Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks

It is desirable to train convolutional networks (CNNs) to run more efficiently during inference. In many cases however, the computational budget that the system has for inference cannot be known beforehand during training, or the inference budget is dependent on the changing real-time resource availability. Thus, it is inadequate to train just inference-efficient CNNs, whose inference costs are not adjustable and cannot adapt to varied inference budgets. We propose a novel approach for cost-adjustable inference in CNNs - Stochastic Downsampling Point (SDPoint). During training, SDPoint applies feature map downsampling to a random point in the layer hierarchy, with a random downsampling ratio. The different stochastic downsampling configurations known as SDPoint instances (of the same model) have computational costs different from each other, while being trained to minimize the same prediction loss. Sharing network parameters across different instances provides significant regularization boost. During inference, one may handpick a SDPoint instance that best fits the inference budget. The effectiveness of SDPoint, as both a cost-adjustable inference approach and a regularizer, is validated through extensive experiments on image classification

MiR-155 protects against sepsis-induced cardiomyocyte apoptosis via activation of NO/cGMP signaling pathway by eNOS

Purpose: To examine the impact of miR-155 on sepsis-induced myocardial apoptosis and heart failure, and to explore its molecular mechanism. Methods: Mice were divided into four groups and septic myocardial dysfunction was induced by intraperitoneal injection of lipopolysaccharide (LPS, 5 mg/kg). The LPS stimulation expression of miR-155 levels was determined by real time-polymerase chain reaction (RT-PCR). In vivo, echocardiography and TUNEL staining were used to investigate the effects of miR-155 in inhibiting cardiac function and myocardial apoptosis. Changes in the expression of eNOS when miR-155 was overexpressed or inhibited were determined by RT-PCR, while double luciferase gene assay assessed the relationship between eNOS and miR-155, eNOS, expression of iNOS, SGC alpha 1, and PKG protein. Results: MiR-155 was significantly increased after LPS stimulation (p < 0.01). In vitro, the inhibition of miR-155 by antagomiR significantly down-regulated the apoptosis of cardiomyocytes (p < 0.05), while overexpression of miR-155 by agomiR significantly up-regulated the apoptosis of cardiomyocytes (p < 0.05). In vivo, ejection fraction, fractional shortening and heart weight were significantly increased (p < 0.05), while apoptosis was significantly decreased (p < 0.05). MiR-155 negatively regulated the expression of eNOS (p < 0.01), and targeted the expression of eNOS mRNA (p < 0.001). In addition, the expression of eNOS, sGCα1 and PKA were significantly up-regulated (p < 0.05), while the expression of iNOS was significantly down-regulated (p < 0.05) after the inhibition of miR-155 in LPS mouse model. Conclusion: MiR-155 regulates sepsis-induced cardiomyocyte apoptosis and heart failure through eNOS /NO/cGMP signaling pathway. Thus, these findings can potentially facilitate the development of an effective strategy for management of heart failure

A Fully Data-Driven Approach for Realistic Traffic Signal Control Using Offline Reinforcement Learning

The optimization of traffic signal control (TSC) is critical for an efficient transportation system. In recent years, reinforcement learning (RL) techniques have emerged as a popular approach for TSC and show promising results for highly adaptive control. However, existing RL-based methods suffer from notably poor real-world applicability and hardly have any successful deployments. The reasons for such failures are mostly due to the reliance on over-idealized traffic simulators for policy optimization, as well as using unrealistic fine-grained state observations and reward signals that are not directly obtainable from real-world sensors. In this paper, we propose a fully Data-Driven and simulator-free framework for realistic Traffic Signal Control (D2TSC). Specifically, we combine well-established traffic flow theory with machine learning to construct a reward inference model to infer the reward signals from coarse-grained traffic data. With the inferred rewards, we further propose a sample-efficient offline RL method to enable direct signal control policy learning from historical offline datasets of real-world intersections. To evaluate our approach, we collect historical traffic data from a real-world intersection, and develop a highly customized simulation environment that strictly follows real data characteristics. We demonstrate through extensive experiments that our approach achieves superior performance over conventional and offline RL baselines, and also enjoys much better real-world applicability.Comment: 15 pages, 6 figure

Effect of a Management Algorithm for Wet Contamination of Peritoneal Dialysis System on the Prevention of Peritonitis: A Prospective Observational Study

Introduction: Wet contamination was a common problem of peritoneal dialysis (PD) system. We developed a management algorithm for wet contamination of PD system (wet contamination) on the basis of the related research literature and clinical practice experience. The purpose of this study was to observe clinical effect of the management algorithm on the prevention of peritonitis. Methods: Patients treated wet contamination in a single PD center between October 2017 and September 2022 were included. A management algorithm was established to treat wet contamination. It comprised identification of the contamination type, addressing contaminated or aging catheters, prophylactic antibiotics, and retraining. Demographic data and clinical data about wet contamination were collected and compared. Results: One hundred and forty-one cases of wet contamination were included in this study. The mean age was 51.7 ± 14.1 years, and 49.6% were female. The proportion of diabetic nephropathy was 9.9%. The median PD duration was 27.0 (1.7–79.7) months. Eighteen episodes (12.8%) of wet contamination-associated peritonitis developed after wet contamination. The main pathogenic bacteria of peritonitis were Gram-positive bacteria (33.3%) and Gram-negative bacteria (27.8%). The incidence of wet contamination-associated peritonitis in the compliance with the management algorithm group was significantly lower than that in the non-compliance with the management algorithm group (0.9 vs. 48.6%; p < 0.001). Non-compliance with management algorithm (OR = 185.861, p < 0.001) together with advance age (OR = 1.116, p < 0.001) and longer distance from home to hospital (OR = 1.007, p < 0.001) were independent risk factors for wet contamination-associated peritonitis. Conclusion: The management algorithm for wet contamination of PD system could reduce the risk of peritonitis

Identification of Competing Endogenous RNA Regulatory Networks in Vitamin A Deficiency-Induced Congenital Scoliosis by Transcriptome Sequencing Analysis

Background/Aims: Congenital scoliosis (CS) is a result of anomalous development of vertebrae and is frequently associated with somitogenesis malformation. Although noncoding RNAs (ncRNAs) have been recently determined to be involved in the pathogenesis of CS, the competing endogenous RNA (ceRNA) regulatory networks in CS remain largely unknown. Methods: Sequencing was conducted to explore the ncRNA expression profiles in rat embryos (gestation day 9) following vitamin A deficiency (VAD) (n = 9 for the vitamin A deficiency-induced congenital scoliosis (VAD-CS) group and n = 4 for the control group). Real-time reverse transcriptase polymerase chain reaction (RT-PCR) was conducted to verify the expression levels of selected mRNAs, long noncoding RNAs (lncRNAs), circular RNAs (circRNAs), and microRNAs (miRNAs). Bioinformatics analysis was used to discover the possible relationships and functions of the ceRNAs. Results: A total of 749 mRNAs, 56 miRNAs, 685 lncRNAs, and 70 circRNAs were identified to have significantly different expression levels in the two groups. Wnt, PI3K-ATK, FoxO, EGFR, and mTOR were found to be the most significant pathways involved in VAD-CS pathogenesis. The circRNA/miRNA/mRNA and lncRNA/miRNA/mRNA networks of CS were built, and the gene expression mechanisms regulated by ncRNAs were unveiled via the ceRNA regulatory networks. Conclusion: We comprehensively identified ceRNA regulatory networks of embryonic somite development in VAD-CS as well as revealed the contribution of different ncRNA expression profiles. Our data demonstrate the association between mRNAs and ncRNAs in the pathogenic mechanism of CS

AI-driven blind signature classification for IoT connectivity: a deep learning approach

Non-orthogonal multiple access (NOMA) promises to fulfill the fast-growing connectivities in future Internet of Things (IoT) using abundant multiple-access signatures. While explicitly notifying the utilized NOMA signatures causes large signaling cost, blind signature classification naturally becomes a low-cost option. To accomplish signature classification for NOMA, we study both likelihood- and feature-based methods. A likelihood-based method is firstly proposed and showed to be optimal in the asymptotic limit of the observations, despite high computational complexity. While feature-based classification methods promise low complexity, efficient features are non-trivial to be manually designed. To this end, we resort to artificial intelligence (AI) for deep learning-based automatic feature extraction. Specifically, our proposed deep neural network for signature classification, namely DeepClassifier, establishes on the insights gained from the likelihood-based method, which contains two stages to respectively deal with a single observation and aggregate the classification results of an observation sequence. The first stage utilizes an iterative structure where each layer employs a memory-extended network to explicitly exploit the knowledge of signature pool. The second stage incorporates the straight-through channels within a deep recurrent structure to avoid information loss of previous observations. Experiments show that DeepClassifier approaches the optimal likelihood-based method with a reduction of 90% complexity

Study on the construction deformation of a slotted shield in loess tunnels with different buried depths and large sections

Since there is no precedent for the use of slotted shield tunneling in the large section of high-speed railways in China, the relevant technological accumulation and systematic research achievements are few. Therefore, this paper provides theoretical support for loess tunnel construction decision-making through the study of slotted shields and is expected to promote the mechanization and even intelligent construction of a high-speed iron-loess tunnel. Taking the Luochuan tunnel of the Xiyan high-speed railway as the engineering background, this paper uses the numerical simulation software packages of ANSYS and FLAC3D to study the tunnel deformation (surface settlement, vault settlement, tunnel bottom uplift, and horizontal convergence) caused by the slotted shield construction in three different buried depths of 30, 40, and 50 m surrounding rock. The deformation law and mechanical characteristics of a cutter shield construction of large cross-section loess tunnels under the influence of different buried depths are put forward. Results showed that 1) the mutual interference between the working procedures can be significantly reduced by inserting the cutting tool into the soil instead of the advanced tubule before excavation; 2) the settlement in the upper part of the longitudinal axis of the tunnel is the largest; the greater the depth of the tunnel is, the smaller the surface settlement is; and 3) the horizontal deformation of the arch waist and foot of the tunnel under different buried depths is symmetrically distributed into the tunnel during the whole process of slotted shield tunneling