SIMBA: scalable inversion in optical tomography using deep denoising priors

Two features desired in a three-dimensional (3D) optical tomographic image reconstruction algorithm are the ability to reduce imaging artifacts and to do fast processing of large data volumes. Traditional iterative inversion algorithms are impractical in this context due to their heavy computational and memory requirements. We propose and experimentally validate a novel scalable iterative mini-batch algorithm (SIMBA) for fast and high-quality optical tomographic imaging. SIMBA enables highquality imaging by combining two complementary information sources: the physics of the imaging system characterized by its forward model and the imaging prior characterized by a denoising deep neural net. SIMBA easily scales to very large 3D tomographic datasets by processing only a small subset of measurements at each iteration. We establish the theoretical fixedpoint convergence of SIMBA under nonexpansive denoisers for convex data-fidelity terms. We validate SIMBA on both simulated and experimentally collected intensity diffraction tomography (IDT) datasets. Our results show that SIMBA can significantly reduce the computational burden of 3D image formation without sacrificing the imaging quality.https://arxiv.org/abs/1911.13241First author draf

Green innovation for the ecological footprints of tourism in China. Fresh evidence from ARDL approach

This study’s objective is to analyze ecological footprints that exist among China’s economic growth, energy consumption, carbon dioxide emissions, and the revenue that is generated from tourism in other countries. The years 1995 through 2020 are the focus of this particular research endeavor. The relationship between tourism and carbon emissions has been discovered by a large number of researchers; nevertheless, the findings have been inconsistent and do not give a clear picture of the situation. We can only hope that the results of the study will improve the existing body of knowledge on tourism and the quality of the surrounding environment. Throughout the whole of this investigation, the autoregressive distributed lagged (ARDL) model was used to explore both long-run and short-run estimations. A dynamic ordinary least squares (DOLS) model was used in the study to arrive at long-term estimations that could be relied upon. Even though money from tourism does not have a substantial influence on the quality of the environment in China, growth and increasing energy usage are primary donors to carbon emissions in the nation. ARDL model’s long-term projections were shown to be correct by the DOLS approach, which offered this validation. The results of the research provide fresh insights into the body of knowledge that has been accumulated on the subject of the linkage between tourism and the natural environment. Because the receipts from tourism do not have any significant negative exteriority toward the environment, energy usage is an important element of environmental degradation and policymakers should prioritize the development of the tourism sector over energy-focused manufacturing activities to maintain the growth of the nation in the upper quartiles. This is because tourismdoes not have any significant negative externalities on the environment. Sustainable tourism minimizes environmental and cultural damage while boosting profits. Developing the appropriate technology, physical infrastructure, and human capital requires money, time, and effort

Spatial Parameter Identification for MIMO Systems in the Presence of Non-Gaussian Interference

Reliable identification of spatial parameters for multiple-input multiple-output (MIMO) systems, such as the number of transmit antennas (NTA) and the direction of arrival (DOA), is a prerequisite for MIMO signal separation and detection. Most existing parameter estimation methods for MIMO systems only consider a single parameter in Gaussian noise. This paper develops a reliable identification scheme based on generalized multi-antenna time-frequency distribution (GMTFD) for MIMO systems with non-Gaussian interference and Gaussian noise. First, a new generalized correlation matrix is introduced to construct a generalized MTFD matrix. Then, the covariance matrix based on time-frequency distribution (CM-TF) is characterized by using the diagonal entries from the auto-source signal components and the non-diagonal entries from the cross-source signal components in the generalized MTFD matrix. Finally, by making use of the CM-TF, the Gerschgorin disk criterion is modified to estimate NTA, and the multiple signal classification (MUSIC) is exploited to estimate DOA for MIMO system. Simulation results indicate that the proposed scheme based on GMTFD has good robustness to non-Gaussian interference without prior information and that it can achieve high estimation accuracy and resolution at low and medium signal-to-noise ratios (SNRs)

On a kind of two-weight code

AbstractA special type of two-weight code is defined by using subcodes. The generalized Hamming weight and the chain property of this kind of two-weight code are determined. The higher-weight enumerators and an application of this kind of two-weight code are given

A computational offloading optimization scheme based on deep reinforcement learning in perceptual network

Currently, the deep integration of the Internet of Things (IoT) and edge computing has improved the computing capability of the IoT perception layer. Existing offloading techniques for edge computing suffer from the single problem of solidifying offloading policies. Based on this, combined with the characteristics of deep reinforcement learning, this paper investigates a computation offloading optimization scheme for the perception layer. The algorithm can adaptively adjust the computational task offloading policy of IoT terminals according to the network changes in the perception layer. Experiments show that the algorithm effectively improves the operational efficiency of the IoT perceptual layer and reduces the average task delay compared with other offloading algorithms

Family history of cancer is a prognostic factor for better survival in operable esophageal squamous cell carcinoma: A propensity score matching analysis

Lay summaryPatients with a family history of cancer, especially digestive tract cancer and esophageal cancer, a family history of cancer in the first degree, and more than one relative affected by cancer were associated with favorable survival when compared to those without a family history of cancer.Precis for use in the Table of ContentsA family history of cancer is a favorable independent prognostic factor in ESCC. Patients with a family history of cancer, especially digestive tract cancer and esophageal cancer, a family history of cancer in the first degree, and more than one relative affected by cancer were associated with favorable survival when compared to those without a family history of cancer.BackgroundA family history of cancer (FH) is closely associated with the risk and survival of many cancers. However, the effect of FH on the prognosis of patients with esophageal squamous cell carcinoma (ESCC) remains unclear. We performed a large cohort study in the Chinese population to obtain insight into the prognostic value of FH in patients with operable ESCC.MethodsA total of 1,322 consecutive patients with thoracic ESCC who had undergone esophagectomy between January 1997 and December 2013 were included. The FH group included patients with any degree of FH, while the non-FH group included patients without any degree of FH. In total, 215 patients with FH and 215 without FH were matched using the propensity score matching analysis method to adjust for differences in baseline variables between the two groups. The impact of FH on disease-free survival (DFS) and overall survival (OS) was estimated using the Kaplan–Meier method and Cox’s proportional hazards models.ResultsBefore matching, 280 (21.2%) patients were included in the FH group and 1,042 (78.8%) in the non-FH group. FH was associated with early pathological T stage (p = 0.001), lymph node-negative status (p = 0.022), and early pathological stage (p = 0.006). After matching, FH was an independent prognostic factor for DFS and OS in ESCC patients. Patients with FH had 35% lower risk of disease progression (hazard ratio [HR] = 0.65, 95% CI: 0.51–0.84, p = 0.001) and 34% lower risk of death (HR = 0.66, 95% CI: 0.51–0.86, p = 0.002) than those without FH. Patients with a family history of digestive tract cancer (FH-DC), a family history of esophageal cancer (FH-EC), FH in first-degree relatives (FH-FD), and more than one relative affected by cancer were associated with favorable DFS and OS as compared to those without FH.ConclusionFH is a favorable independent prognostic factor in ESCC. Patients with FH, especially those with FH-DC, FH-EC, FH-FD, and more than one relative affected by cancer, had improved survival

The Hepatoprotective Effect of Sodium Nitrite on Cold Ischemia-Reperfusion Injury

Liver ischemia-reperfusion injury is a major cause of primary graft non-function or initial function failure post-transplantation. In this study, we examined the effects of sodium nitrite supplementation on liver IRI in either Lactated Ringer's (LR) solution or University of Wisconsin (UW) solution. The syngeneic recipients of liver grafts were also treated with or without nitrite by intra-peritoneal injection. Liver AST and LDH release were significantly reduced in both nitrite-supplemented LR and UW preservation solutions compared to their controls. The protective effect of nitrite was more efficacious with longer cold preservation times. Liver histological examination demonstrated better preserved morphology and architecture with nitrite treatment. Hepatocellular apoptosis was significantly reduced in the nitrite-treated livers compared their controls. Moreover, liver grafts with extended cold preservation time of 12 to 24 hours demonstrated improved liver tissue histology and function post-reperfusion with either the nitrite-supplemented preservation solution or in nitrite-treated recipients. Interestingly, combined treatment of both the liver graft and recipient did not confer protection. Thus, nitrite treatment affords significant protection from cold ischemic and reperfusion injury to donor livers and improves liver graft acute function post-transplantation. The results from this study further support the potential for nitrite therapy to mitigate ischemia-reperfusion injury in solid organ transplantation

PD-Flow: A Point Cloud Denoising Framework with Normalizing Flows

Point cloud denoising aims to restore clean point clouds from raw observations corrupted by noise and outliers while preserving the fine-grained details. We present a novel deep learning-based denoising model, that incorporates normalizing flows and noise disentanglement techniques to achieve high denoising accuracy. Unlike existing works that extract features of point clouds for point-wise correction, we formulate the denoising process from the perspective of distribution learning and feature disentanglement. By considering noisy point clouds as a joint distribution of clean points and noise, the denoised results can be derived from disentangling the noise counterpart from latent point representation, and the mapping between Euclidean and latent spaces is modeled by normalizing flows. We evaluate our method on synthesized 3D models and real-world datasets with various noise settings. Qualitative and quantitative results show that our method outperforms previous state-of-the-art deep learning-based approaches