Global regularity to the Navier-Stokes equations for a class of large initial data

In [5], Chemin, Gallagher and Paicu proved the global regularity of solutions to the classical Navier-Stokes equations with a class of large initial data on T2 × R. This data varies slowly in vertical variable and has a norm which blows up as the small parameter ( represented by ǫ in the paper) tends to zero. However, to the best of our knowledge, the result is still unclear for the whole spaces R3. In this paper, we consider the generalized Navier-Stokes equations on Rn(n ≥ 3): ∂tu + u · ∇u + Dsu + ∇P = 0, div u = 0. For some suitable number s, we prove that the Cauchy problem with initial data of the form u0ǫ(x) = (v0h(xǫ), ǫ−1v0n(xǫ))T , xǫ = (xh, ǫxn)T , is globally well-posed for all small ǫ > 0, provided that the initial velocity profile v0 is analytic in xn and certain norm of v0 is sufficiently small but independent of ǫ. In particular, our result is true for the n-dimensional classical Navier-Stokes equations with n ≥ 4 and the fractional Navier-Stokes equations with 1 ≤ s < 2 in 3D

Mobile Platform for livestock monitoring and inspection.

Livestock keepers acquire and manage information (e.g. identification numbers, images, etc.) about livestock to identify and keep track of livestock using systems with capabilities to extract such information. Examples of such systems are Radio Frequency Identification (RFID) systems which are used to collect and transmit livestock's information to host devices. Sophisticated RFID readers are very expensive, and more functional than the cheap ones whose use are mostly limited to reading and transmission of tag IDs. Cross-platform mobile applications will allow monitoring of livestock irrespective of the platform on which mobile devices are being operated. Farmers' secured access to records via web services is not limited to a device as they can login on any mobile device with the installed application. In this work, a mobile platform which consists of a cross-platform mobile application, webservice and database is developed to cost-effectively manage and exploit records of livestock acquired using a cheap RFID reader. The mobile application was developed using a Xamarin form framework. The programming language and development environment used are C# and Visual studio respectively. Records of livestock were acquired, posted, updated, deleted and retrieved from the database via a web service. Additional advantages offer by the solution implemented include, exporting of animals’ records via email and SMS, viewing of animal's record by scanning their tags or QR code of animals' passports, and login system to sign users in and out of the application. Development of RFID readers with sensors to acquire health-related parameters for health monitoring is recommended

Mask-Attention-Free Transformer for 3D Instance Segmentation

Recently, transformer-based methods have dominated 3D instance segmentation, where mask attention is commonly involved. Specifically, object queries are guided by the initial instance masks in the first cross-attention, and then iteratively refine themselves in a similar manner. However, we observe that the mask-attention pipeline usually leads to slow convergence due to low-recall initial instance masks. Therefore, we abandon the mask attention design and resort to an auxiliary center regression task instead. Through center regression, we effectively overcome the low-recall issue and perform cross-attention by imposing positional prior. To reach this goal, we develop a series of position-aware designs. First, we learn a spatial distribution of 3D locations as the initial position queries. They spread over the 3D space densely, and thus can easily capture the objects in a scene with a high recall. Moreover, we present relative position encoding for the cross-attention and iterative refinement for more accurate position queries. Experiments show that our approach converges 4x faster than existing work, sets a new state of the art on ScanNetv2 3D instance segmentation benchmark, and also demonstrates superior performance across various datasets. Code and models are available at https://github.com/dvlab-research/Mask-Attention-Free-Transformer.Comment: Accepted to ICCV 2023. Code and models are available at https://github.com/dvlab-research/Mask-Attention-Free-Transforme

DreamAvatar: Text-and-Shape Guided 3D Human Avatar Generation via Diffusion Models

We present DreamAvatar, a text-and-shape guided framework for generating high-quality 3D human avatars with controllable poses. While encouraging results have been reported by recent methods on text-guided 3D common object generation, generating high-quality human avatars remains an open challenge due to the complexity of the human body's shape, pose, and appearance. We propose DreamAvatar to tackle this challenge, which utilizes a trainable NeRF for predicting density and color for 3D points and pretrained text-to-image diffusion models for providing 2D self-supervision. Specifically, we leverage the SMPL model to provide shape and pose guidance for the generation. We introduce a dual-observation-space design that involves the joint optimization of a canonical space and a posed space that are related by a learnable deformation field. This facilitates the generation of more complete textures and geometry faithful to the target pose. We also jointly optimize the losses computed from the full body and from the zoomed-in 3D head to alleviate the common multi-face ''Janus'' problem and improve facial details in the generated avatars. Extensive evaluations demonstrate that DreamAvatar significantly outperforms existing methods, establishing a new state-of-the-art for text-and-shape guided 3D human avatar generation.Comment: Project page: https://yukangcao.github.io/DreamAvatar

GlyphControl: Glyph Conditional Control for Visual Text Generation

Recently, there has been a growing interest in developing diffusion-based text-to-image generative models capable of generating coherent and well-formed visual text. In this paper, we propose a novel and efficient approach called GlyphControl to address this task. Unlike existing methods that rely on character-aware text encoders like ByT5 and require retraining of text-to-image models, our approach leverages additional glyph conditional information to enhance the performance of the off-the-shelf Stable-Diffusion model in generating accurate visual text. By incorporating glyph instructions, users can customize the content, location, and size of the generated text according to their specific requirements. To facilitate further research in visual text generation, we construct a training benchmark dataset called LAION-Glyph. We evaluate the effectiveness of our approach by measuring OCR-based metrics and CLIP scores of the generated visual text. Our empirical evaluations demonstrate that GlyphControl outperforms the recent DeepFloyd IF approach in terms of OCR accuracy and CLIP scores, highlighting the efficacy of our method.Comment: Technical report. The codes will be released at https://github.com/AIGText/GlyphControl-releas

HeadSculpt: Crafting 3D Head Avatars with Text

Recently, text-guided 3D generative methods have made remarkable advancements in producing high-quality textures and geometry, capitalizing on the proliferation of large vision-language and image diffusion models. However, existing methods still struggle to create high-fidelity 3D head avatars in two aspects: (1) They rely mostly on a pre-trained text-to-image diffusion model whilst missing the necessary 3D awareness and head priors. This makes them prone to inconsistency and geometric distortions in the generated avatars. (2) They fall short in fine-grained editing. This is primarily due to the inherited limitations from the pre-trained 2D image diffusion models, which become more pronounced when it comes to 3D head avatars. In this work, we address these challenges by introducing a versatile coarse-to-fine pipeline dubbed HeadSculpt for crafting (i.e., generating and editing) 3D head avatars from textual prompts. Specifically, we first equip the diffusion model with 3D awareness by leveraging landmark-based control and a learned textual embedding representing the back view appearance of heads, enabling 3D-consistent head avatar generations. We further propose a novel identity-aware editing score distillation strategy to optimize a textured mesh with a high-resolution differentiable rendering technique. This enables identity preservation while following the editing instruction. We showcase HeadSculpt's superior fidelity and editing capabilities through comprehensive experiments and comparisons with existing methods.Comment: Webpage: https://brandonhan.uk/HeadSculpt

Rank-DETR for High Quality Object Detection

Modern detection transformers (DETRs) use a set of object queries to predict a list of bounding boxes, sort them by their classification confidence scores, and select the top-ranked predictions as the final detection results for the given input image. A highly performant object detector requires accurate ranking for the bounding box predictions. For DETR-based detectors, the top-ranked bounding boxes suffer from less accurate localization quality due to the misalignment between classification scores and localization accuracy, thus impeding the construction of high-quality detectors. In this work, we introduce a simple and highly performant DETR-based object detector by proposing a series of rank-oriented designs, combinedly called Rank-DETR. Our key contributions include: (i) a rank-oriented architecture design that can prompt positive predictions and suppress the negative ones to ensure lower false positive rates, as well as (ii) a rank-oriented loss function and matching cost design that prioritizes predictions of more accurate localization accuracy during ranking to boost the AP under high IoU thresholds. We apply our method to improve the recent SOTA methods (e.g., H-DETR and DINO-DETR) and report strong COCO object detection results when using different backbones such as ResNet-$50$, Swin-T, and Swin-L, demonstrating the effectiveness of our approach. Code is available at \url{https://github.com/LeapLabTHU/Rank-DETR}.Comment: NeurIPS 202

Design and implementation of IoT enabled generic platform for precision livestock farming and applications

This thesis focuses on precision agriculture of livestock farming. Precision Livestock Farming is a modern farming development that emphasises on deploying advanced information and communication technology in physical farms to optimise the contributions of individual animals. Relevant techniques such as the Internet of Things (IoT), machine learning and 5G communication are all needed for improving the level of automation, intelligence and efficiency towards smart farming.;With the support of more powerful computing resources, we are capable of handling massive volumes of data, where highly automated processes can be applied for data capturing, analysis and improved decision making. In addition to the economic benefits, Precision Livestock Farming also meets several social goals, such as high-quality and safe food products, efficient and sustainable livestock farming, better animal welfare and low footprint to the environment.;Manual observation of animals is a tedious job, especially over a long time, and it can often be affected by the observer's bias. As a result, IoT enabled AI machine learning-based automatic monitoring and management of livestock with image processing and analysis can offer massive potential for producing the unbiased status report in a more efficient and effective way. By utilizing the real-time monitoring technology, and the corresponding management system can boost productivity whilst reducing the cost and environmental emissions in livestock farms.;To tackle this emerging issue and needs, a Precision Livestock Farming platform has been designed and implemented in this thesis, with the support of various sensors and corresponding analytics technologies. By continuously recording and analysing live data, it can not only recognise the welfare and health status of the animals but also for evidence-based smart decision-making with the support of the massive volumes of data. For implementing the proposed system, various techniques have been introduced to address the challenging issues, especially real-time multi-camera video streaming and transmission in an on-demand manner for improving the efficiency and efficacy in mobile/cloud-based environments.;As a case study, the developed generic platform has been applied for tracking and behaviour recognition of pigs. These include background detection, object detection, object classification and target selection, followed by object tracking and behaviour recognition. The successful application has not only validated the efficacy of the proposed system but also demonstrated the flexibility and great potential of the proposed system in a wide range of application areas.;Finally, some future directions are also provided after the summary of the contribution points, which are expected to benefit the further development of the corresponding fields and the automation and intelligence of the livestock farming.This thesis focuses on precision agriculture of livestock farming. Precision Livestock Farming is a modern farming development that emphasises on deploying advanced information and communication technology in physical farms to optimise the contributions of individual animals. Relevant techniques such as the Internet of Things (IoT), machine learning and 5G communication are all needed for improving the level of automation, intelligence and efficiency towards smart farming.;With the support of more powerful computing resources, we are capable of handling massive volumes of data, where highly automated processes can be applied for data capturing, analysis and improved decision making. In addition to the economic benefits, Precision Livestock Farming also meets several social goals, such as high-quality and safe food products, efficient and sustainable livestock farming, better animal welfare and low footprint to the environment.;Manual observation of animals is a tedious job, especially over a long time, and it can often be affected by the observer's bias. As a result, IoT enabled AI machine learning-based automatic monitoring and management of livestock with image processing and analysis can offer massive potential for producing the unbiased status report in a more efficient and effective way. By utilizing the real-time monitoring technology, and the corresponding management system can boost productivity whilst reducing the cost and environmental emissions in livestock farms.;To tackle this emerging issue and needs, a Precision Livestock Farming platform has been designed and implemented in this thesis, with the support of various sensors and corresponding analytics technologies. By continuously recording and analysing live data, it can not only recognise the welfare and health status of the animals but also for evidence-based smart decision-making with the support of the massive volumes of data. For implementing the proposed system, various techniques have been introduced to address the challenging issues, especially real-time multi-camera video streaming and transmission in an on-demand manner for improving the efficiency and efficacy in mobile/cloud-based environments.;As a case study, the developed generic platform has been applied for tracking and behaviour recognition of pigs. These include background detection, object detection, object classification and target selection, followed by object tracking and behaviour recognition. The successful application has not only validated the efficacy of the proposed system but also demonstrated the flexibility and great potential of the proposed system in a wide range of application areas.;Finally, some future directions are also provided after the summary of the contribution points, which are expected to benefit the further development of the corresponding fields and the automation and intelligence of the livestock farming

THz MEMS Switch Design

In this work, an mm-wave/THz MEMS switch design process is presented. The challenges and solutions associated with the switch electrical design, modeling, fabrication, and test are explored and discussed. To investigate the feasibility of this design process, the switches are designed on both silicon and fused quartz substrate and then tested in the 140–750 GHz frequency range. The measurement fits design expectations and simulation well. At 750 GHz the measurement results from switches on both substrates have an ON state insertion loss of less than 3 dB and an OFF state isolation larger than 12 dB