Shifted Diffusion for Text-to-image Generation

We present Corgi, a novel method for text-to-image generation. Corgi is based on our proposed shifted diffusion model, which achieves better image embedding generation from input text. Unlike the baseline diffusion model used in DALL-E 2, our method seamlessly encodes prior knowledge of the pre-trained CLIP model in its diffusion process by designing a new initialization distribution and a new transition step of the diffusion. Compared to the strong DALL-E 2 baseline, our method performs better in generating image embedding from the text in terms of both efficiency and effectiveness, resulting in better text-to-image generation. Extensive large-scale experiments are conducted and evaluated in terms of both quantitative measures and human evaluation, indicating a stronger generation ability of our method compared to existing ones. Furthermore, our model enables semi-supervised and language-free training for text-to-image generation, where only part or none of the images in the training dataset have an associated caption. Trained with only 1.7% of the images being captioned, our semi-supervised model obtains FID results comparable to DALL-E 2 on zero-shot text-to-image generation evaluated on MS-COCO. Corgi also achieves new state-of-the-art results across different datasets on downstream language-free text-to-image generation tasks, outperforming the previous method, Lafite, by a large margin

Pre-operative administration of butorphanol mitigates emergence agitation in patients undergoing functional endoscopic sinus surgery: A randomized controlled clinical trial

BackgroundThis study explored the effectiveness of pre-operative intravenous injection of butorphanol in the alleviation of emergence agitation (EA) in patients undergoing functional endoscopic sinus surgery (FESS).MethodsPatients (n = 708) were randomized into two groups. The butorphanol group (Group B, n = 358) received butorphanol infusion (20 ug/kg) before anesthesia induction, while the control group (Group C, n = 350) received an equal volume of normal saline infusion. General anesthesia was induced with sufentanil, propofol, and rocuronium, and was maintained with sevoflurane and remifentanil. Vasoactive drugs maintained the hemodynamic indices within 20% of the baseline.ResultsThe incidence of EA was significantly lower in Group B than that in Group C (Group B vs. C: 24.3% vs. 31.4%, respectively; P = 0.034). The times to spontaneous breathing (26.5 min vs. 23.7 min, P = 0.011), verbal response (36.0 min vs. 33.4 min, P = 0.012), and extubation (31.0 min vs. 28.7 min, P = 0.025) were longer in Group B, and the grade of cough (0.33 vs. 0.43, P = 0.024) at extubation in Group B was lower than that in Group C (P = 0.024). The mean arterial pressure at the end of the operation (P = 0.004) and at 5 min after extubation (P = 0.008) was higher and hypotension was less prominent (0.6% vs. 2.6%, P = 0.030) in Group B.ConclusionPre-operative intravenous injection of butorphanol decreased the incidence of EA after FESS and provided smooth and hemodynamically stable emergence without extending the stay in post-anesthesia care unit.Clinical trial registrationhttps://www.clinicaltrials.gov/, identifier NCT03398759

Dual-wavelength DFB laser array based on sidewall grating and lateral modulation of the grating coupling coefficient

A monolithic dual-wavelength DFB laser array based on sidewall gratings and a novel modulation of the grating coupling coefficient is proposed and demonstrated experimentally. The grating coupling coefficient distribution along the cavity is modulated by changing the alignment between the gratings on the two sidewalls. The frequency difference between the two lasing modes can be modulated by changing the cavity length and grating recess depth. A series of microwave signals in the range of 50 GHz to 59 GHz is observed after beating the two optical lines in a photodetector. The measured optical linewidths are 250 kHz and 850 kHz when the cavity length is 1200 μm and 1000 μm, respectively

Remaining Useful Life Estimation of Rolling Bearing Based on SOA-SVM Algorithm

Rolling bearings are an important part of rotating machinery, and are of great significance for fault diagnosis and life monitoring of rolling bearings. Analyzing fault signals, extracting effective degradation information and establishing corresponding models are the premise of residual life prediction of rolling bearings. In this paper, first, the time-domain features were extracted to form the eigenvector of the vibration signal, and then the index representing the bearing degradation was found. It was found that the time-domain index could effectively describe the degradation information of the bearing, and the multi-dimensional time-domain characteristic information could effectively describe the attenuation trend of the vibration signal of the rolling bearing. On this basis, appropriate feature vectors were selected to describe the degradation characteristics of bearings. Aiming at the problems of large amounts of data, large amounts of information redundancy and unclear performance index of multi-dimensional feature vectors, the dimensionality of multi-dimensional feature vectors was reduced with principal component analysis, thus, simplifying the multi-dimensional feature vectors and reducing the information redundancy. Finally, in view of the support vector machine (SVM)’s needs to determine kernel function parameters and penalty factors, the squirrel optimization algorithm (SOA) was used to adaptively select parameters and establish the state-life evaluation model of rolling bearings. In addition, mean absolute error (MAE) and root mean squared error (RMSE) were used to comprehensively evaluate SOA. The results showed that the SOA reduced the errors by 5.1% and 13.6%, respectively, compared with a genetic algorithm (GA). Compared with particle swarm optimization (PSO), the error of SOA was reduced by 7.6% and 15.9%, respectively. It showed that SOA-SVM effectively improved the adaptability and regression performance of SVM, thus, significantly improving the prediction accuracy

Relevant Feedback Based Accurate and Intelligent Retrieval on Capturing User Intention for Personalized Websites

With the rapid growth of networking, cyber–physical–social systems (CPSSs) provide vast amounts of information. Aimed at the huge and complex data provided by networking, obtaining valuable information to meet precise search needs when capturing user intention has become a major challenge, especially in personalized websites. General search engines face difficulties in addressing the challenges brought by this exploding amount of information. In this paper, we use real-time location and relevant feedback technology to design and implement an efficient, configurable, and intelligent retrieval framework for personalized websites in CPSSs. To improve the retrieval results, this paper also proposes a strategy of implicit relevant feedback based on click-through data analysis, which can obtain the relationship between the user query conditions and retrieval results. Finally, this paper designs a personalized PageRank algorithm including modified parameters to improve the ranking quality of the retrieval results using the relevant feedback from other users in the interest group. Experiments illustrate that the proposed accurate and intelligent retrieval framework improves the user experience

Retrieval of Phytoplankton Pigment Composition from Their In Vivo Absorption Spectra

Algal pigment composition is an indicator of phytoplankton community structure that can be estimated from optical observations. Assessing the potential capability to retrieve different types of pigments from phytoplankton absorption is critical for further applications. This study investigated the performance of three models and the utility of hyperspectral in vivo phytoplankton absorption spectra for retrieving pigment composition using a large database (n = 1392). Models based on chlorophyll-a (Chl-a model), Gaussian decomposition (Gaussian model), and partial least squares (PLS) regression (PLS model) were compared. Both the Gaussian model and the PLS model were applied to hyperspectral phytoplankton absorption data. Statistical analysis revealed the advantages and limitations of each model. The Chl-a model performed well for chlorophyll-c (Chl-c), diadinoxanthin, fucoxanthin, photosynthetic carotenoids (PSC), and photoprotective carotenoids (PPC), with a median absolute percent difference for cross-validation (MAPDCV) CV < 43%). The performance of the PLS model was comparable to that of the Chl-a model, and it exhibited improved retrievals of chlorophyll-b, alloxanthin, peridinin, and zeaxanthin. Additional work undertaken with the PLS model revealed the prospects of hyperspectral-resolution data and spectral derivative analyses for retrieving marker pigment concentrations. This study demonstrated the applicability of in situ hyperspectral phytoplankton absorption data for retrieving pigment composition and provided useful insights regarding the development of bio-optical algorithms from hyperspectral and satellite-based ocean-colour observations

Study on Influencing Factors of the Information Content of Satellite Remote-Sensing Aerosol Vertical Profiles Using Oxygen A-Band

Aerosol vertical distribution is decisive and hard to be constrained. It is of great significance for the study of atmospheric climate and environment. Oxygen absorption A-bands (755&ndash;775 nm) provide a unique opportunity to acquire vertical aerosol profiles from satellites over a large spatial coverage. To investigate the ability of O2 A-bands in retrieving aerosol vertical distribution, the dependence of retrieval on satellite observation geometry, spectral resolution, signal-to-noise ratio (SNR), size distribution, and a priori knowledge is quantified using information content theory. This work uses the radiative transfer model UNL to simulate four aerosol modes and the instrument noise model. The simulations show that a small scattering angle leads to an increase in the total amount of observed aerosol profile information, with the degrees freedom of signal (DFS) of a single band increasing from 0.4 to 0.85 at high spectral resolution (0.01 nm). The total DFS value of O2 A-bands varies accordingly between 1.2&ndash;2.3 to 3.8&ndash;5.1 when the spectral resolution increases from 1 nm to 0.01 nm. The spectral resolution has a greater impact on DFS value than the impact from SNR (an improvement of roughly 41&ndash;53% resulted from the change in spectral resolution and the SNR led to 13&ndash;18%). The retrieval is more sensitive to aerosols with a coarse-dominated mode. The improvement in spectral resolution on information acquisition is demonstrated using the DFS and the posterior error at various previous errors and resolutions

Bio-Inspired Self-Organized Fission&ndash;Fusion Control Algorithm for UAV Swarm

Swarm control has become a challenging topic for the current unmanned aerial vehicle (UAV) swarm due to its conflicting individual behaviors and high external interference. However, in contrast to static obstacles, limited attention has been paid to the fission&ndash;fusion behavior of the swarm against dynamic obstacles. In this paper, inspired by the interaction mechanism and fission&ndash;fusion motion of starlings, we propose a Bio-inspired Self-organized Fission&ndash;fusion Control (BiSoFC) algorithm for the UAV swarm, where the number of UAVs in the sub-swarm is controllable. It solves the problem of swarm control under dynamic obstacle interference with the tracking function. Firstly, we establish the kinematic equations of the individual UAV and swarm controllers and introduce a fission&ndash;fusion control framework to achieve the fission&ndash;fusion movement of the UAV swarm with a lower communication load. Afterward, a sub-swarm selection algorithm is built upon the topological interaction structure. When a swarm is faced with different tasks, the swarm that can control the number of agents in a sub-swarm can accomplish the corresponding task with a more reasonable number of agents. Finally, we design a sub-swarm trapping algorithm with a tracking function for the dynamic obstacles. The simulation results show that the UAV swarm can self-organize fission sub-swarms to cope with dynamic obstacles under different disturbance situations, and successfully achieve the goal of protecting the parent swarm from dynamic obstacles. The experimental results prove the feasibility and effectiveness of our proposed control algorithm

Aerosol Retrieval Study from a Particulate Observing Scanning Polarimeter Onboard Gao-Fen 5B without Prior Surface Knowledge, Based on the Optimal Estimation Method

To meet the demand for the aerosol detection of single-angle and multi-band polarization instrument containing short-wave infrared bands, an inversion algorithm that makes full use of multi-band intensity and polarization information is proposed based on optimal estimation theory. This method uses the polarization information in the short-wave infrared band to perform surface and atmosphere decoupling without a prior information on the surface. This obtains the initial value of the aerosol, and then it uses the scalar information to obtain the final result. Moreover, the multi-band information of the instrument is used for decoupling the surface and atmospheric information, which avoids the inversion error caused by the untimely update of the surface reflectance database and the error of spatio-temporal matching. The measured data of the Particulate Observing Scanning Polarimeter (POSP) are used to test the proposed algorithm. Firstly, to verify the effectiveness of the algorithm under different surface conditions, four regions with large geographical differences (Beijing, Hefei, Baotou, and Taiwan) are selected for aerosol optical depth (AOD) inversion, and they are compared with the aerosol robotic network (AERONET) products of the nearby stations. The validation against the AERONET products produces high correlation coefficients of 0.982, 0.986, 0.718, and 0.989, respectively, which verifies the effectiveness of the algorithm in different regions. Further, we analyzed the effectiveness of the proposed algorithm under different pollution conditions. Regions with AOD >0.7 and AOD < 0.7 are screened by using the AOD products of the Moderate-Resolution Imaging Spectroradiomete (MODIS), and the AOD of the corresponding region is inverted using POSP data. It was found to be spatially consistent with the MODIS products. The correlation coefficient and root mean square error (RMSE) in the AOD high region were 0.802 and 0.217, respectively, and 0.944 and 0.022 in the AOD low region, respectively, which verified the effectiveness of the proposed algorithm under different pollution conditions