Fast Window-Based Event Denoising with Spatiotemporal Correlation Enhancement.

Previous deep learning-based event denoising methods mostly suffer from poor interpretability and difficulty in real-time processing due to their complex architecture designs. In this paper, we propose window-based event denoising, which simultaneously deals with a stack of events while existing element-based denoising focuses on one event each time. Besides, we give the theoretical analysis based on probability distributions in both temporal and spatial domains to improve interpretability.

Source-free collaborative domain adaptation via multi-perspective feature enrichment for functional MRI analysis.

Resting-state functional MRI (rs-fMRI) is increasingly employed in multi-site research to analyze neurological disorders, but there exists cross-site/domain data heterogeneity caused by site effects such as differences in scanners/protocols. Existing domain adaptation methods that reduce fMRI heterogeneity generally require accessing source domain data, which is challenging due to privacy concerns and/or data storage burdens. To this end, we propose a source-free collaborative domain adaptation (SCDA) framework using only a pretrained source model and unlabeled target data.

Photoprogrammed Multifunctional Optoelectronic Synaptic Transistor Arrays Based on Photosensitive Polymer-Sorted Semiconducting Single-Walled Carbon Nanotubes for Image Recognition.

The development of neuromorphic optoelectronic systems opens up the possibility of the next generation of artificial vision. In this work, the novel broadband (from 365 to 940 nm) and multilevel storage optoelectronic synaptic thin-film transistor (TFT) arrays are reported using the photosensitive conjugated polymer (poly[(9,9-dioctylfluorenyl-2,7-diyl)-co-(bithiophene)], F8T2) sorted semiconducting single-walled carbon nanotubes (sc-SWCNTs) as channel materials. The broadband synaptic responses are inherited to absorption from both photosensitive F8T2 and sorted sc-SWCNTs, and the excellent optoelectronic synaptic behaviors with 200 linearly increasing conductance states and long retention time > 10 s are attributed to the superior charge trapping at the AlO dielectric layer grown by atomic layer deposition.

Hybrid Multimodality Fusion with Cross-Domain Knowledge Transfer to Forecast Progression Trajectories in Cognitive Decline.

Magnetic resonance imaging (MRI) and positron emission tomography (PET) are increasingly used to forecast progression trajectories of cognitive decline caused by preclinical and prodromal Alzheimer's disease (AD). Many existing studies have explored the potential of these two distinct modalities with diverse machine and deep learning approaches. But successfully fusing MRI and PET can be complex due to their unique characteristics and missing modalities.

Brain Anatomy-Guided MRI Analysis for Assessing Clinical Progression of Cognitive Impairment with Structural MRI.

Brain structural MRI has been widely used for assessing future progression of cognitive impairment (CI) based on learning-based methods. Previous studies generally suffer from the limited number of labeled training data, while there exists a huge amount of MRIs in large-scale public databases. Even without task-specific label information, brain anatomical structures provided by these MRIs can be used to boost learning performance intuitively.

Aberrant dynamic functional network connectivity in type 2 diabetes mellitus individuals.

An increasing number of recent brain imaging studies are dedicated to understanding the neuro mechanism of cognitive impairment in type 2 diabetes mellitus (T2DM) individuals. In contrast to efforts to date that are limited to static functional connectivity, here we investigate abnormal connectivity in T2DM individuals by characterizing the time-varying properties of brain functional networks. Using group independent component analysis (GICA), sliding-window analysis, and k-means clustering, we extracted thirty-one intrinsic connectivity networks (ICNs) and estimated four recurring brain states.

Phenotypic and comparative transcriptomics analysis of RDS1 overexpression reveal tolerance of Saccharomyces cerevisiae to furfural.

The yeast Saccharomyces cerevisiae able to tolerate lignocellulose-derived inhibitors like furfural. Yeast strain performance tolerance has been measured by the length of the lag phase for cell growth in response to the furfural inhibitor challenge. The aims of this work were to obtain RDS1 yeast tolerant strain against furfural through overexpression using a method of in vivo homologous recombination.

Surface defect detection competition with a bio-inspired vision sensor.

This paper reports the background and results of the Surface Defect Detection Competition with Bio-inspired Vision Sensor, as well as summarizes the champion solutions, current challenges and future directions.

Contribution of Overexpression to the Resistance of BY4742 Strain to Furfural Inhibitor.

Lignocellulosic biomass is still considered a feasible source of bioethanol production. can adapt to detoxify lignocellulose-derived inhibitors, including furfural. Tolerance of strain performance has been measured by the extent of the lag phase for cell proliferation following the furfural inhibitor challenge.

Adaptive Search-and-Training for Robust and Efficient Network Pruning.

Both network pruning and neural architecture search (NAS) can be interpreted as techniques to automate the design and optimization of artificial neural networks. In this paper, we challenge the conventional wisdom of training before pruning by proposing a joint search-and-training approach to learn a compact network directly from scratch. Using pruning as a search strategy, we advocate three new insights for network engineering: 1) to formulate adaptive search as a cold start strategy to find a compact subnetwork on the coarse scale; and 2) to automatically learn the threshold for network pruning; 3) to offer flexibility to choose between efficiency and robustness.

Deep Gaussian Scale Mixture Prior for Image Reconstruction.

Image reconstruction from partial observations has attracted increasing attention. Conventional image reconstruction methods with hand-crafted priors often fail to recover fine image details due to the poor representation capability of the hand-crafted priors. Deep learning methods attack this problem by directly learning mapping functions between the observations and the targeted images can achieve much better results.

Searching Efficient Model-guided Deep Network for Image Denoising.

Unlike the success of neural architecture search (NAS) in high-level vision tasks, it remains challenging to find computationally efficient and memory-efficient solutions to low-level vision problems such as image restoration through NAS. One of the fundamental barriers to differential NAS-based image restoration is the optimization gap between the super-network and the sub-architectures, causing instability during the searching process. In this paper, we present a novel approach to fill this gap in image denoising application by connecting model-guided design (MoD) with NAS (MoD-NAS).

Detecting type 2 diabetes mellitus cognitive impairment using whole-brain functional connectivity.

Type 2 diabetes mellitus (T2DM) is closely linked to cognitive decline and alterations in brain structure and function. Resting-state functional magnetic resonance imaging (rs-fMRI) is used to diagnose neurodegenerative diseases, such as cognitive impairment (CI), Alzheimer's disease (AD), and vascular dementia (VaD). However, whether the functional connectivity (FC) of patients with T2DM and mild cognitive impairment (T2DM-MCI) is conducive to early diagnosis remains unclear.

Altered cortical thickness, degree centrality, and functional connectivity in middle-age type 2 diabetes mellitus.

Purpose: This study aimed to investigate the changes in brain structure and function in middle-aged patients with type 2 diabetes mellitus (T2DM) using morphometry and blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI).

Methods: A total of 44 middle-aged patients with T2DM and 45 matched healthy controls (HCs) were recruited. Surface-based morphometry (SBM) was used to evaluate the changes in brain morphology.

Cortical gray matter microstructural alterations in patients with type 2 diabetes mellitus.

Background And Purpose: Neurodegenerative processes are widespread in the brains of type 2 diabetes mellitus (T2DM) patients; gaps remain to exist in the current knowledge of the associated gray matter (GM) microstructural alterations.

Methods: A cross-sectional study was conducted to investigate alterations in GM microarchitecture in T2DM patients by diffusion tensor imaging and neurite orientation dispersion and density imaging (NODDI). Seventy-eight T2DM patients and seventy-four age-, sex-, and education level-matched healthy controls (HCs) without cognitive impairment were recruited.

Deep Posterior Distribution-Based Embedding for Hyperspectral Image Super-Resolution.

In this paper, we investigate the problem of hyperspectral (HS) image spatial super-resolution via deep learning. Particularly, we focus on how to embed the high-dimensional spatial-spectral information of HS images efficiently and effectively. Specifically, in contrast to existing methods adopting empirically-designed network modules, we formulate HS embedding as an approximation of the posterior distribution of a set of carefully-defined HS embedding events, including layer-wise spatial-spectral feature extraction and network-level feature aggregation.

Convolutional Neural Networks for Classification of T2DM Cognitive Impairment Based on Whole Brain Structural Features.

Purpose: Cognitive impairment is generally found in individuals with type 2 diabetes mellitus (T2DM). Although they may not have visible symptoms of cognitive impairment in the early stages of the disorder, they are considered to be at high risk. Therefore, the classification of these patients is important for preventing the progression of cognitive impairment.

Altered Functional Network Connectivity of Precuneus and Executive Control Networks in Type 2 Diabetes Mellitus Without Cognitive Impairment.

In epidemiological studies, type 2 diabetes mellitus (T2DM) has been associated with cognitive impairment and dementia, but studies about functional network connectivity in T2DM without cognitive impairment are limited. This study aimed to explore network connectivity alterations that may help enhance our understanding of damage-associated processes in T2DM. MRI data were analyzed from 82 patients with T2DM and 66 normal controls.

Fine-Grained Image Quality Caption With Hierarchical Semantics Degradation.

Blind image quality assessment (BIQA), which is capable of precisely and automatically estimating human perceived image quality with no pristine image for comparison, attracts extensive attention and is of wide applications. Recently, many existing BIQA methods commonly represent image quality with a quantitative value, which is inconsistent with human cognition. Generally, human beings are good at perceiving image quality in terms of semantic description rather than quantitative value.

Myeloarchitectonic plasticity in elite golf players' brains.

Human neuroimaging studies have demonstrated that exercise influences the cortical structural plasticity as indexed by gray or white matter volume. It remains elusive, however, whether exercise affects cortical changes at the finer-grained myelination structure level. To answer this question, we scanned 28 elite golf players in comparison with control participants, using a novel neuroimaging technique-quantitative magnetic resonance imaging (qMRI).

Contrastive Self-Supervised Pre-Training for Video Quality Assessment.

Video quality assessment (VQA) task is an ongoing small sample learning problem due to the costly effort required for manual annotation. Since existing VQA datasets are of limited scale, prior research tries to leverage models pre-trained on ImageNet to mitigate this kind of shortage. Nonetheless, these well-trained models targeting on image classification task can be sub-optimal when applied on VQA data from a significantly different domain.

Superhydrophilic polyethersulfone (PES) membranes with high scale inhibition properties obtained through bionic mineralization and RTIPS.

Reverse thermally induced separation (RTIPS) was used to obtain a separation membrane with a better internal structure for a higher water flux and a surface that could easily form a hydration layer. In comparison to the traditional modification method, this work focused on the aspect that the internal structure obtained by changing the membrane-making method provided easier adhesion conditions for the dopamine/TiO hybrid nanoparticles (DA/TiO HNPs) obtained by biomimetic mineralization. It provided a basis for exploring the variation in adhesion with the water bath temperature and the amount of titanium added through the study of turbidity point, SEM images, water contact angle, thermogravimetric test, EDX, AFM, XPS, FTIR and other test results.

Altered Gray Matter Volume, Functional Connectivity, and Degree Centrality in Early-Onset Type 2 Diabetes Mellitus.

Structural and functional brain alterations that underlie cognitive decline have been observed in elderly adults with type 2 diabetes mellitus (T2DM); however, whether these alterations can be observed in patients with early-onset T2DM remains unclear. Therefore, we aimed to describe the abnormalities in brain volume and functional patterns in patients with early-onset T2DM in the present study. We enrolled 20 patients with early-onset T2DM and 20 healthy controls (HCs).