MegDet: A Large Mini-Batch Object Detector

The improvements in recent CNN-based object detection works, from R-CNN [11], Fast/Faster R-CNN [10, 31] to recent Mask R-CNN [14] and RetinaNet [24], mainly come from new network, new framework, or novel loss design. But mini-batch size, a key factor in the training, has not been well studied. In this paper, we propose a Large MiniBatch Object Detector (MegDet) to enable the training with much larger mini-batch size than before (e.g. from 16 to 256), so that we can effectively utilize multiple GPUs (up to 128 in our experiments) to significantly shorten the training time. Technically, we suggest a learning rate policy and Cross-GPU Batch Normalization, which together allow us to successfully train a large mini-batch detector in much less time (e.g., from 33 hours to 4 hours), and achieve even better accuracy. The MegDet is the backbone of our submission (mmAP 52.5%) to COCO 2017 Challenge, where we won the 1st place of Detection task

Dimensionality Reduction in Deep Learning for Chest X-Ray Analysis of Lung Cancer

Efficiency of some dimensionality reduction techniques, like lung segmentation, bone shadow exclusion, and t-distributed stochastic neighbor embedding (t-SNE) for exclusion of outliers, is estimated for analysis of chest X-ray (CXR) 2D images by deep learning approach to help radiologists identify marks of lung cancer in CXR. Training and validation of the simple convolutional neural network (CNN) was performed on the open JSRT dataset (dataset #01), the JSRT after bone shadow exclusion - BSE-JSRT (dataset #02), JSRT after lung segmentation (dataset #03), BSE-JSRT after lung segmentation (dataset #04), and segmented BSE-JSRT after exclusion of outliers by t-SNE method (dataset #05). The results demonstrate that the pre-processed dataset obtained after lung segmentation, bone shadow exclusion, and filtering out the outliers by t-SNE (dataset #05) demonstrates the highest training rate and best accuracy in comparison to the other pre-processed datasets.Comment: 6 pages, 14 figure

On-line near-infrared spectroscopy optimizing and monitoring biotransformation process of γ-aminobutyric acid

AbstractNear-infrared spectroscopy (NIRS) with its fast and nondestructive advantages can be qualified for the real-time quantitative analysis. This paper demonstrates that NIRS combined with partial least squares (PLS) regression can be used as a rapid analytical method to simultaneously quantify l-glutamic acid (l-Glu) and γ-aminobutyric acid (GABA) in a biotransformation process and to guide the optimization of production conditions when the merits of NIRS are combined with response surface methodology. The high performance liquid chromatography (HPLC) reference analysis was performed by the o-phthaldialdehyde pre-column derivatization. NIRS measurements of two batches of 141 samples were firstly analyzed by PLS with several spectral pre-processing methods. Compared with those of the HPLC reference analysis, the resulting determination coefficients (R2), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) of the external validation for the l-Glu concentration were 99.5%, 1.62g/L, and 11.3, respectively. For the GABA concentration, R2, RMSEP, and RPD were 99.8%, 4.00g/L, and 16.4, respectively. This NIRS model was then used to optimize the biotransformation process through a Box-Behnken experimental design. Under the optimal conditions without pH adjustment, 200g/L l-Glu could be catalyzed by 7148 U/L glutamate decarboxylase (GAD) to GABA, reaching 99% conversion at the fifth hour. NIRS analysis provided timely information on the conversion from l-Glu to GABA. The results suggest that the NIRS model can not only be used for the routine profiling of enzymatic conversion, providing a simple and effective method of monitoring the biotransformation process of GABA, but also be considered to be an optimal tool to guide the optimization of production conditions

Effects of chronic liver disease on the outcomes of simultaneous resection of colorectal cancer with synchronous liver metastases: a propensity score matching study

IntroductionGiven the rising prevalence of chronic liver disease (CLD), it is increasingly important to understand its impact on surgical outcomes. Our aim was to evaluate the impact of CLD on short-term outcomes in patients with colorectal cancer and synchronous liver metastases undergoing simultaneous surgery.MethodsWe retrospectively reviewed patients with colorectal cancer and liver metastases who underwent simultaneous resection between January 2013 and June 2022. Patients were divided into the CLD and non-CLD groups. Data regarding short-term surgical outcomes were compared between the two groups.ResultsA total of 187 patients were included. After propensity score matching, there were 42 patients in each group, and the basic characteristics of the two groups were similar. Patients with CLD had a significantly greater incidence of postoperative complications (47.6% vs. 26.2%; P = 0.042). The operation times of the CLD and non-CLD groups were similar (297 vs. 307.5 min, P = 0.537), and the blood loss was comparable between the two groups (250 vs. 155 ml, P = 0.066). No significant differences were observed between the two groups in pneumonia (P > 0.999), urinary infection rate (P > 0.999), ileus rate (P = 0.474), wound infection rates (P > 0.999), abdominal infection rate (P = 0.533), anastomotic leakage rate (P > 0.999), digestive hemorrhage rate (P > 0.999), bile leakage rate (P > 0.999), hepatic hemorrhage rate (P > 0.999), reoperation rate (P > 0.999), intensive care rate (P > 0.999), or severe liver failure (P > 0.999). There were no deaths in the two groups. CLD significantly prolonged the length of hospital stay (P = 0.011).DiscussionCLD is an important factor affecting postoperative complications in patients with colorectal cancer liver metastases undergoing simultaneous surgery. Considering the large number of patients with CLD in China, more attention and medical care should be provided to patients with CLD who require simultaneous resection of colorectal cancer with synchronous liver metastases

Kinetic and thermodynamic investigations of CO2 gasification of coal chars prepared via conventional and microwave pyrolysis

This study examined an isothermal CO2 gasification of four chars prepared via two different methods, i.e., conventional and microwave-assisted pyrolysis, by the approach of thermogravimetric analysis. Physical, chemical, and structural behaviours of chars were examined using ultimate analysis, X-ray diffraction, and scanning electronic microscopy. Kinetic parameters were calculated by applying the shrinking unreacted core (SCM) and random pore (RPM) models. Moreover, char-CO2 gasification was further simulated by using Aspen Plus to investigate thermodynamic performances in terms of syngas composition and cold gas efficiency (CGE). The microwave-induced char has the largest C/H mass ratio and most ordered carbon structure, but the smallest gasification reactivity. Kinetic analysis indicates that the RPM is better for describing both gasification conversion and reaction rates of the studied chars, and the activation energies and pre-exponential factors varied in the range of 78.45–194.72 kJ/mol and 3.15–102,231.99 s−1, respectively. In addition, a compensation effect was noted during gasification. Finally, the microwave-derived char exhibits better thermodynamic performances than the conventional chars, with the highest CGE and CO molar concentration of 1.30% and 86.18%, respectively. Increasing the pyrolysis temperature, gasification temperature, and CO2-to-carbon molar ratio improved the CGE