AutoQNN: An End-to-End Framework for Automatically Quantizing Neural Networks

Exploring the expected quantizing scheme with suitable mixed-precision policy is the key point to compress deep neural networks (DNNs) in high efficiency and accuracy. This exploration implies heavy workloads for domain experts, and an automatic compression method is needed. However, the huge search space of the automatic method introduces plenty of computing budgets that make the automatic process challenging to be applied in real scenarios. In this paper, we propose an end-to-end framework named AutoQNN, for automatically quantizing different layers utilizing different schemes and bitwidths without any human labor. AutoQNN can seek desirable quantizing schemes and mixed-precision policies for mainstream DNN models efficiently by involving three techniques: quantizing scheme search (QSS), quantizing precision learning (QPL), and quantized architecture generation (QAG). QSS introduces five quantizing schemes and defines three new schemes as a candidate set for scheme search, and then uses the differentiable neural architecture search (DNAS) algorithm to seek the layer- or model-desired scheme from the set. QPL is the first method to learn mixed-precision policies by reparameterizing the bitwidths of quantizing schemes, to the best of our knowledge. QPL optimizes both classification loss and precision loss of DNNs efficiently and obtains the relatively optimal mixed-precision model within limited model size and memory footprint. QAG is designed to convert arbitrary architectures into corresponding quantized ones without manual intervention, to facilitate end-to-end neural network quantization. We have implemented AutoQNN and integrated it into Keras. Extensive experiments demonstrate that AutoQNN can consistently outperform state-of-the-art quantization.Comment: 22 pages, 9 figures, 7 tables, Journal of Computer Science and Technolog

The roles and mechanisms of gut microbiome and metabolome in patients with cerebral infarction

As the most common type of stroke, ischemic stroke, also known as cerebral infarction (CI), with its high mortality and disability rate, has placed a huge burden on social economy and public health. Treatment methods for CI mainly include thrombectomy, thrombolysis, drug therapy, and so on. However, these treatments have certain timeliness and different side effects. In recent years, the gut-brain axis has become a hot topic, and its role in nervous system diseases has been confirmed by increasing evidences. The intestinal microbiota, as an important part of the gut-brain axis, has a non-negligible impact on the progression of CI through mechanisms such as inflammatory response and damage-associated molecular patterns, and changes in the composition of intestinal microbiota can also serve as the basis for predicting CI. At the same time, the diagnosis of CI requires more high-throughput techniques, and the analysis method of metabolomics just fits this demand. This paper reviewed the changes of intestinal microbiota in patients within CI and the effects of the intestinal microbiota on the course of CI, and summarized the therapeutic methods of the intervention with the intestinal microbiota. Furthermore, metabolic changes of CI patients were also discussed to reveal the molecular characteristics of CI and to elucidate the potential pathologic pathway of its interference

Serum BLMH and CKM as Potential Biomarkers for Predicting Therapeutic Effects of Deep Brain Stimulation in Parkinson's Disease: A Proteomics Study

Background: Deep brain stimulation (DBS) is recommended for the treatment of advanced Parkinson’s disease (PD), though individual reactions may be different. There are currently no clinically available biomarkers for predicting the responses of PD patients to DBS before surgery. This study aimed to determine serum biomarkers to predict DBS responses in PD. Methods: We profiled differentially expressed proteins (DEPs) in serum samples and identified potential biomarkers to predict the therapeutic responses to DBS in PD patients. Ten serum samples were selected from PD patients to identify DEPs via mass spectrometry proteomics; these were then verified by enzyme-linked immunosorbent assay in another 21 serum samples of PD patients. Results: The present study identified 14 DEPs (10 downregulated and four upregulated DEPs) with significantly different levels between non-responders and responders. Most of the DEPs were related to amino acid metabolism and protein modification pathways. Bleomycin hydrolase (BLMH) and creatine kinase M-type (CKM) were found to be significantly downregulated in the responders. Additionally, subsequent logistic regression and receiver operating characteristic analyses were performed to determine the diagnostic performance of candidate proteins. Conclusions: The identified DEPs show potential as biomarkers for the accurate evaluation of DBS therapeutic responses before surgery. Furthermore, assessment of serum BLMH and CKM may be particularly useful for predicting the therapeutic responses to DBS in PD patients

Effects of Damage to the Integrity of the Left Dual-Stream Frontotemporal Network Mediated by the Arcuate Fasciculus and Uncinate Fasciculus on Acute/Subacute Post-Stroke Aphasia

(1) Background: To investigate the correlation between the integrity of the left dual-stream frontotemporal network mediated by the arcuate fasciculus (AF) and uncinate fasciculus (UF), and acute/subacute post-stroke aphasia (PSA). (2) Methods: Thirty-six patients were recruited and received both a language assessment and a diffusion tensor imaging (DTI) scan. Correlations between diffusion indices in the bilateral LSAF/UF and language performance assessment were analyzed with correlation analyses. Multiple linear regression analysis was also implemented to investigate the effects of the integrity of the left LSAF/UF on language performance. (3) Results: Correlation analyses showed that the diffusion indices, including mean fractional anisotropy (FA) values and the fiber number of the left LSAF rather than the left UF was significantly positively associated with language domain scores (p p > 0.05). (4) Conclusions: The integrity of the left LSAF, but not the UF, might play important roles in supporting residual language ability in individuals with acute/subacute PSA; simultaneous disruption of the dual-stream frontotemporal network mediated by the left LSAF and UF would not result in more severe aphasia than damage to either pathway alone