Learning When to Concentrate or Divert Attention: Self-Adaptive Attention Temperature for Neural Machine Translation

Most of the Neural Machine Translation (NMT) models are based on the sequence-to-sequence (Seq2Seq) model with an encoder-decoder framework equipped with the attention mechanism. However, the conventional attention mechanism treats the decoding at each time step equally with the same matrix, which is problematic since the softness of the attention for different types of words (e.g. content words and function words) should differ. Therefore, we propose a new model with a mechanism called Self-Adaptive Control of Temperature (SACT) to control the softness of attention by means of an attention temperature. Experimental results on the Chinese-English translation and English-Vietnamese translation demonstrate that our model outperforms the baseline models, and the analysis and the case study show that our model can attend to the most relevant elements in the source-side contexts and generate the translation of high quality.Comment: To appear in EMNLP 201

SEISMIC ANALYSIS OF NUCLEAR POWER PLANT CANNED MOTOR PUMP UNIT BASED ON INTEGRAL CALCULATION METHOD

The canned motor pump is a device in one of the most important loops in the nuclear power plant system and key technology research project, of which the seismic requirements shall be checked by Category A. It is required that the structural integrity and electric drive assembly performability of the unit can be ensured during or after operating basic earthquake (OBE) or safe shutdown earthquake (SSE). The author uses Ansys software workbench module to carry out appearance-based three-dimensional modeling, finite element meshing, intrinsic mode analysis, and carry out structural overall element analysis and calculation considering dead weight load and earthquake spectrum load. The results show that the unit major structure rotary and static parts, gear system, bearing parts, bolt and screw strengths meet the requirements and the structure maintains integrity, the relative deformation of the unit rotary and static parts shall be less than the specified value of gap among them, so as to keep the performability and not interfere with the operation. The appearance–based seismic analysis method not only can ensure the calculation accuracy, but also can greatly reduce the workload in calculation and checking, has a certain learning value

Enhanced Multiple-Object Tracking Using Delay Processing and Binary-Channel Verification

Tracking objects over time, i.e., identity (ID) consistency, is important when dealing with multiple object tracking (MOT). Especially in complex scenes with occlusion and interaction of objects this is challenging. Significant improvements in single object tracking (SOT) methods have inspired the introduction of SOT to MOT to improve the robustness, that is, maintaining object identities as long as possible, as well as helping alleviate the limitations from imperfect detections. SOT methods are constantly generalized to capture appearance changes of the object, and designed to efficiently distinguish the object from the background. Hence, simply extending SOT to a MOT scenario, which consists of a complex scene with spatially mixed, occluded, and similar objects, will encounter problems in computational efficiency and drifted results. To address this issue, we propose a binary-channel verification model that deeply excavates the potential of SOT in refining the representation while maintaining the identities of the object. In particular, we construct an integrated model that jointly processes the previous information of existing objects and new incoming detections, by using a unified correlation filter through the whole process to maintain consistency. A delay processing strategy consisting of the three parts - attaching, re-initialization, and reclaiming - is proposed to tackle drifted results caused by occlusion. Avoiding the fuzzy appearance features of complex scenes in MOT, this strategy can improve the ability to distinguish specific objects from each other without contaminating the fragile training space of a single object tracker, which is the main cause of the drift results. We demonstrate the effectiveness of our proposed approach on the MOT17 challenge benchmarks. Our approach shows better overall ID consistency performance in comparison with previous works

Accelerated discovery of molecular nanojunction photocatalysts for hydrogen evolution by using automated screening and flow synthesis

Discovering and optimizing multicomponent organic semiconductors is typically a laborious process. High-throughput experimentation can accelerate this, but the results of small-scale screening trials are not always transferable to bulk materials production. Here we report the accelerated discovery of molecular nanojunction photocatalysts based on a combinatorial donor–acceptor molecular library assisted by high-throughput automated screening. The knowledge gained from this high-throughput batch screening is then transferred to a scaled-up, flow-based synthesis process. The scaled-up molecular nanojunction MTPA-CA:CNP147 (3-(4-(bis(4-methoxyphenyl)amino)phenyl)-2-cyanoacrylic acid:2,6-bis(4-cyanophenyl)-4-(4′-fluoro-[1,1′-biphenyl]-4-yl)pyridine-3,5-dicarbonitrile) exhibits a sacrificial hydrogen evolution rate of 330.3 mmol h−1 g−1 with an external quantum efficiency of 80.3% at 350 nm, which are among the highest reported for an organic photocatalyst. A one-dimensional nanofibre architecture is identified for this molecular nanojunction, which exhibits efficient charge separation. Electronic structure–property correlations across the photocatalyst library show that a moderate binding energy between the donor and the acceptor molecules is a potential factor for efficient molecular nanojunction formation

Core promoter-specific gene regulation: TATA box selectivity and Initiator-dependent bi-directionality of serum response factor-activated transcription

Gene-specific activation by enhancers involves their communication with the basal RNA polymerase II transcription machinery at the core promoter. Core promoters are diverse and may contain a variety of sequence elements such as the TATA box, the Initiator (INR), and the downstream promoter element (DPE) recognized, respectively, by the TATA-binding protein (TBP) and TBP-associated factors of the TFIID complex. Core promoter elements contribute to the gene selectivity of enhancers, and INR/DPE-specific enhancers and activators have been identified. Here, we identify a TATA box-selective activating sequence upstream of the human β-actin (ACTB) gene that mediates serum response factor (SRF)-induced transcription from TATA-dependent but not INR-dependent promoters and requires the TATA-binding/bending activity of TBP, which is otherwise dispensable for transcription from a TATA-less promoter. The SRF-dependent ACTB sequence is stereospecific on TATA promoters but activates in an orientation-independent manner a composite TATA/INR-containing promoter. More generally, we show that SRF-regulated genes of the actin/cytoskeleton/contractile family tend to have a TATA box. These results suggest distinct TATA-dependent and INR-dependent mechanisms of TFIID-mediated transcription in mammalian cells that are compatible with only certain stereospecific combinations of activators, and that a TBP-TATA binding mechanism is important for SRF activation of the actin/cytoskeleton-related gene family

Efficiently Scheduling Remote And Local Resources in ML Data Input Pipeline

Machine Learning model training is costly and time-consuming. According to recent research, the bottleneck of the model training process lies in the input data processing stages. tf.data.service, as well as its extension Cachew, tries to solve this problem by disaggregating the input pipelines from the model and moving them onto the cloud. This successfully removes the bottleneck from the input pipeline. However, such an approach introduces extra cost from the cloud service and fails to fully utilize the computation resources on the host machine. In this thesis, we discuss two different approaches to solving this problem: utilizing local workers and pipeline splitting, and propose a final policy integrating both of them to minimize the extra cost while keeping the pipeline fast enough. This policy is implemented upon v2.8 of tf.data and tested on different input pipelines, seeing a 9% to 26% cost saving compared to Cachew’s autoscaling policy

Effect of Welding Speed and Post Quenching on the Microstructure and Mechanical Properties of Laser-Welded B1500HS Joints

In this research, B1500HS high-strength steel with different thicknesses were laser welded, and the effects of welding speed and post quenching were investigated by analyzing the microstructure, microhardness distribution, and high-temperature tensile properties of weld joints. The results show that an obvious difference can be found in the metallographic structure and grain morphology of the weld joint at different locations, which also lead to the significant uneven distribution of hardness. After quenching, the grain size of the original heat-affected zone was uniform, the columnar grains in the fusion zone were transformed into fine equiaxed grains, and no obvious hardness difference can be found in the weld joint. For the weld joint without quenching, the increase of welding speed can reduce the dimensions of grains of fusion zone and coarse grain zone, and slightly increase the hardness of these regions. In contrast, welding speed change has little influence on the microstructure and hardness of the weld joint after quenching. The high-temperature flow stress–strain curves of fusion zone welded under different welding speeds were calculated based on the mixture rule. The analysis results indicated that the fusion zone has higher strength but lower elongation than the base metal. In addition, the change of welding speed has a small impact on the high-temperature tensile properties of the fusion zone