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The activity of the secreted phosphodiesterase autotaxin produces the inflammatory signaling molecule LPA and has been associated with a number of human diseases including idiopathic pulmonary fibrosis (IPF). We screened a single DNA-encoded chemical library (DECL) of 225 million compounds and identified a series of potent inhibitors. Optimization of this series led to the discovery of compound 1 (X-165), a highly potent, selective, and bioavailable small molecule. Cocrystallization of compound 1 with human autotaxin demonstrated that it has a novel binding mode occupying both the hydrophobic pocket and a channel near the autotaxin active site. Compound 1 inhibited the production of LPA in human and mouse plasma at nanomolar levels and showed efficacy in a mouse model of human lung fibrosis. After successfully completing IND-enabling studies, compound 1 was approved by the FDA for a Phase I clinical trial. These results demonstrate that DECL hits can be readily optimized into clinical candidates.

Feature engineering usually needs to excavate dense-and-implicit cross features from multi-filed sparse data. Recently, many state-of-the-art models have been proposed to achieve low-order and high-order feature interactions. However, most of them ignore the importance of cross features and fail to suppress the negative impact of useless features. In this paper, a novel multi-scale feature-crossing attention network (MsFcNET) is proposed to extract dense-and-implicit cross features and learn their importance in the different scales. The model adopts the DIA-LSTM units to construct a new attention calibration architecture, which can adaptively adjust the weights of features in the process of feature interactions. On the other hand, it also integrates a multi-scale feature-crossing module to strengthen the representation ability of cross features from multi-field sparse data. The extensive experimental results on three real-world prediction datasets demonstrate that our proposed model yields superior performance compared with the other state-of-the-art models.

This paper proposes an innovative rail pressure sensor signal processing algorithm to determine the start of injection and end of injection for single or multiple injections of diesel engines with a common rail injection system (CRIS). The importance of fuel injection timing (FIT) and fuel injection volume (FIV) in the CRIS is discussed, as well as the current progress of research in this field. The working principle of a solenoid valve injector is also described, pressure wave propagation is analyzed, and the characteristics of pressure waves are studied by simulation and experiments. The proposed algorithm has high reliability and flexibility and can be adjusted in real time according to engine operating status. The algorithm can effectively avoid the defects and shortcomings of traditional engine matching calibration, enhance the control precision of the FIT and FIV, improve the consistency and stability of fuel injection, and is expected to promote the performance of engine on fuel economy and pollutant emissions. The hardware implementation of this algorithm in practical ECU is also presented.

Influence of original and T-shape blade end on performance of a mixed-flow pump is investigated by using experimental measurement and numerical simulation. The new proposed T-shape blade end is formed at 95%–100% blade height with a linear increase of blade thickness. In comparison with original blade end, the efficiency of pump with T-shape blade end increases by 1.86%, and the leakage flow decreases by 15.95%. Space streamlines across the blade tip clearance can be divided into three beams with different movement trajectories, and the swirl motions of streamlines directly correspond to the swirling strength. The highest amplitude of pressure fluctuation appears at the blade leading edge along the tip clearance. In comparison with original blade end, the highest amplitude of pressure fluctuation for T-shape blade end decreases by 27.45%. The dominant frequencies of the pressure fluctuations in tip clearance region for original blade end and T-shape blade end are both five times of the axis rotation frequency, corresponding to the impeller blade number of five.

A number of edge-aware filters can efficiently boost the appearance of an image by detail decomposition and enhancement. However, they often fail to produce photographic enhanced appearance due to some visible artifacts, especially noise, halos and unnatural contrast. The essential reason is that the guidance and the constraint of high-quality appearance are not sufficient enough in the process of enhancement. Thus our idea is to train a detail dictionary from a lot of high-quality patches in order to constrain and control the entire appearance enhancement. In this paper, we propose a novel learningbased enhancement method for photographic appearance, which includes two main stages: dictionary training and sparse reconstruction. In the training stage, we construct a training set of detail patches extracted from some high-quality photos, and then train an overcomplete detail dictionary by iteratively minimizing an l 1-norm energy function. In the reconstruction stage, we employ the trained dictionary to reconstruct the boosted detail layer, and further formalize a gradient-guided optimization function to improve the local coherence between patches. Moreover, we propose two evaluation metrics to measure the performance of appearance enhancement. The final experimental results have demonstrated the effectiveness of our learning-based enhancement method.

The inverse dynamics and numerical iterative algorithm are presented for a 3-RRR rigid-flexible planar parallel mechanism. The natural coordinate formulation (NCF) along with absolute nodal coordinate formulation (ANCF) is employed to describe flexible and rigid links of the mechanism, respectively. The inverse multibody dynamics is constructed based on Lagrange principle. An efficient calculation of the strain energy of flexible links is carried on to avoid Poisson locking by a reduced integration method and invariant matrices. The constrained inverse dynamic solution is gained by generalized-cx approach. The experimental iteration results for a circular planar trajectory show that the angular displacement and speed of actuated linkages differ from the results of ideal rigid-body model. The changes of shear angles at the midpoint of the flexible links possess symmetric characteristics. The solution has great significance in theory and application for future controller design.

Cigarette code is a string printed on the wrapper of cigarette packet as a basis of distinguishing illegal sales for tobacco administration. In general, the code is excerpted and entered to administration system manually during on-site inspection, which is quite time-consuming and laborious. In this paper, we propose a new solution based on convolutional neural network for intelligent transcription. Our recognition method is composed of four components: detection, identification, alignment, and regularization. First of all, the detection component fine-tunes an end-to-end detection network to obtain the bounding box region of cigarette code. Then the identification component constructs an optimized CNN architecture to recognize each character in the region of cigarette code. Meanwhile the alignment component trains a CPM-based network to estimate the positions of all characters including some missing characters. Finally, the regularization component develops a matching algorithm to produce a regularized result with all characters. The experimental results demonstrate that our proposed method can perform a better, faster and more labor-saving cigarette code transcription process.

China film market has become the second largest box office market just after North America today, the Chinese film market is less than 1/5 size of the now, in 10 years ago. The Chinese film market is growing so fast, the reasons research on this phenomenon become a hot spot. In this article, we tried to find the rule of films box office market based on the historical data of China released film. Gaining film market rules will help film-makers understand audience preferences and make decisions on future movie projects. We collected the movies data released in China in recent 15 years which contains box office, and basic information (such as release date, language, type, country, etc.), correspondingly, we also collected the movie audience's com-ments on the internet, more than 100000 comments in total, There is no public movies released in China set provided by a inde-pendent agency until now, According to the obtained data, we analyzed the movies data distribution rules in the month, year, genre, using language, We also visualized the proportion of all films in the rating (from douban.com), and analyzed collectively the quality of domestic released films. We conducted word cloud processing on the comments of the highest box office movie Wolf warriors 2, which demonstrate intuitively the public's discussion on this movie.

This paper presents a Class-C LC VCO which can be used for 802.11 b/g/n, Bluetooth and ZigBee. Implemented in 0.18μm process, the VCO can achieve coarse frequency tuning and fine frequency tuning. The bias voltage of the cross-coupled transistors can be adjusted to control the conduction angle of the VCO to improve the current utilization efficiency. The bias current can be adjusted to achieve a compromise of power consumption and phase noise.

Face aging is one of research hotspots in the field of computer vision, but there is no mature system yet that can be applied to digital entertainment. This paper proposes a face aging system based on feature fusion, which can synthesize aging facial images of young people efficiently and quickly by using facial texture features of elderly people's facial images, achieve the goal of simulating the appearance after aging. The system published online by the “eleme” app for the Mother's Day, showing great potential for digital entertainment.

Face anti-spoofing, as a security measure for face verification and recognition system, could distinguish between genuine and fake faces. Although some impressive results have been achieved by CNN when evaluated on intra-tests (i.e. the model is trained and tested on the same dataset). Unfortunately, most of these models fail to generalize well to unseen attacks (e.g. when the model is trained on one dataset and then evaluated on another dataset). This is a major concern in face anti-spoofing research which is mostly overlooked. Our experiments on two challenging benchmark face spoofing datasets, CASIA and Replay-Attack, demonstrate the disappointed adaptation ability for CNN from one dataset to another. Via visualizing the implicit attention of CNN, we find that scene-dependent features extracted by CNN affect models generalization capabilities. To address this problem, we propose a novel solution based on applying scene-independent features representation.

Video facial swap usually has strong entertainment applications, and it is also applicable for the post-production of films and has great application value. At present, the popular face swap is done manually by the PS software, and the synthetic effect of the automatic face changing technology is not good. In order to make up for the lack of these features, this paper proposes a method of video face swap based on autoencoder generation network. The network learns the mapping relationship between distorted face and original face: the encoder can distinguish and extract facial information, and the decoder can restore face separately. First, the local information of tow face is sent to the network to get the initial model; then, the global information is put into the network for fine-tuning; finally, the face exchange between A and B is completed with face alignment and alpha fusion. The experimental results show that the quality of the method is improved significantly.

This paper presents a three-channel, which are low-side driver (LS), battery high-side driver (LVHS) and booster high-side driver (HVHS), area efficient integrated gate driver with Schmitt-triggered input for piston engine fuel injection control SoC. The gate driver is implemented in 0.18 μ m BCD process and can achieve a switching frequency higher than 200KHz and work at 75V floating channel voltage for bootstrap operation. Narrow pulses are used to drive high-voltage level shifter, thus reducing system power consumption, while improving the life of high-voltage NMOS transistors. By adopting an innovated new bootstrap circuit structure, area of the gate driver output drive stage is reduced by 40% compared to the conventional one. The bootstrap capacitor is integrated on chip to reduce cost and improve reliability.

A novel high-precision, high-speed fuel injection control SoC for Diesel Engine Management System (DEMS), which achieve the real-time, flexible and precise control of fuel injection amount and fuel injection time, is presented in this paper. This SoC includes control logic and state machine, DC/DC boost converter, high-side and low-side current-sense amplifier and gate driver. In which, the gate driver and current-sense amplifier are already implemented in TSMC 0.18μm 70V BCD process. To verify the design scheme, a DEMS test system is designed and implemented based on PCB. System level test results show that the fuel injection control SoC can control and actuate the solenoid valve injector well, it can offer a switching frequency higher than 200KHz, the booster voltage can reach as high as 75V and the injection current can reach 16.7A in less than 200 μ s during booster phase. And the proposed DEMS can meet Chinese emission standard at phase III for non-road.

A 125 KHz wake-up receiver to receive wake-up signal and a 433MHz data transmitter to transmit temperature and pressure data of the tire to central control module for battery-less TPMS (Tire Pressure Monitoring System) are presented in this paper. The receiver adopts dual-channel to improve reliability and logarithmic amplifier to achieve ASK demodulation and dynamic range compression. The receiver is implemented in 0.35μm BCD process. Maximum data rate is 35Kbps with 0.5mVpp sensitivity and 5μA typical power consumption. The transmitter is implemented in 0.18μm process. The typical power consumption is 7.3mA with a transmit power of −10 dBm@433.9 MHz, and phase noise of the PLL is − 103dBc/Hz@ 300 KHz with 30Kbps data rate.

High dynamic range (HDR) imaging has seen a lot of progress in recent years, while an efficient way to capture and generate HDR video is still in need. In this paper, we present a method to generate HDR video from frame sequence of alternating exposures in a fast and concise fashion. It takes advantage of the recent advancement in deep learning to achieve superior efficiency compared to other state-of-art method. By training an end-to-end CNN model to estimate optical flow between frames of different exposures, we are able to achieve dense image registration of them. Using this as a base, we develop an efficient method to reconstruct the aligned LDR frames with different exposure and then merge them to produce the corresponding HDR frame. Our approach shows good performance and time efficiency while still maintain a relatively concise framework.

Tone mapping can compress the color range of HDR (high dynamic range) image to produce its LDR (low dynamic range) image. However, without preserving the original scene detail, tone mapping may produce various artifacts and their fidelity will be severely decreased. In this paper, we propose a new detail-preserving refinement method to efficiently improve the fidelity of tone mapping. Its overall flow includes the four key steps: tone initialization, detail extraction, tone reconstruction, and detail restoration. First of all, we employ clustering and decomposition to obtain initial tone layer and detail map. Then we construct detail affinity and gradient guidance to optimize the detail map and reconstruct the new tone layer. Finally, we combine them to produce a high-fidelity result of tone mapping. We demonstrate the effectiveness of the proposed method through a number of experiments in visual comparison, and verify its high-efficiency performance in execution time.

A 130 nm 1Mb embedded phase change memory (PCM) has been achieved, requiring only three additional masks for phase change storage element, featuring 500 kb/s single channel write throughput and > 10 8 endurance. The prepare process has been optimized to reduce the cost and power. An 80 nm heat electrode has been prepared with 130 nm process. The optimal Read/Write circuit module is designed to realize the load/store function for PCM. The critical operation parameter is Reset/70 ns/2.5 mA and Set/1500 ns/1 mA, which means that the signal channel write throughput arrives to 500 kb/s.

In order to reduce the RESET current of phase change memory (PCM), which is fabricated using standard 0.13-μm CMOS technology, we have investigated various process factors that might affect the phase transition process, including doping concentration, diameter of bottom electrode contact (BEC) and different chalcogenide materials. Test results suggest that the PCM memory cell, utilizing Si2Sb2Te5 (SST) material as storage element with 80 nm BEC, can be operated using a 40 ns electrical pulse with current amplitude as small as 0.5 mA. This is mainly resulted from the high electrical resistances of the novel chalcogenide both in amorphous and crystalline state, which contribute greatly to the improved efficiency of heating process.

In this paper, a new contrast media diffusion simulation algorithm is proposed for minimally invasive vascular interventions. On the basis of smoothed particle hydrodynamics (SPH), the algorithm could be mainly divided into two parts: fluid-fluid interaction and fluid-solid interaction. In the fluid-fluid interaction, an adsorption model of iodine particles is built, and the adjacent blood flow particles around that could be got quickly by Eulerian grid and Space Sparse Hash. In the fluid-solid interaction, coulomb friction model is used to realize the frictional contact between the fluid particles and the inner walls of vessel. For improving the computational speed, the algorithm uses multi-thread parallel technology to address the complex diffusion problem of contrast media on computer unified device architecture (CUDA). The experimental results show that this algorithm is feasible, and could greatly enhance the development effect of blood vessels with real time performance, especially the capillaries.

In this paper, a real-time facial animation system is presented, that captures live facial expressions and poses from user, and uses them to animate a synthetic 2.5D cartoon character (Avatar) in real-time. Compared to existing 3D Avatar systems, the 2.5D cartoon models are easier to create than full 3D model, and the cartoon-style drawings are more popular among young people. Our system requires only an ordinary PC camera, and the initialization is fully automatic. We established a mapping from real-time facial motion to the atomic motion parameters of the cartoon model by pose and expression estimation. The animation is driven by these parameters in the way of blend shape interpolation.

In the cardiovascular interventional operation, the surgeon steers the tip of a long-thin guidewire to reach the clinical targets while traveling through the inner of blood vessels, and performs a wide range of minimally invasive procedures. However, real-time simulating the physical deformation behaviours of guidewire caused by a large areas of frictional contact between guidewire and vasculature during insertion is a challenge task. From the microscopic view, this paper built a novel multi-frictional contact dynamics model based on flexible multi-body system to address the multi-frictional interaction between them. In the model, guidewire and vascular formed a flexible multi-body system and the process of contact and collision could be divided into three stages, including contact detection, contact handling and separation. In the first stage, a continuous collision detection algorithm based on an adaptive layer was proposed to obtain a set of “point-surface” contact pairs quickly. After confirming the contact areas, a multi-frictional contact dynamics algorithm based on nonlinear equivalent spring damping was put forward. In the normal direction, nonlinear spring damping model was used to compute the spring restoring force and nonlinear damping force. In the tangential direction, sliding friction, static friction and rolling friction were calculated during the collision between two bodies by coulomb friction model. Finally, all frictional forces in the contact areas were added to the physical models of guidewire for further simulating various non-linear deformation behaviors. The experimental results show that this algorithm is feasible and could simulate the multi-frictional interaction between guidewire and blood vessles very well with real-time performance.

Generally, for a global or local color transfer, the traditional approaches will rearrange color distribution in source image according to reference image. However, the destruction of scene detail and illumination environment might produce a low-fidelity color transfer result. In this paper, we propose a unified fidelity optimization model for color transfer to yield a high-fidelity transfer result in terms of color, detail and illumination. Corresponding to the three characteristics, our approach is described as an optimization problem with three energy terms: color mapping, detail preserving and illumination awareness. Color mapping can employ histogram matching to impose the color style of reference image on source image; Detail preserving can apply gradient guidance to maintain scene detail in source image; Illumination awareness can construct illumination affinity to harmonize illumination environment. Moreover, following the definition of fidelity with three characteristics, we also propose an objective evaluation metric to analyze the performance of our approach in different coefficients. The comparison of experiment results demonstrates the effectiveness of our optimization model.

In planar ${\cal N}=4$ supersymmetric Yang-Mills theory we have studied supersymmetric Wilson loops composed of a large number of light-like segments, i.e., null zig-zags. These contours oscillate around smooth underlying spacelike paths. At one-loop in perturbation theory we have compared the finite part of the expectation value of null zig-zags to the finite part of the expectation value of non-scalar-coupled Wilson loops whose contours are the underlying smooth spacelike paths. In arXiv:0710.1060 [hep-th] it was argued that these quantities are equal for the case of a rectangular Wilson loop. Here we present a modest extension of this result to zig-zags of circular shape and zig-zags following non-parallel, disconnected line segments and show analytically that the one-loop finite part is indeed that given by the smooth spacelike Wilson loop without coupling to scalars which the zig-zag contour approximates. We make some comments regarding the generalization to arbitrary shapes., Comment: 13 pages, 6 figures

In order to organise the awareness information in the collaborative learning environment reasonably and efficiently, making good use of the ontology characteristics such as clear knowledge organization, explicit definition of concept and relation etc, an awareness ontology model is presented in this paper. The model contains six aspects: person, community, role, KnowledgeDomain, event and activity as well as each kind of relations among these concepts and in the concept interior. This model can solve the existing flaws like chaotic organization and difficult maintenance in the traditional mode of awareness information organization. Thus enhances collaborative learning efficiency in the greatest degree. For constructing needed ontology reasonably and conveniently, a method of KnowledgeDomain correlativity extraction based on the N-Grams is presented on the basis of awareness ontology model. It can extract the asrelativity? relation automatically and satisfy dynamic awareness processing

We report the analysis and TCAD results of a gate-all-around cylindrical (GAAC) FinFET with operation based on channel accumulation. The cylindrical channel of the GAAC FinFET is essentially controlled by an infinite number of gates surrounding the cylinder-shaped channel. The symmetrical nature of the field in the channel leads to improved electrical characteristics, e.g. reduced leakage current and negligible corner effects. The Ion/Ioff ratio of the device can be larger than 10 6 , as the key parameter for device operation. The GAAC FinFET operating in