Methanesulfonate in the firn of King George Island, Antarctica

Methanesulfonate was investigated as a potential contributor to the sulfur budget, based on analysis of a firn core from Collins Ice Cap, King George Island, Antarctica (62°10′ S, 58°50′ W). The anion was found to be present at a mean concentration of 0.17 μeq L−1, with a maximum of 0.73 μeq L−1. Dating based on the δ 18O profile suggests that the principal peaks of methanesulfonate are associated with snow deposited in summer and autumn. A careful examination of MSA, SO4 2− and nssSO4 2− profiles indicates that two of the three peaks in the MSA profile may result mainly from migration and relocation of MSA. The mechanism responsible for this might be similar to that for deep cores from other Antarctic glaciers, supporting the migration hypothesis proposed by prior researchers and extending it to near-temperate ice. Due to the post-depositional modification, the main part of the MSA profile of the firn is no longer indicative of the seasonal pattern of MSA in the atmosphere, and the basis for calculation of the MSA/nssSO4 2− ratio should be changed. The MSA/nssS04 2 ratio obtained by a new computation is 0.22, 10% higher than that ignoring the effect of MSA migration

Confidence-guided Centroids for Unsupervised Person Re-Identification

Unsupervised person re-identification (ReID) aims to train a feature extractor for identity retrieval without exploiting identity labels. Due to the blind trust in imperfect clustering results, the learning is inevitably misled by unreliable pseudo labels. Albeit the pseudo label refinement has been investigated by previous works, they generally leverage auxiliary information such as camera IDs and body part predictions. This work explores the internal characteristics of clusters to refine pseudo labels. To this end, Confidence-Guided Centroids (CGC) are proposed to provide reliable cluster-wise prototypes for feature learning. Since samples with high confidence are exclusively involved in the formation of centroids, the identity information of low-confidence samples, i.e., boundary samples, are NOT likely to contribute to the corresponding centroid. Given the new centroids, current learning scheme, where samples are enforced to learn from their assigned centroids solely, is unwise. To remedy the situation, we propose to use Confidence-Guided pseudo Label (CGL), which enables samples to approach not only the originally assigned centroid but other centroids that are potentially embedded with their identity information. Empowered by confidence-guided centroids and labels, our method yields comparable performance with, or even outperforms, state-of-the-art pseudo label refinement works that largely leverage auxiliary information

Any-Size-Diffusion: Toward Efficient Text-Driven Synthesis for Any-Size HD Images

Stable diffusion, a generative model used in text-to-image synthesis, frequently encounters resolution-induced composition problems when generating images of varying sizes. This issue primarily stems from the model being trained on pairs of single-scale images and their corresponding text descriptions. Moreover, direct training on images of unlimited sizes is unfeasible, as it would require an immense number of text-image pairs and entail substantial computational expenses. To overcome these challenges, we propose a two-stage pipeline named Any-Size-Diffusion (ASD), designed to efficiently generate well-composed images of any size, while minimizing the need for high-memory GPU resources. Specifically, the initial stage, dubbed Any Ratio Adaptability Diffusion (ARAD), leverages a selected set of images with a restricted range of ratios to optimize the text-conditional diffusion model, thereby improving its ability to adjust composition to accommodate diverse image sizes. To support the creation of images at any desired size, we further introduce a technique called Fast Seamless Tiled Diffusion (FSTD) at the subsequent stage. This method allows for the rapid enlargement of the ASD output to any high-resolution size, avoiding seaming artifacts or memory overloads. Experimental results on the LAION-COCO and MM-CelebA-HQ benchmarks demonstrate that ASD can produce well-structured images of arbitrary sizes, cutting down the inference time by 2x compared to the traditional tiled algorithm

Methanesulfonate in the firn of King George Island, Antarctica

Methanesulfonate was investigated as a potential contributor to the sulfur budget, based on analysis of a firn core from Collins Ice Cap, King George Island, Antarctica (62°10\u27 S, 58°50\u27 W). The anion was found to be present at a mean concentration of 0.17 μeq L-1, with a maximum of 0.73 μeq L-1. Dating based on the δ18O profile suggests that the principal peaks of methanesulfonate are associated with snow deposited in summer and autumn. A careful examination of MSA, SO42-and nssSO42- profiles indicates that two of the three peaks in the MSA profile mayresult mainlyfrom migration and relocation of MSA. The mechanism responsible for this might be similar to that for deep cores from other Antarctic glaciers, supporting the migration hypothesis proposed by prior researchers and extending it to near-temperate ice. Due to the post-depositional modification, the main part of the MSA profile of the firn is no longer indicative of the seasonal pattern of MSA in the atmosphere, and the basis for calculation of the MSA/nssSO42- ratio should be changed. The MSA/nssSO42- ratio obtained bya new computation is 0.22, 10% higher than that ignoring the effect of MSA migration

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

Antenna Grouping Assisted Spatial Modulation for mmWave-based UAV-BS

—The flexible deployment without new infrastructure makes unmanned aerial vehicles employing as base stations (UAV-BS) promising for many application. Since the signals in millimeter-wave frequencies have very small wavelengths, large antenna arrays can be placed in the UAV-BS. Thus, the UAV-BS is capable of providing abundant spatial resources. Spatial modulation (SM) is an effective technology in exploiting additional capacity of the spatial domain by transmitting antenna indices as virtual bits information. However, a limitation of the classical SM is a single transmit antenna activated at each time slot. As a result, the multiplexing gain offered by the multiple transmit antennas has a significantly loss. Generalised spatial modulation (GSM) allows several antennas to be activated to overcome the problem of SM. However, GSM has a improvement in throughput, while suffers from the performance loss resulting from the channel correlation, which is generated by multiple active antennas. Thus, the grouping SM (GrSM) is utilized to offer spatial capacity for the UAV-BS in mmWave frequency. Specially, the transmit antennas of the UAV-BS are partitioned into groups based on their channel characteristics. The SM is adopted by each group, and the multiplexing gain is achieved across groups. Moreover, the deployment of the UAV-BS has significantly influence on the throughput of the system. In this paper, we formulate a problem to maximize the achievable sum rate of the ground user. The GrSM scheme is utilized to obtain extra throughput in spatial domain. Since the dimension of the UAV position is not very high, a grid based exhaustive search method is adopted to solve the optimization problem. Simulation results demonstrate the proposed solution has an improvement in terms of the sum rate performance