Huntington’s disease(HD)is an autosomal dominant neurodegenerative disease that is caused by a cytosine-adenine-guanine(CAG)expansion in the first exon of the huntingtin(HTT)gene,which codes for the hun-tingtin *** t...
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Huntington’s disease(HD)is an autosomal dominant neurodegenerative disease that is caused by a cytosine-adenine-guanine(CAG)expansion in the first exon of the huntingtin(HTT)gene,which codes for the hun-tingtin *** typically manifests with a triad of symptoms,including motor disorders,cognitive impair-ment and psychiatric disturbances[1].HD primar-ily affects the basal ganglia(BG),especially the caudate and putamen,after which it extends to more widespread gray and white matter[2].Perivascular spaces(PVSs)are fluid-filled extensions of the subarachnoid spaces that enclose cerebral blood vessels and extend from the cer-ebral cortex through the brain *** physi-ological role of PVSs is the drainage of brain interstitial fluid into perivascular pathways for the elimination of waste products through the glymphatic drainage *** increasing number of studies have demonstrated that enlarged PVSs indicate glymphatic dysfunction and are associated with many neurological diseases,such as Alzheimer’s disease,Parkinson’s disease and small vessel disease[3].With the advantage of strong field strengths,7.0 T images show superior resolution and signal-to-noise ratios than 3.0 T,which facilitate the visualization of *** automated segmentation methods could accurately identify PVS in a short time with no inter-rater *** the current study,we used U-shaped networks(U-net),a class of deep learning methods,to explore the PVS distribution in HD and *** date,PVS distribution in HD is still *** two studies have investigated PVSs in HD,and both dem-onstrated increased visible PVS burden in manifest HD compared to controls[4,5].However,whether PVS bur-den increases in premanifest HD(pre-HD)individuals remains unknown,and the relationship of PVS with cog-nitive decline has never been studied.
Neural network pruning is a popular approach to reducing the computational complexity of deep neural *** recent years,as growing evidence shows that conventional network pruning methods employ inappropriate proxy metr...
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Neural network pruning is a popular approach to reducing the computational complexity of deep neural *** recent years,as growing evidence shows that conventional network pruning methods employ inappropriate proxy metrics,and as new types of hardware become increasingly available,hardware-aware network pruning that incorporates hardware characteristics in the loop of network pruning has gained growing attention,Both network accuracy and hardware efficiency(latency,memory consumption,etc.)are critical objectives to the success of network pruning,but the conflict between the multiple objectives makes it impossible to find a single optimal *** studies mostly convert the hardware-aware network pruning to optimization problems with a single *** this paper,we propose to solve the hardware-aware network pruning problem with Multi-Objective Evolutionary Algorithms(MOEAs).Specifically,we formulate the problem as a multi-objective optimization problem,and propose a novel memetic MOEA,namely HAMP,that combines an efficient portfoliobased selection and a surrogate-assisted local search,to solve *** studies demonstrate the potential of MOEAs in providing simultaneously a set of alternative solutions and the superiority of HAMP compared to the state-of-the-art hardware-aware network pruning method.
Recently, it has been pointed out in many studies that the performance of evolutionary multi-objective optimization (EMO) algorithms can be improved by selecting solutions from all examined solutions stored in an unbo...
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Hypervolume subset selection (HSS) is a hot topic in the evolutionary multi-objective optimization (EMO) community since hypervolume is the most widely-used performance indicator. In the literature, most HSS algorithm...
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Cryo-Electron Tomography (Cryo-ET) and subtomogram averaging (STA) have been instrumental in advancing the analysis of high-resolution structural biology, enabling detailed insights into macromolecular complexes. Howe...
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Recently, it has been demonstrated that a solution set that is better than the final population can be obtained by subset selection in some studies on evolutionary multi-objective optimization. The main challenge in t...
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Cryo-electron tomography (Cryo-ET) and subtomogram averaging techniques are highly effective in revealing high-resolution molecular structures. In this technique, accurate Contrast Transfer Function (CTF) correction i...
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Performance of evolutionary multi-objective optimization (EMO) algorithms is usually evaluated using artificial test problems such as DTLZ and WFG. Every year, new EMO algorithms with high performance on those test pr...
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brain-computerinterface (BCI) control of multiple rehabilitation robots provides a novel type of human-robot interaction and an important research direction in the field of intelligent rehabilitation. However, most c...
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Research on animal robots utilizing neural electrical stimulation is a significant focus within the field of neuro-control, though precise behavior control remains challenging. This study proposes a parameter-adaptive...
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