Ray Conditioning: Trading Photo-consistency for Photo-realism in Multi-view Image Generation

Multi-view image generation attracts particular attention these days due to its promising 3D-related applications, e.g., image viewpoint editing. Most existing methods follow a paradigm where a 3D representation is first synthesized, and then rendered into 2D images to ensure photo-consistency across viewpoints. However, such explicit bias for photo-consistency sacrifices photo-realism, causing geometry artifacts and loss of fine-scale details when these methods are applied to edit real images. To address this issue, we propose ray conditioning, a geometry-free alternative that relaxes the photo-consistency constraint. Our method generates multi-view images by conditioning a 2D GAN on a light field prior. With explicit viewpoint control, state-of-the-art photo-realism and identity consistency, our method is particularly suited for the viewpoint editing task.Comment: Project page at https://ray-cond.github.io

LAPTM4B-35 promotes cancer cell migration via stimulating integrin beta1 recycling and focal adhesion dynamics

Metastasis is the main cause of cancer patients' death despite tremendous efforts invested in developing the related molecular mechanisms. During cancer cell migration, cells undergo dynamic regulation of filopodia, focal adhesion, and endosome trafficking. Cdc42 is imperative for maintaining cell morphology and filopodia, regulating cell movement. Integrin beta1 activates on the endosome, the majority of which distributes itself on the plasma membrane, indicating that endocytic trafficking is essential for this activity. In cancers, high expression of lysosome-associated protein transmembrane 4B (LAPTM4B) is associated with poor prognosis. LAPTM4B-35 has been reported as displaying plasma membrane distribution and being associated with cancer cell migration. However, the detailed mechanism of its isoform-specific distribution and whether it relates to cell migration remain unknown. Here, we first report and quantify the filopodia localization of LAPTM4B-35: mechanically, that specific interaction with Cdc42 promoted its localization to the filopodia. Furthermore, our data show that LAPTM4B-35 stabilized filopodia and regulated integrin beta1 recycling via interaction and cotrafficking on the endosome. In our zebrafish xenograft model, LAPTM4B-35 stimulated the formation and dynamics of focal adhesion, further promoting cancer cell dissemination, whereas in skin cancer patients, LAPTM4B level correlated with poor prognosis. In short, this study establishes an insight into the mechanism of LAPTM4B-35 filopodia distribution, as well as into its biological effects and its clinical significance, providing a novel target for cancer therapeutics development.Peer reviewe

A Drive to Driven Model of Mapping Intraspecific Interaction Networks.

Community ecology theory suggests that an individual\u27s phenotype is determined by the phenotypes of its coexisting members to the extent at which this process can shape community evolution. Here, we develop a mapping theory to identify interaction quantitative trait loci (QTL) governing inter-individual dependence. We mathematically formulate the decision-making strategy of interacting individuals. We integrate these mathematical descriptors into a statistical procedure, enabling the joint characterization of how QTL drive the strengths of ecological interactions and how the genetic architecture of QTL is driven by ecological networks. In three fish full-sib mapping experiments, we identify a set of genome-wide QTL that control a range of societal behaviors, including mutualism, altruism, aggression, and antagonism, and find that these intraspecific interactions increase the genetic variation of body mass by about 50%. We showcase how the interaction QTL can be used as editors to reconstruct and engineer new social networks for ecological communities

Developing high-performance enzymatic biofuel cell

Nowadays, biomaterials is becoming a promising green and renewable technology compared with conventional fossil fuels. It occurs either in forms of biofuels, which is a process of converting chemical energy in the substances into mechanical energies, or biofuel cells (BFCs), which use enzymes as the catalysts to harvest energy from environmental and sustainable fuels abundantly producible from biological systems. However, the applications of current BFCs is greatly limited by their poor stability and high specificity to only one fuel type of these bio-catalysts. In this project, we demonstrate a unique BFC equipped with two identical enzyme-free electrodes based on Co3O4 coated 3D graphene, has the ability to harvest electricity from various sugarfuels (glucose, sucrose, or lactose) efficiently. Considering the advantages of the dual catalytic ability of nanostructured Co3O4 for both glucose oxidation and oxygen reduction together with the exceptional electrical and structural properties of 3D graphene, our glucose-powered BFC which has the excellent long-term stability, can provide the highest open circuit potential (~1.1 V) and power density output (2.38 ± 0.17 mW/cm2) ever reported.​Master of Science (Biomedical Engineering

Effects of Mechanical Harvesting of Main Crop on Soil Rolling and Yield of Ratooned Rice

[Objectives] To study the effect of mechanical harvesting of main crop on soil rolling and yield of ratooned rice. [Methods] In this study, the harvesting method was optimized and improved through field research and theoretical research. [Results] Compared with farmers' habits, the mechanical harvesting method could significantly increase the working area per unit time and reduce the rolling area in the field, but it would increase the rolling rate of the land or transfer area. At the same time, the optimization method could reduce the soil bulk density in the primary rolling area, but it had no significant impact on soil compactness. [Conclusions] Compared with the farmer's customary method, the optimization method could reduce the crushing and damage of rice piles in the field, increase the seedling rate and panicle extraction rate, thus increasing the yield of rice in the ratooning season

Ultra-high amplitude isolated attosecond pulses generated in the transmission regime from ultrathin foil

A coherent-synchrotron-emission regime of forward high-order harmonics generation (HHG) is proposed for the emission of an isolated unipolar half-cycle attosecond pulse by a three-color laser pulse impinging on ultrathin foil. A theoretical model is proposed for the electron nanobunching mechanism and the forward radiation, which is consistent with the numerical-simulation results. As the forward HHG does not need to penetrate from the front to the rear side of the target, the spectrum of the forward HHG has no low-frequency cutoff. The robustness of this regime is verified with different laser and foil plasma conditions as well as the two-dimensional effects. The robustness is also checked with similarity theory, which confirms that isolated attosecond pulses can be efficiently generated when the plasma density and the laser amplitude change simultaneously such that their ratio remains unchanged

Pricing Strategies in Dual-Channel Reverse Supply Chains Considering Fairness Concern

The fierce competition in the recycling industry and the rapid development of internet technology has prompted recycling centers to develop a dual-channel reverse supply chain with both offline and online recycling channels. After the introduction of online channels, recycling centers and third-party recyclers (TPR) have paid attention to the division of profits in supply chain systems and the behavior of fairness concerns. Therefore, it is necessary to help recycling enterprises make pricing decisions in consideration of fairness concerns. This paper is aimed at answering the following two main questions: (1) When the recycling center or TPR have fairness concerns, how does the optimal pricing and revenue of supply chain members change when both sides are neutral? (2) When the fairness concern coefficient changes, how does the overall revenue of the supply chain system change? How should supply chain members adjust their pricing decisions to maximize their own profits? In order to solve the above problems, Stackelberg game models were made from three aspects: both sides are neutral, only the TPR has fairness concerns, and only the recycling center has fairness concerns. Based on the results of the example analyses for the model, we found that when only the TPR has fairness concerns, the profit of the recycling center and the transfer price of offline channels will decrease, while the profit of TPR is the opposite. Furthermore, when only a recycling center has fairness concerns, it will lead to the reduction of not only the recycling price and transfer price of offline channels, but also the profits of the entire supply chain system. Specially, whether it is for a recycling center or TPR, a lower level of fairness concern coefficient has a stronger impact on pricing and revenue than at high levels

Handwritten Font Classification Method Based on Ghost Imaging

With the rapid development of the economy， financial services such as bills are also increasing day by day. Among them， the important information in the bill business， such as personal vouchers， checks， and other bills， requires manual reading and input of a large amount of digital information. In order to avoid the waste of human and financial resources， related researchers classify and recognize handwritten fonts based on the neural network classification idea of ​​deep learning. When the method based on deep learning is used for feature extraction of handwritten fonts， there is often a lack of detailed information such as edges and textures， which leads to the problem of low recognition accuracy. Aiming at the problem that the features of handwritten digits or letters are difficult to effectively extract， the recognition efficiency was not high and even caused recognition errors， a new automatic recognition method of handwritten digits or letters was proposed by combining the principle of ghost imaging and the classification network based on deep learning. This method utilizes the principle of ghost imaging. It can save the imaging process in the traditional image recognition method， and jumping out of the inherent thinking that identifying objects is identifying images， and can quickly classify the image of handwritten digits and letters only by the total light intensity value transmitted by handwritten digits or letters without extracting and identifying features of handwritten digits or letters. The automatic recognition of handwritten digits or letters based on ghost imaging solves the critical problem of needing to extract digits or letter images features in traditional handwritten font recognition methods， and can greatly improve the recognition efficiency of handwritten digits or letters. Firstly， a ghost imaging detection system is built using commonly used optical instruments such as lasers， digital micromirror arrays， and single-pixel detectors. The laser in the built detection system is used to generate a pseudothermal light source， and the digital micromirror array is used to obtain the Hadamard speckle sequence with a resolution of 32×32 irradiating the target object at different times. And realizing the irradiation of 17 239 handwritten images of handwritten digits and letters. Secondly， the single-pixel detector is used to collect data on the total light intensity value transmitted by the handwritten digits and letters. The data collection process is very fast and does not cause huge time costs. The value of the bucket detector after the collection is converted into a one-dimensional vector， and use the one-dimensional vector corresponds to the handwritten font as the input of network training. Finally， the network framework is built based on the advantages of the convolutional neural network in image classification and is used to solve the problems in the training process. The network degradation problem is added to the residual block structure， which can directly pass shallow information to deeper layers by skipping one or several layers through skip connections. In order to solve the problem of overfitting， the Dropout layer is added to it， and the robustness of the network to the loss of specific neuron connections is improved by reducing the weight. The experimental results show that： for handwritten digits， compared with the fully connected network， the precision， recall rate and F1 value of the convolutional neural network model are increased by 86.50%/97.25%， 86.40%/98.03%， 86.31%/97.60%； for handwritten letters， the precision， recall， and F1 value of the convolutional neural network under full sampling are 91.87%， 90%， and 90.23%， respectively. At the same time， in the case of undersampling and non-undersampling， the ten types of digits from 0 to 9 under the two models of convolutional neural network and fully connected neural network and randomly selected l， v， y， z， m， n， o， r， s， and h ten types of letters are compared and analyzed. The experimental results show that the accuracy rate of each type of digit and letter of the convolutional neural network is higher than that of the fully connected network under the same conditions. The accuracy of each type of digit and letter under the two models further verifies that as the sampling rate increases， the recognition accuracy also increases. By comparing the evaluation indicators of the convolutional neural network and the fully connected network architecture， the effectiveness and rationality of the proposed method are further illustrated. The classification and recognition results of handwritten letters verified by experiments further illustrate the versatility of the constructed convolutional neural network. It provides the possibility for the wide application of handwritten fonts in real life. The research on the classification and the recognition of handwritten fonts based on ghost imaging can effectively solve the bottleneck problem of low recognition efficiency of existing font recognition methods

Uncertainty-Controlled Remaining Useful Life Prediction of Bearings with a New Data-Augmentation Strategy

The remaining useful life (RUL) of bearings based on deep learning methods has been increasingly used. However, there are still two obstacles in deep learning RUL prediction: (1) the training process of the deep learning model requires enough data, but run-to-failure data are limited in the actual industry; (2) the mutual dependence between RUL predictions at different time instants are commonly ignored in existing RUL prediction methods. To overcome these problems, a RUL prediction method combining the data augmentation strategy and Wiener–LSTM network is proposed. First, the Sobol sampling strategy is implemented to augment run-to-failure data based on the degradation model. Then, the Wiener–LSTM model is developed for the RUL prediction of bearings. Different from the existing LSTM-based bearing RUL methods, the Wiener–LSTM model utilizes the Wiener process to represent the mutual dependence between the predicted RUL results at different time instants and embeds the Wiener process into the LSTM to control the uncertainty of the result. A joint optimization strategy is applied in the construction of the loss function. The efficacy and superiority of the proposed method are verified on a rolling bearing dataset obtained from the PRONOSTIA platform. Compared with the conventional bearing RUL prediction methods, the proposed method can effectively augment the bearing run-to-failure data and, thus, improve the prediction results. Meanwhile, fluctuations of the bearing RUL prediction result are significantly suppressed by the proposed method, and the prediction errors of the proposed method are much lower than other comparative methods