Investigation of Different Image Super Resolution Methods on Paired Electron Microscopic Images

This thesis is concerned with investigating super-resolution algorithms and solutions for handling electron microscopic images. Please note two main aspects differentiating the problem discussed here from those considered in the literature. The first difference is that in the electron imaging setting, a pair of physical high-resolution and low-resolution images is used, rather than a physical image with its downsampled counterpart. The high-resolution image covers about 25\% of the view field of the low-resolution image, and the objective is to enhance the area of the low-resolution image where there is no high-resolution counterpart. The second difference is that the physics behind electron imaging is different from that of optical (visible light) photos. The implication is that super-resolution models trained by optical photos are not effective when applied to electron images. Focusing on the unique properties, a global and local registration method is devised to match the high- and low-resolution image patches and different training strategies are discussed for applying deep learning and non deep learning based super-resolution methods to the paired electron images. This thesis investigates the uniqueness of the super-resolution problem on paired electron microscopic images. After extensive experimentation and comparison on 22 pairs of electron images, it is now believed that the self-training strategy, in which the training images come from the same image pair of the test set, leads to better super-resolution outcomes, despite the relatively small training data size. Deep learning-based super-resolution methods show the best performances, whereas a revised paired library-based non-local mean method shows advantage in training time and interpretability. Paired images super-resolution has important implications in many research areas. Paired electron images are rather common in scientific experiments, especially in material and medical research. Due to the destructive imaging process while using electron sources, researchers tend to use low-energy beams or subject the samples to a short duration of exposure to protect the sample. As a consequence, low-resolution images are generated. Super-resolution methods, which can subsequently boost these low-resolution images to a higher resolution, are much desired in scientific researches using electron imaging

DreamVideo: High-Fidelity Image-to-Video Generation with Image Retention and Text Guidance

Image-to-video generation, which aims to generate a video starting from a given reference image, has drawn great attention. Existing methods try to extend pre-trained text-guided image diffusion models to image-guided video generation models. Nevertheless, these methods often result in either low fidelity or flickering over time due to their limitation to shallow image guidance and poor temporal consistency. To tackle these problems, we propose a high-fidelity image-to-video generation method by devising a frame retention branch based on a pre-trained video diffusion model, named DreamVideo. Instead of integrating the reference image into the diffusion process at a semantic level, our DreamVideo perceives the reference image via convolution layers and concatenates the features with the noisy latents as model input. By this means, the details of the reference image can be preserved to the greatest extent. In addition, by incorporating double-condition classifier-free guidance, a single image can be directed to videos of different actions by providing varying prompt texts. This has significant implications for controllable video generation and holds broad application prospects. We conduct comprehensive experiments on the public dataset, and both quantitative and qualitative results indicate that our method outperforms the state-of-the-art method. Especially for fidelity, our model has a powerful image retention ability and delivers the best results in UCF101 compared to other image-to-video models to our best knowledge. Also, precise control can be achieved by giving different text prompts. Further details and comprehensive results of our model will be presented in https://anonymous0769.github.io/DreamVideo/

Research and Application on Spark Clustering Algorithm in Campus Big Data Analysis

Big data analysis has penetrated into all fields of society and has brought about profound changes. However, there is relatively little research on big data supporting student management regarding college and university’s big data. Taking the student card information as the research sample, using spark big data mining technology and K-Means clustering algorithm, taking scholarship evaluation as an example, the big data is analyzed. Data includes analysis of students’ daily behavior from multiple dimensions, and it can prevent the unreasonable scholarship evaluation caused by unfair factors such as plagiarism, votes of teachers and students, etc. At the same time, students’ absenteeism, physical health and psychological status in advance can be predicted, which makes student management work more active, accurate and effective

Fuse Your Latents: Video Editing with Multi-source Latent Diffusion Models

Latent Diffusion Models (LDMs) are renowned for their powerful capabilities in image and video synthesis. Yet, video editing methods suffer from insufficient pre-training data or video-by-video re-training cost. In addressing this gap, we propose FLDM (Fused Latent Diffusion Model), a training-free framework to achieve text-guided video editing by applying off-the-shelf image editing methods in video LDMs. Specifically, FLDM fuses latents from an image LDM and an video LDM during the denoising process. In this way, temporal consistency can be kept with video LDM while high-fidelity from the image LDM can also be exploited. Meanwhile, FLDM possesses high flexibility since both image LDM and video LDM can be replaced so advanced image editing methods such as InstructPix2Pix and ControlNet can be exploited. To the best of our knowledge, FLDM is the first method to adapt off-the-shelf image editing methods into video LDMs for video editing. Extensive quantitative and qualitative experiments demonstrate that FLDM can improve the textual alignment and temporal consistency of edited videos

Printing surface charge as a new paradigm to program droplet transport

Directed, long-range and self-propelled transport of droplets on solid surfaces, especially on water repellent surfaces, is crucial for many applications from water harvesting to bio-analytical devices. One appealing strategy to achieve the preferential transport is to passively control the surface wetting gradients, topological or chemical, to break the asymmetric contact line and overcome the resistance force. Despite extensive progress, the directional droplet transport is limited to small transport velocity and short transport distance due to the fundamental trade-off: rapid transport of droplet demands a large wetting gradient, whereas long-range transport necessitates a relatively small wetting gradient. Here, we report a radically new strategy that resolves the bottleneck through the creation of an unexplored gradient in surface charge density (SCD). By leveraging on a facile droplet printing on superamphiphobic surfaces as well as the fundamental understanding of the mechanisms underpinning the creation of the preferential SCD, we demonstrate the self-propulsion of droplets with a record-high velocity over an ultra-long distance without the need for additional energy input. Such a Leidenfrost-like droplet transport, manifested at ambient condition, is also genetic, which can occur on a variety of substrates such as flexible and vertically placed surfaces. Moreover, distinct from conventional physical and chemical gradients, the new dimension of gradient in SCD can be programmed in a rewritable fashion. We envision that our work enriches and extends our capability in the manipulation of droplet transport and would find numerous potential applications otherwise impossible.Comment: 11 pages, 4 figure

Towards Neural Mixture Recommender for Long Range Dependent User Sequences

Understanding temporal dynamics has proved to be highly valuable for accurate recommendation. Sequential recommenders have been successful in modeling the dynamics of users and items over time. However, while different model architectures excel at capturing various temporal ranges or dynamics, distinct application contexts require adapting to diverse behaviors. In this paper we examine how to build a model that can make use of different temporal ranges and dynamics depending on the request context. We begin with the analysis of an anonymized Youtube dataset comprising millions of user sequences. We quantify the degree of long-range dependence in these sequences and demonstrate that both short-term and long-term dependent behavioral patterns co-exist. We then propose a neural Multi-temporal-range Mixture Model (M3) as a tailored solution to deal with both short-term and long-term dependencies. Our approach employs a mixture of models, each with a different temporal range. These models are combined by a learned gating mechanism capable of exerting different model combinations given different contextual information. In empirical evaluations on a public dataset and our own anonymized YouTube dataset, M3 consistently outperforms state-of-the-art sequential recommendation methods.Comment: Accepted at WWW 201

Isolation, purification and PEG-mediated transient expression of mesophyll protoplasts in Camellia oleifera

Background: Camellia oleifera (C. oleifera) is a woody edible oil crop of great economic importance. Because of the lack of modern biotechnology research, C. oleifera faces huge challenges in both breeding and basic research. The protoplast and transient transformation system plays an important role in biological breeding, plant regeneration and somatic cell fusion. The objective of this present study was to develop a highly efficient protocol for isolating and purifying mesophyll protoplasts and transient transformation of C. oleifera. Several critical factors for mesophyll protoplast isolation from C. oleifera, including starting material (leaf age), pretreatment, enzymatic treatment (type of enzyme, concentration and digestion time), osmotic pressure and purification were optimized. Then the factors affecting the transient transformation rate of mesophyll protoplasts such as PEG molecular weights, PEG4000 concentration, plasmid concentration and incubation time were explored.Results: The in vitro grown seedlings of C. oleifera 'Huashuo' were treated in the dark for 24 h, then the 1st to 2nd true leaves were picked and vacuumed at - 0.07 MPa for 20 min. The maximum yield (3.5 x 10(7)/g.W) and viability (90.9%) of protoplast were reached when the 1st to 2nd true leaves were digested in the enzymatic solution containing1.5% (w/v) Cellulase R-10, 0.5% (w/v) Macerozyme R-10 and 0.25% (w/v) Snailase and 0.4 M mannitol for 10 h. Moreover, the protoplast isolation method was also applicable to the other two cultivars, the protoplast yield for 'TXP14' and 'DP47' was 1.1 x 10(7)/g.FW and 2.6 x 10(7)/g. FW, the protoplast viability for 'TXP14' and 'DP47' was 90.0% and 88.2%. The purification effect was the best when using W buffer as a cleaning agent by centrifugal precipitation. The maximum transfection efficiency (70.6%) was obtained with the incubation of the protoplasts with 15 mu g plasmid and 40% PEG4000 for 20 min.Conclusion: In summary, a simple and efficient system for isolation and transient transformation of C. oleifera mesophyll protoplast is proposed, which is of great significance in various aspects of C. oleifera research, including the study of somatic cell fusion, genome editing, protein function, signal transduction, transcriptional regulation and multi-omics analyses