Self-Calibrated Cross Attention Network for Few-Shot Segmentation

The key to the success of few-shot segmentation (FSS) lies in how to effectively utilize support samples. Most solutions compress support foreground (FG) features into prototypes, but lose some spatial details. Instead, others use cross attention to fuse query features with uncompressed support FG. Query FG could be fused with support FG, however, query background (BG) cannot find matched BG features in support FG, yet inevitably integrates dissimilar features. Besides, as both query FG and BG are combined with support FG, they get entangled, thereby leading to ineffective segmentation. To cope with these issues, we design a self-calibrated cross attention (SCCA) block. For efficient patch-based attention, query and support features are firstly split into patches. Then, we design a patch alignment module to align each query patch with its most similar support patch for better cross attention. Specifically, SCCA takes a query patch as Q, and groups the patches from the same query image and the aligned patches from the support image as K&V. In this way, the query BG features are fused with matched BG features (from query patches), and thus the aforementioned issues will be mitigated. Moreover, when calculating SCCA, we design a scaled-cosine mechanism to better utilize the support features for similarity calculation. Extensive experiments conducted on PASCAL-5^i and COCO-20^i demonstrate the superiority of our model, e.g., the mIoU score under 5-shot setting on COCO-20^i is 5.6%+ better than previous state-of-the-arts. The code is available at https://github.com/Sam1224/SCCAN.Comment: This paper is accepted by ICCV'2

Tris(tetra­methyl­ammonium) tetra-μ2-sulfido-tetra­sulfidocopper(I)dimolyb­denum(VI) N,N-dimethyl­formamide solvate

The title compound, (C4H12N)3[CuMo2S8]·C3H7NO, was obtained from the self-assembly of tetra­thio­molybdate, tetra­methyl­ammonium nitrate and cuprous sulfide in dimethyl­formamide (DMF). The asymmetric unit contains three (NMe4)+ cations, one [Mo2S8Cu]3− anion and one DMF solvent mol­ecule, and no obvious inter­actions are observed between these species. The trinuclear anion can be viewed as fused [MoS4Cu]− units sharing a copper center. The geometric parameters of the trivalent anion are comparable to those reported for other related salts including isomorphous anions, namely (NEt4)2(PPh4)[Mo2S8Cu] (a) and (Ph3P=N=PPh3)2(NEt4)[W2S8Cu]·2CH3CN (b). However, the Mo—Cu—Mo angle is found to be 160.24 (3)° for the title salt, while this angle is 162.97 (2)° in (a) and the W—Cu—W angle is 170.3 (2)° in (b), indicating that the largest deviation from linearity is in the title compound

Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentation

Current methods for few-shot segmentation (FSSeg) have mainly focused on improving the performance of novel classes while neglecting the performance of base classes. To overcome this limitation, the task of generalized few-shot semantic segmentation (GFSSeg) has been introduced, aiming to predict segmentation masks for both base and novel classes. However, the current prototype-based methods do not explicitly consider the relationship between base and novel classes when updating prototypes, leading to a limited performance in identifying true categories. To address this challenge, we propose a class contrastive loss and a class relationship loss to regulate prototype updates and encourage a large distance between prototypes from different classes, thus distinguishing the classes from each other while maintaining the performance of the base classes. Our proposed approach achieves new state-of-the-art performance for the generalized few-shot segmentation task on PASCAL VOC and MS COCO datasets

Learn to Optimize Denoising Scores for 3D Generation: A Unified and Improved Diffusion Prior on NeRF and 3D Gaussian Splatting

We propose a unified framework aimed at enhancing the diffusion priors for 3D generation tasks. Despite the critical importance of these tasks, existing methodologies often struggle to generate high-caliber results. We begin by examining the inherent limitations in previous diffusion priors. We identify a divergence between the diffusion priors and the training procedures of diffusion models that substantially impairs the quality of 3D generation. To address this issue, we propose a novel, unified framework that iteratively optimizes both the 3D model and the diffusion prior. Leveraging the different learnable parameters of the diffusion prior, our approach offers multiple configurations, affording various trade-offs between performance and implementation complexity. Notably, our experimental results demonstrate that our method markedly surpasses existing techniques, establishing new state-of-the-art in the realm of text-to-3D generation. Furthermore, our approach exhibits impressive performance on both NeRF and the newly introduced 3D Gaussian Splatting backbones. Additionally, our framework yields insightful contributions to the understanding of recent score distillation methods, such as the VSD and DDS loss

Sculpt3D: Multi-View Consistent Text-to-3D Generation with Sparse 3D Prior

Recent works on text-to-3d generation show that using only 2D diffusion supervision for 3D generation tends to produce results with inconsistent appearances (e.g., faces on the back view) and inaccurate shapes (e.g., animals with extra legs). Existing methods mainly address this issue by retraining diffusion models with images rendered from 3D data to ensure multi-view consistency while struggling to balance 2D generation quality with 3D consistency. In this paper, we present a new framework Sculpt3D that equips the current pipeline with explicit injection of 3D priors from retrieved reference objects without re-training the 2D diffusion model. Specifically, we demonstrate that high-quality and diverse 3D geometry can be guaranteed by keypoints supervision through a sparse ray sampling approach. Moreover, to ensure accurate appearances of different views, we further modulate the output of the 2D diffusion model to the correct patterns of the template views without altering the generated object's style. These two decoupled designs effectively harness 3D information from reference objects to generate 3D objects while preserving the generation quality of the 2D diffusion model. Extensive experiments show our method can largely improve the multi-view consistency while retaining fidelity and diversity. Our project page is available at: https://stellarcheng.github.io/Sculpt3D/.Comment: Accepted by CVPR 2024. Project Page: https://stellarcheng.github.io/Sculpt3D

Effect of grape pomace supplement on growth performance, gastrointestinal microbiota, and methane production in Tan lambs

Grape pomace (GP), a by-product in wine production, is nutritious and can be used as a feed ingredient for ruminants; however, its role in shaping sheep gastrointestinal tract (GIT) microbiota is unclear. We conducted a controlled trial using a randomized block design with 10 Tan lambs fed a control diet (CD) and 10 Tan lambs fed a pelleted diet containing 8% GP (dry matter basis) for 46 days. Rumen, jejunum, cecum, and colon bacterial and archaeal composition were identified by 16S rRNA gene sequencing. Dry matter intake (DMI) was greater (p < 0.05) in the GP than CD group; however, there was no difference in average daily gain (ADG, p < 0.05) and feed conversion ratio (FCR, p < 0.05) between the two groups. The GP group had a greater abundance of Prevotella 1 and Prevotella 7 in the rumen; of Sharpe, Ruminococcaceae 2, and [Ruminococcus] gauvreauii group in the jejunum; of Ruminococcaceae UCG-014 and Romboutsia in the cecum, and Prevotella UCG-001 in the colon; but lesser Rikenellaceae RC9 gut group in the rumen and cecum, and Ruminococcaceae UCG-005 and Ruminococcaceae UCG-010 in the colon than the CD group. The pathways of carbohydrate metabolism, such as L-rhamnose degradation in the rumen, starch and glycogen degradation in the jejunum, galactose degradation in the cecum, and mixed acid fermentation and mannan degradation in the colon were up-graded; whereas, the pathways of tricarboxylic acid (TCA) cycle VIII, and pyruvate fermentation to acetone in the rumen and colon were down-graded with GP. The archaeal incomplete reductive TCA cycle was enriched in the rumen, jejunum, and colon; whereas, the methanogenesis from H2 and CO2, the cofactors of methanogenesis, including coenzyme M, coenzyme B, and factor 420 biosynthesis were decreased in the colon. The study concluded that a diet including GP at 8% DM did not affect ADG or FCR in Tan lambs. However, there were some potential benefits, such as enhancing propionate production by microbiota and pathways in the GIT, promoting B-vitamin production in the rumen, facilitating starch degradation and amino acid biosynthesis in the jejunum, and reducing methanogenesis in the colon

Molecular Storage of Mg Ions with Vanadium Oxide Nanoclusters

Mg batteries have potential advantages in terms of safety, cost, and reliability over existing battery technologies, but their practical implementations are hindered by the lack of amenable high-voltage cathode materials. The development of cathode materials is complicated by limited understandings of the unique divalent Mg²⁺ ion electrochemistry and the interaction/transportation of Mg²⁺ ions with host materials. Here, it is shown that highly dispersed vanadium oxide (V₂O₅) nanoclusters supported on porous carbon frameworks are able to react with Mg²⁺ ions reversibly in electrolytes that are compatible with Mg metal, and exhibit high capacities and good reaction kinetics. They are able to deliver initial capacities exceeding 300 mAh g⁻¹ at 40 mA g⁻¹ in the voltage window of 0.5 to 2.8 V. The combined electron microscope, spectroscopy, and electrochemistry characterizations suggest a surface-controlled pseudocapacitive electrochemical reaction, and may be best described as a molecular energy storage mechanism. This work can provide a new approach of using the molecular mechanism for pseudocapacitive storage of Mg²⁺ for Mg batteries cathode materials

Baichuan 2: Open Large-scale Language Models

Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering. However, most powerful LLMs are closed-source or limited in their capability for languages other than English. In this technical report, we present Baichuan 2, a series of large-scale multilingual language models containing 7 billion and 13 billion parameters, trained from scratch, on 2.6 trillion tokens. Baichuan 2 matches or outperforms other open-source models of similar size on public benchmarks like MMLU, CMMLU, GSM8K, and HumanEval. Furthermore, Baichuan 2 excels in vertical domains such as medicine and law. We will release all pre-training model checkpoints to benefit the research community in better understanding the training dynamics of Baichuan 2.Comment: Baichuan 2 technical report. Github: https://github.com/baichuan-inc/Baichuan

The Ubiquitin Peptidase UCHL1 Induces G0/G1 Cell Cycle Arrest and Apoptosis Through Stabilizing p53 and Is Frequently Silenced in Breast Cancer

Background: Breast cancer (BrCa) is a complex disease driven by aberrant gene alterations and environmental factors. Recent studies reveal that abnormal epigenetic gene regulation also plays an important role in its pathogenesis. Ubiquitin carboxyl- terminal esterase L1 (UCHL1) is a tumor suppressor silenced by promoter methylation in multiple cancers, but its role and alterations in breast tumorigenesis remain unclear. Methodology/Principal Findings: We found that UCHL1 was frequently downregulated or silenced in breast cancer cell lines and tumor tissues, but readily expressed in normal breast tissues and mammary epithelial cells. Promoter methylation of UCHL1 was detected in 9 of 10 breast cancer cell lines (90%) and 53 of 66 (80%) primary tumors, but rarely in normal breast tissues, which was statistically correlated with advanced clinical stage and progesterone receptor status. Pharmacologic demethylation reactivated UCHL1 expression along with concomitant promoter demethylation. Ectopic expression of UCHL1 significantly suppressed the colony formation and proliferation of breast tumor cells, through inducing G0/G1 cell cycle arrest and apoptosis. Subcellular localization study showed that UCHL1 increased cytoplasmic abundance of p53. We further found that UCHL1 induced p53 accumulation and reduced MDM2 protein level, and subsequently upregulated the expression of p21, as well as cleavage of caspase3 and PARP, but not in catalytic mutant UCHL1 C90Sexpressed cells