D2^2: Decentralized Training over Decentralized Data

While training a machine learning model using multiple workers, each of which collects data from their own data sources, it would be most useful when the data collected from different workers can be {\em unique} and {\em different}. Ironically, recent analysis of decentralized parallel stochastic gradient descent (D-PSGD) relies on the assumption that the data hosted on different workers are {\em not too different}. In this paper, we ask the question: {\em Can we design a decentralized parallel stochastic gradient descent algorithm that is less sensitive to the data variance across workers?} In this paper, we present D$^2$, a novel decentralized parallel stochastic gradient descent algorithm designed for large data variance \xr{among workers} (imprecisely, "decentralized" data). The core of D$^2$ is a variance blackuction extension of the standard D-PSGD algorithm, which improves the convergence rate from $O\left({\sigma \over \sqrt{nT}} + {(n\zeta^2)^{\frac{1}{3}} \over T^{2/3}}\right)$ to $O\left({\sigma \over \sqrt{nT}}\right)$ where $\zeta^{2}$ denotes the variance among data on different workers. As a result, D$^2$ is robust to data variance among workers. We empirically evaluated D$^2$ on image classification tasks where each worker has access to only the data of a limited set of labels, and find that D$^2$ significantly outperforms D-PSGD

Blood urea nitrogen in the prediction of in-hospital mortality of patients with acute aortic dissection

Background: Blood urea nitrogen (BUN) has been shown to be associated with adverse cardiovascular disease outcomes. The aim of the present study was to evaluate the prognostic role of BUN in patients with acute aortic dissection (AAD). Hypothesis: BUN has correlation with in-hospital mortality of patients with AAD. Methods: Patients admitted to the emergency room within the first 24 h of onset of AAD were included in the study. BUN levels were measured on admission and the endpoints were mortality during hospi­talization after receiving surgical or endovascular repair. Results: A total of 192 patients with AAD were enrolled. During hospitalization, 19 patients died and 173 patients survived. Increased levels of BUN (8.9 [7.0–9.7] vs. 6.0 [5.1–7.2] mmol/L, p < 0.001) were found in non-survivors compared with those survived. Using multivariable logistic analysis, BUN was an independent predictor of in-hospital mortality in patients with AAD (OR 1.415, 95% CI 1.016–1.971, p = 0.040). Furthermore, using receiver operating characteristic analysis, the optimal cutoff value for BUN was 6.95 mmol/L. Under this value, the area under the curve was 0.785 (95% CI 0.662–0.909, p < 0.001) and the sensitivity and specificity to predict in-hospital mortality was 78.9%, and 72.2%, respectively. Conclusions: Admission BUN levels were an independent predictor for in hospital mortality in pa­tients with AAD

Physical States, Factorization and Nonlinear Structures in Two Dimensional Quantum Gravity

The nonlinear structures in 2D quantum gravity coupled to the $(q+1,q)$ minimal model are studied in the Liouville theory to clarify the factorization and the physical states. It is confirmed that the dressed primary states outside the minimal table are identified with the gravitational descendants. Using the discrete states of ghost number zero and one we construct the currents and investigate the Ward identities which are identified with the W and the Virasoro constraints. As nontrivial examples we derive the $L_0$, $L_1$ and $W_{-1}^{(3)}$ equations exactly. $L_n$ and $W^{(k)}_n$ equations are also discussed. We then explicitly show the decoupling of the edge states $O_j ~(j=0~ {\rm mod}~ q)$. We consider the interaction theory perturbed by the cosmological constant $O_1$ and the screening charge $S^+ =O_{2q+1}$. The formalism can be easily generalized to potential models other than the screening charge.Comment: 18 pages, LaTex, YITP/U-93-2

Parallel numerical simulation for a super large-scale compositional reservoir

 A compositional reservoir simulation model with ten-million grids is successfully computed using parallel processing techniques. The load balance optimization principle for parallel calculation is developed, which improves the calculation speed and accuracy, and provides a reliable basis for the design of reservoir development plan. Taking M reservoir as an example, the parallel numerical simulation study of compositional model with ten million grids is carried out. When the number of computational nodes increases, message passing processes and data exchange take much time, the proportion time of solving equation is reduced. When the CPU number increases, the creation of Jacobian matrix process has the higher acceleration ratio, and the acceleration ratio of I/O process become lower. Therefore, the I/O process is the key to improve the acceleration ratio. Finally, we study the use of GPU and CPU parallel acceleration technology to increase the calculation speed. The results show that the technology is 2.4 ∼ 5.4 times faster than CPU parallel technology. The more grids there are, the better GPU acceleration effect it has. The technology of parallel numerical simulation for compositional model with ten-million grids presented in this paper has provided the foundation for fine simulation of complex reservoirs.Cited as: Lian, P., Ji, B., Duan, T., Zhao, H., Shang, X. Parallel numerical simulation for a super large-scale compositional reservoir. Advances in Geo-Energy Research, 2019, 3(4): 381-386, doi: 10.26804/ager.2019.04.0

Human Health Indicator Prediction from Gait Video

Body Mass Index (BMI), age, height and weight are important indicators of human health conditions, which can provide useful information for plenty of practical purposes, such as health care, monitoring and re-identification. Most existing methods of health indicator prediction mainly use front-view body or face images. These inputs are hard to be obtained in daily life and often lead to the lack of robustness for the models, considering their strict requirements on view and pose. In this paper, we propose to employ gait videos to predict health indicators, which are more prevalent in surveillance and home monitoring scenarios. However, the study of health indicator prediction from gait videos using deep learning was hindered due to the small amount of open-sourced data. To address this issue, we analyse the similarity and relationship between pose estimation and health indicator prediction tasks, and then propose a paradigm enabling deep learning for small health indicator datasets by pre-training on the pose estimation task. Furthermore, to better suit the health indicator prediction task, we bring forward Global-Local Aware aNd Centrosymmetric Encoder (GLANCE) module. It first extracts local and global features by progressive convolutions and then fuses multi-level features by a centrosymmetric double-path hourglass structure in two different ways. Experiments demonstrate that the proposed paradigm achieves state-of-the-art results for predicting health indicators on MoVi, and that the GLANCE module is also beneficial for pose estimation on 3DPW

The use of High-Fat/Carbohydrate Diet-Fed and Streptozotocin-Treated Mice as a Suitable Animal Model of Type 2 Diabetes Mellitus

This study defined a mouse model of type 2 diabetes that closely simulated the development and metabolic  abnormalities of the human disease. Male C57BL/6J mice were fed with diet enriched in fat and simple carbohydrate  for 6 weeks and then injected with streptozotocin (STZ, 150 mg/kg intraperitoneally) to develop type  2 diabetes. High-fat/carbohydrate-fed mice showed similar blood glucose concentrations to chow-fed mice, but  higher insulin concentrations (P<0.01). Hyperglycemia (17.6±3.27 mmol/L) was observed in these mice after  STZ injection, and the insulin concentrations decreased to the level comparable to, or still higher than, the normal.  The model mice showed impaired glucose tolerance in the oral glucose tolerance test (OGTT), and insulin  resistance in the insulin tolerance test (ITT). Moreover, these animals had lower glycogen storage (P<0.001),  higher serum free fatty acid (P<0.001), and higher triglycerides (P<0.05) levels compared with control mice.  Furthermore, the model mice were sensitive to the glucose lowering effect of metformin. In conclusion, this  mouse model could be considered as one of the suitable animal models for type 2 diabetes mellitus, and hence  can reasonably be used for type 2 diabetes pathophysiological research and therapeutic-compound evaluation.