The stabilities and electronic/band structures of single-layer bismuth oxyhalides have been investigated by employing first-principles calculations. The results indicate that the single-layer bismuth oxyhalide materials, except for BiOF, have robust energetic and dynamical stabilities because of their low formation energies and the absence of imaginary frequencies within the entire Brillouin zone. Furthermore, calculations of the electronic structures and optical absorptions indicate that single-layer BiOI possesses a favorable band gap, suitable band edge positions, different orbital characteristics and different effective masses at the valence band maximum (VBM) and conduction band minimum (CBM), thus presenting excellent photocatalytic activity for water splitting. Moreover, the resulting compressive strains can shift the band edge positions of the single-layer materials to more suitable places to enhance their photocatalytic activities.

We aimed to evaluate the value of sentinel lymph node contrast-enhanced ultrasound (SLN-CEUS) and surface tracing for the biopsy of intra-operative sentinel lymph nodes (SLNs). Between June 2015 and December 2017, a total of 453 patients with early invasive breast cancer were recruited. Patients received an intradermal injection of microbubble contrast agent around the areola on the day before surgery. The locations and sizes of lymphatic channels (LCs) and SLNs were marked on the body surface using gentian violet. Then, injection of double blue dye was performed half an hour before surgery. We compared the pathway of LCs and the location of SLNs obtained from SLN-CEUS and blue dye during surgery. Among the 453 patients, the mean numbers of LCs and SLNs detected by SLN-CEUS were 1.42 and 1.72, respectively, and the coincidence rate was 98.2% compared with blue dye during surgery. The median distance from the SLN to skin measured by preoperative CEUS and blue dye was 1.95 § 0.69 and 2.03 § 0.87 cm (p = 0.35). There were three SLN enhancement in our research, including homogeneous enhancement, inhomogeneous enhancement and no enhancement, with the sensitivity, specificity, positive predictive value and negative predictive value of SLN-CEUS for the diagnosis of SLNs being 96.82%, 91.91%, 87.54% and 98.01%, respectively. SLN-CEUS with skin marking can identify the pathway of LCs and the location of the SLN before surgery, measure the distance from the SLN to skin and determine if the SLN is metastatic. SLN-CEUS can be used as an effective complement to the blue dye method.

A damage degree identification method based on high-order difference mathematical morphology gradient spectrum entropy (HMGSEDI) is proposed in this paper to solve the problem that fault signal of rolling bearings are weak and difficult to be quantitatively measured. In the HMGSEDI method, on the basis of mathematical morphology gradient spectrum and spectrum entropy, the changing scale influence of structure elements to damage degree identification is thoroughly analyzed to determine its optimal scale range. The high-order difference mathematical morphology gradient spectrum entropy is then defined in order to quantitatively describe the fault damage degree of bearing. The discrimination concept of fault damage degree is defined to quantitatively describe the difference between the high-order differential mathematical entropy and the general mathematical morphology entropy in order to propose a fault damage degree identification method. The vibration signal of motors under no-load and load states are used to testify the effectiveness of the proposed HMGSEDI method. The experiment shows that high-order differential mathematical morphology entropy can more effectively identify the fault damage degree of bearings and the identification accuracy of fault damage degree can be greatly improved. Therefore, the HMGSEDI method is an effective quantitative fault damage degree identification method, and provides a new way to identify fault damage degree and fault prediction of rotating machinery.

Many recent machine learning models rely on fine-grained dynamic control flow for training and inference. In particular, models based on recurrent neural networks and on reinforcement learning depend on recurrence relations, data-dependent conditional execution, and other features that call for dynamic control flow. These applications benefit from the ability to make rapid control-flow decisions across a set of computing devices in a distributed system. For performance, scalability, and expressiveness, a machine learning system must support dynamic control flow in distributed and heterogeneous environments. This paper presents a programming model for distributed machine learning that supports dynamic control flow. We describe the design of the programming model, and its implementation in TensorFlow, a distributed machine learning system. Our approach extends the use of dataflow graphs to represent machine learning models, offering several distinctive features. First, the branches of conditionals and bodies of loops can be partitioned across many machines to run on a set of heterogeneous devices, including CPUs, GPUs, and custom ASICs. Second, programs written in our model support automatic differentiation and distributed gradient computations, which are necessary for training machine learning models * Work done primarily at Google Brain. that use control flow. Third, our choice of non-strict semantics enables multiple loop iterations to execute in parallel across machines, and to overlap compute and I/O operations.We have done our work in the context of TensorFlow, and it has been used extensively in research and production. We evaluate it using several real-world applications, and demonstrate its performance and scalability.