proposed architecture achieves state-of-the-art performance on two public and one large-scale private datasets. We also present that the Bilateral-Fuser is more robust on both normal and diseased retina images and has better generalization capacity in cross-dataset experiments.Comment: This paper is prepared for IEEE TRANSACTIONS ON MEDICAL IMAGIN

Effects of a Passive Back-Support Exosuit on Postural Control and Cognitive Performance During a Fatigue-Inducing Posture Maintenance

Objective To evaluate the effectiveness of passive back-support exosuit on postural control and cognitive performance during a fatigue-inducing posture maintenance task. Background Wearable support systems (exoskeletons/exosuits) reduce physical demands but may also influence postural control and cognitive performance by reducing muscular fatigue. Method Eighteen participants visited on two different days to test an exosuit system and performed dual-task cognitive assessments based on human information processing (information acquisition, information integration, and action implementation) while maintaining a 35° trunk flexion posture for 16 minutes. Center-of-pressure (CoP), cognitive performance, and perceived workload were recorded, while erector spinae muscle activity was captured to quantify muscle fatigue. Results The exosuit was effective in reducing erector spinae muscle fatigue during the static posture maintenance task (61% less in Δmedian frequency: −9.5 Hz (EXO-Off) versus −3.7 Hz (EXO-On)). The fatigue-inducing task increased CoP velocity as a function of time (29% greater: 9.3 mm/sec (pre) versus 12.0 mm/sec (post)), and exosuit use decreased CoP velocity (23% less: 12.1 mm/sec (EXO-Off) versus 9.4 mm/sec (EXO-On)). The exosuit was also effective at mitigating cognitive degradation, as evidenced by a higher hit-to-signal ratio (8% greater: 81.3 (EXO-Off) versus 87.9 (EXO-On)) in the information integration task and reducing perceived workload in all stages of human information processing. Conclusion Exosuit provided benefits of postural control and information integration processing during a 16-min static posture maintenance task.This is a manuscript of the article Published as Kim, Jiwon, Sang Hyeon Kang, Jinfeng Li, Gary A. Mirka, and Michael C. Dorneich. "Effects of a Passive Back-Support Exosuit on Postural Control and Cognitive Performance During a Fatigue-Inducing Posture Maintenance Task." Human Factors (2024): 00187208231221890. doi: https://doi.org/10.1177/00187208231221890. Posted with Permission. © 2024 Human Factors and Ergonomics Society

Simulation study of the impact of quantum confinement on the electrostatically driven oerformance of n-type nanowire transistors

In this paper, we have studied the impact of quantum confinement on the performance of n-type silicon nanowire transistors (NWTs) for application in advanced CMOS technologies. The 3-D drift-diffusion simulations based on the density gradient approach that has been calibrated with respect to the solution of the Schrödinger equation in 2-D cross sections along the direction of the transport are presented. The simulated NWTs have cross sections and dimensional characteristics representative of the transistors expected at a 7-nm CMOS technology. Different gate lengths, cross-sectional shapes, spacer thicknesses, and doping steepness were considered. We have studied the impact of the quantum corrections on the gate capacitance, mobile charge in the channel, drain-induced barrier lowering, and subthreshold slope. The mobile charge to gate capacitance ratio, which is an indicator of the intrinsic speed of the NWTs, is also investigated. We have also estimated the optimal gate length for different NWT design conditions

Spatiotemporal Genotype Replacement of H5N8 Avian Influenza Viruses Contributed to H5N1 Emergence in 2021/2022 Panzootic

Since 2020, clade 2.3.4.4b highly pathogenic avian influenza H5N8 and H5N1 viruses have swept through continents, posing serious threats to the world. Through comprehensive analyses of epidemiological, genetic, and bird migration data, we found that the dominant genotype replacement of the H5N8 viruses in 2020 contributed to the H5N1 outbreak in the 2021/2022 wave. The 2020 outbreak of the H5N8 G1 genotype instead of the G0 genotype produced reassortment opportunities and led to the emergence of a new H5N1 virus with G1's HA and MP genes. Despite extensive reassortments in the 2021/2022 wave, the H5N1 virus retained the HA and MP genes, causing a significant outbreak in Europe and North America. Furtherly, through the wild bird migration flyways investigation, we found that the temporal-spatial coincidence between the outbreak of the H5N8 G1 virus and the bird autumn migration may have expanded the H5 viral spread, which may be one of the main drivers of the emergence of the 2020-2022 H5 panzootic.IMPORTANCESince 2020, highly pathogenic avian influenza (HPAI) H5 subtype variants of clade 2.3.4.4b have spread across continents, posing unprecedented threats globally. However, the factors promoting the genesis and spread of H5 HPAI viruses remain unclear. Here, we found that the spatiotemporal genotype replacement of H5N8 HPAI viruses contributed to the emergence of the H5N1 variant that caused the 2021/2022 panzootic, and the viral evolution in poultry of Egypt and surrounding area and autumn bird migration from the Russia-Kazakhstan region to Europe are important drivers of the emergence of the 2020-2022 H5 panzootic. These findings provide important targets for early warning and could help control the current and future HPAI epidemics.</p

Plasmonic organic solar cell and its absorption enhancement analysis using cylindrical Ag nano-particle model based on finite difference time domain (FDTD)

We report the plasmon-assisted photocurrent enhancement in Ag nanoparticles (NPs)-embedded PEDOT:PSS/P3HT:PCBM organic solar cells, and theoretically investigate the causes of the improved optical absorption based on a cylindrical Ag-NPs model which is simulated with a finite difference time domain (FDTD) method. The proposed cylindrical Ag-NPs model is able to explain the optical absorption enhancement by the localized surface plasmon resonance (LSPR) modes, and to provide a further understanding of Ag-NPs shape parameters which play an important role to determine the broadband absorption phenomena in plasmonic organic solar cells