CD2^2: Fine-grained 3D Mesh Reconstruction with Twice Chamfer Distance

Monocular 3D reconstruction is to reconstruct the shape of object and its other information from a single RGB image. In 3D reconstruction, polygon mesh, with detailed surface information and low computational cost, is the most prevalent expression form obtained from deep learning models. However, the state-of-the-art schemes fail to directly generate well-structured meshes, and most of meshes have two severe problems Vertices Clustering (VC) and Illegal Twist (IT). By diving into the mesh deformation process, we pinpoint that the inappropriate usage of Chamfer Distance (CD) loss is the root causes of VC and IT problems in the training of deep learning model. In this paper, we initially demonstrate these two problems induced by CD loss with visual examples and quantitative analyses. Then, we propose a fine-grained reconstruction method CD$^2$ by employing Chamfer distance twice to perform a plausible and adaptive deformation. Extensive experiments on two 3D datasets and comparisons with five latest schemes demonstrate that our CD$^2$ directly generates well-structured meshes and outperforms others by alleviating VC and IT problems.Comment: under major review in TOM

Federated Domain Generalization: A Survey

Machine learning typically relies on the assumption that training and testing distributions are identical and that data is centrally stored for training and testing. However, in real-world scenarios, distributions may differ significantly and data is often distributed across different devices, organizations, or edge nodes. Consequently, it is imperative to develop models that can effectively generalize to unseen distributions where data is distributed across different domains. In response to this challenge, there has been a surge of interest in federated domain generalization (FDG) in recent years. FDG combines the strengths of federated learning (FL) and domain generalization (DG) techniques to enable multiple source domains to collaboratively learn a model capable of directly generalizing to unseen domains while preserving data privacy. However, generalizing the federated model under domain shifts is a technically challenging problem that has received scant attention in the research area so far. This paper presents the first survey of recent advances in this area. Initially, we discuss the development process from traditional machine learning to domain adaptation and domain generalization, leading to FDG as well as provide the corresponding formal definition. Then, we categorize recent methodologies into four classes: federated domain alignment, data manipulation, learning strategies, and aggregation optimization, and present suitable algorithms in detail for each category. Next, we introduce commonly used datasets, applications, evaluations, and benchmarks. Finally, we conclude this survey by providing some potential research topics for the future

FusionAI: Decentralized Training and Deploying LLMs with Massive Consumer-Level GPUs

The rapid growth of memory and computation requirements of large language models (LLMs) has outpaced the development of hardware, hindering people who lack large-scale high-end GPUs from training or deploying LLMs. However, consumer-level GPUs, which constitute a larger market share, are typically overlooked in LLM due to their weaker computing performance, smaller storage capacity, and lower communication bandwidth. Additionally, users may have privacy concerns when interacting with remote LLMs. In this paper, we envision a decentralized system unlocking the potential vast untapped consumer-level GPUs in pre-training, inference and fine-tuning of LLMs with privacy protection. However, this system faces critical challenges, including limited CPU and GPU memory, low network bandwidth, the variability of peer and device heterogeneity. To address these challenges, our system design incorporates: 1) a broker with backup pool to implement dynamic join and quit of computing providers; 2) task scheduling with hardware performance to improve system efficiency; 3) abstracting ML procedures into directed acyclic graphs (DAGs) to achieve model and task universality; 4) abstracting intermediate represention and execution planes to ensure compatibility of various devices and deep learning (DL) frameworks. Our performance analysis demonstrates that 50 RTX 3080 GPUs can achieve throughputs comparable to those of 4 H100 GPUs, which are significantly more expensive

Effects of the standardized ileal digestible lysine to metabolizable energy ratio on performance and carcass characteristics of growing-finishing pigs

<p>Abstract</p> <p>A total of 2,121 growing-finishing pigs (Duroc × Landrace × Large White) were utilized in six experiments conducted to determine the effects of different ratios of standardized ileal digestible lysine (SID-Lys) to metabolizable energy (ME) on the performance and carcass characteristics of growing-finishing pigs. Exps. 1 (30 to 50 kg), 2 (52 to 70 kg) and 3 (81 to 104 kg) were conducted to find an optimum ME level and then this level was used in Exps. 4 (29 to 47 kg), 5 (54 to 76 kg) and 6 (84 to 109 kg) to test the response of pigs to different ratios of SID-Lys:ME. In Exps.1 to 3, four treatments were used consisting of diets with a formulated ME content of 3.1, 3.2, 3.3 or 3.4 in Exps. 1 and 2 while Exp. 3 used 3.05, 3.15, 3.25 or 3.35 Mcal/kg. A constant SID-Lys:ME ratio of 2.6, 2.3 or 2.0 g/Mcal was used in Exps. 1, 2 and 3, respectively. Weight gain was significantly increased with increasing energy level in Exp.1 while weight gain was unaltered in Exps. 2 and 3. For all three experiments, feed intake was decreased (<it>P </it>< 0.05) and feed efficiency was improved (<it>P </it>< 0.05) with increasing energy level. Tenth rib back fat thickness linearly increased (<it>P </it>< 0.05) with increasing energy level. In Exps. 4 to 6, five treatments were used consisting of diets with a SID-Lys:ME ratio of 2.4, 2.6, 2.8, 3.0 or 3.2 in Exp. 1, 2.1, 2.3, 2.5, 2.7, 2.9 or 3.2 in Exp. 2 and 1.8, 2.0, 2.2, 2.4, or 2.6 in Exp. 3. A constant ME level 3.2, 3.2 and 3.05 Mcal/kg was used in Exps. 1, 2 and 3, respectively (selected based on the results of weight gain). For all three experiments, weight gain increased (<it>P </it>< 0.05) and feed efficiency improved linearly (<it>P </it>< 0.05) as the SID-Lys:ME ratio increased. Tenth rib back fat thickness linearly decreased (<it>P </it>< 0.05) as the SID-Lys:ME ratio increased. Based on a straight broken-line model, the estimated SID-Lys:ME ratio to maximize weight gain was 3.0, 2.43 and 2.2 for 29 to 47, 54 to76 and 84 to 109 kg of pigs, respectively.</p

Improved Up-Conversion Luminescence from Er³⁺:LaF₃ Nanocrystals Embedded in Oxyfluoride Glass Ceramics via Simultaneous Triwavelength Excitation

Up-conversion (UC), harvesting near-infrared (NIR) sunlight, is highly desirable for photovoltaic (PV) cells. In regard to this concept, most of the reported experiments on UC materials and their applications, however, were conventionally studied on a monochromatic laser with a narrow excitation band, which is difficult to meet the requirement of solar spectrum conversion. Given the practical applications in PV cells, investigations for UC materials upon simultaneous multiwavelengths even broadband near-infrared (NIR) sunlight excitation are much more meaningful. Herein, we studied the UC luminescence properties of germanate oxyfluoride glass ceramics (GCs) containing LaF₃:Er³⁺ nanocrystals with lower phonon energy upon simultaneous triwavelength excitation. The UC emission intensities upon simultaneous triwavelength excitation were drastically enhanced in comparison with the case of that by monochromatic excitation. The UC luminescence mechanisms were interpreted in-depth in terms of synergetic UC effect owing to the perturbation in the excited states established by different excitation wavelengths. We demonstrated the application of the simultaneous triwavelength excited GC by adding it to the rear face of thin-film hydrogenated amorphous silicon (a-Si:H) solar cells. The photoactive current generated by the reflected UC light upon simultaneous triwavelength excitation was dramatically enhanced in contrast to the case of that upon monochromatic excitation. This Er³⁺-doped germanate oxyfluoride GC, harvesting broader NIR sunlight photons via simultaneous multiwavelength excitation, has colossal potential to improve the power conversion efficiency in PV cells in the near future