SketchSampler: Sketch-based 3D Reconstruction via View-dependent Depth Sampling

Reconstructing a 3D shape based on a single sketch image is challenging due to the large domain gap between a sparse, irregular sketch and a regular, dense 3D shape. Existing works try to employ the global feature extracted from sketch to directly predict the 3D coordinates, but they usually suffer from losing fine details that are not faithful to the input sketch. Through analyzing the 3D-to-2D projection process, we notice that the density map that characterizes the distribution of 2D point clouds (i.e., the probability of points projected at each location of the projection plane) can be used as a proxy to facilitate the reconstruction process. To this end, we first translate a sketch via an image translation network to a more informative 2D representation that can be used to generate a density map. Next, a 3D point cloud is reconstructed via a two-stage probabilistic sampling process: first recovering the 2D points (i.e., the x and y coordinates) by sampling the density map; and then predicting the depth (i.e., the z coordinate) by sampling the depth values at the ray determined by each 2D point. Extensive experiments are conducted, and both quantitative and qualitative results show that our proposed approach significantly outperforms other baseline methods.Comment: 16 pages, 7 figures, accepted by ECCV 202

Inelastic Final-State Interactions of B→VV→πKB \to VV \to \pi K Processes

We study the final-state interactions in $B \to \pi K$ decays through $B \to VV \to \pi K$ processes where the inelastic rescattering occurs via single pion exchange. The next-to-leading order low energy effective Hamiltonian and BSW model are used to evaluate the weak transition matrix elements and the final-state interactions. We found that the final-state interaction effects in $B \to \rho K^* \to \pi K$ processes are significant. The Fleischer-Mannel relation about the CKM angle $\gamma$ may be significantly modified.Comment: Latex, 14 pages, with 1 figures and 2 table

The heme-p53 interaction: Linking iron metabolism to p53 signaling and tumorigenesis

Recently, we reported that heme binds to tumor suppressor p53 protein (TP53, best known as p53) and promotes its nuclear export and cytosolic degradation, whereas iron chelation stabilizes p53 protein and suppresses tumors in a p53-dependent manner. This not only provides mechanistic insights into tumorigenesis associated with iron excess, but also helps guide the administration of chemotherapy based on iron deprivation in the clinic

Diagnostic value of two dimensional shear wave elastography combined with texture analysis in early liver fibrosis.

BACKGROUND: Staging diagnosis of liver fibrosis is a prerequisite for timely diagnosis and therapy in patients with chronic hepatitis B. In recent years, ultrasound elastography has become an important method for clinical noninvasive assessment of liver fibrosis stage, but its diagnostic value for early liver fibrosis still needs to be further improved. In this study, the texture analysis was carried out on the basis of two dimensional shear wave elastography (2D-SWE), and the feasibility of 2D-SWE plus texture analysis in the diagnosis of early liver fibrosis was discussed. AIM: To assess the diagnostic value of 2D-SWE combined with textural analysis in liver fibrosis staging. METHODS: This study recruited 46 patients with chronic hepatitis B. Patients underwent 2D-SWE and texture analysis; Young\u27s modulus values and textural patterns were obtained, respectively. Textural pattern was analyzed with regard to contrast, correlation, angular second moment (ASM), and homogeneity. Pathological results of biopsy specimens were the gold standard; comparison and assessment of the diagnosis efficiency were conducted for 2D-SWE, texture analysis and their combination. RESULTS: 2D-SWE displayed diagnosis efficiency in early fibrosis, significant fibrosis, severe fibrosis, and early cirrhosis (AUC \u3e 0.7, P \u3c 0.05) with respective AUC values of 0.823 (0.678-0.921), 0.808 (0.662-0.911), 0.920 (0.798-0.980), and 0.855 (0.716-0.943). Contrast and homogeneity displayed independent diagnosis efficiency in liver fibrosis stage (AUC \u3e 0.7, P \u3c 0.05), whereas correlation and ASM showed limited values. AUC of contrast and homogeneity were respectively 0.906 (0.779-0.973), 0.835 (0.693-0.930), 0.807 (0.660-0.910) and 0.925 (0.805-0.983), 0.789 (0.639-0.897), 0.736 (0.582-0.858), 0.705 (0.549-0.883) and 0.798 (0.650-0.904) in four liver fibrosis stages, which exhibited equivalence to 2D-SWE in diagnostic efficiency (P \u3e 0.05). Combined diagnosis (PRE) displayed diagnostic efficiency (AUC \u3e 0.7, P \u3c 0.01) for all fibrosis stages with respective AUC of 0.952 (0.841-0.994), 0.896 (0.766-0.967), 0.978 (0.881-0.999), 0.947 (0.835-0.992). The combined diagnosis showed higher diagnosis efficiency over 2D-SWE in early liver fibrosis (P \u3c 0.05), whereas no significant differences were observed in other comparisons (P \u3e 0.05). CONCLUSION: Texture analysis was capable of diagnosing liver fibrosis stage, combined diagnosis had obvious advantages in early liver fibrosis, liver fibrosis stage might be related to the hepatic tissue hardness distribution

PanoView: An iterative clustering method for single-cell RNA sequencing data

Single-cell RNA-sequencing (scRNA-seq) provides new opportunities to gain a mechanistic understanding of many biological processes. Current approaches for single cell clustering are often sensitive to the input parameters and have difficulty dealing with cell types with different densities. Here, we present Panoramic View (PanoView), an iterative method integrated with a novel density-based clustering, Ordering Local Maximum by Convex hull (OLMC), that uses a heuristic approach to estimate the required parameters based on the input data structures. In each iteration, PanoView will identify the most confident cell clusters and repeat the clustering with the remaining cells in a new PCA space. Without adjusting any parameter in PanoView, we demonstrated that PanoView was able to detect major and rare cell types simultaneously and outperformed other existing methods in both simulated datasets and published single-cell RNA-sequencing datasets. Finally, we conducted scRNA-Seq analysis of embryonic mouse hypothalamus, and PanoView was able to reveal known cell types and several rare cell subpopulations

Diffusion Model is Secretly a Training-free Open Vocabulary Semantic Segmenter

Recent research has explored the utilization of pre-trained text-image discriminative models, such as CLIP, to tackle the challenges associated with open-vocabulary semantic segmentation. However, it is worth noting that the alignment process based on contrastive learning employed by these models may unintentionally result in the loss of crucial localization information and object completeness, which are essential for achieving accurate semantic segmentation. More recently, there has been an emerging interest in extending the application of diffusion models beyond text-to-image generation tasks, particularly in the domain of semantic segmentation. These approaches utilize diffusion models either for generating annotated data or for extracting features to facilitate semantic segmentation. This typically involves training segmentation models by generating a considerable amount of synthetic data or incorporating additional mask annotations. To this end, we uncover the potential of generative text-to-image conditional diffusion models as highly efficient open-vocabulary semantic segmenters, and introduce a novel training-free approach named DiffSegmenter. Specifically, by feeding an input image and candidate classes into an off-the-shelf pre-trained conditional latent diffusion model, the cross-attention maps produced by the denoising U-Net are directly used as segmentation scores, which are further refined and completed by the followed self-attention maps. Additionally, we carefully design effective textual prompts and a category filtering mechanism to further enhance the segmentation results. Extensive experiments on three benchmark datasets show that the proposed DiffSegmenter achieves impressive results for open-vocabulary semantic segmentation

SmartRep: Reducing Flow Completion Times with Minimal Replication in Data Centers

To improve users\u27 experience, TCP short flows that are heavily used in interactive services should be completed as soon as possible. In current data centers, large flows and head-of-line blocking in switches hinder short flows from completion, which leads to long-tailed flow completion times (FCT). Replicating short flows with multiple equal-cost paths is a promising way to reduce FCT. However, the original flow and its replicated one are quite likely to be routed to the same path (ECMP hash collision), which increases both the mean and 99-percentile FCT significantly. What\u27s more, inadequate replication leaves many other less-congested equal-cost paths unused and limits the performance while excess replication degrades throughput of large flows. To solve these problems, we propose SmartRep, a scheme consisting of an efficient and effective traceroute based hash collision avoidance method and an algorithm to decide the optimal number of replicated flows for different short flows. SmartRep can be easily implemented in software and readily deployed in data centers. Extensive NS2 simulations show that our approach improves previous replication-based work by 25%-50% in both mean and 99th percentile FCT, and meanwhile imposes negligible impact on large flows.Date of Conference: 8-12 June 2015Conference Location: Londo

Toxicities comparison of rotenone and acetone extract of Tephrosiavogelii and Derris trifoliate against Solenopsis invicta

The high rotenone content and the rotenone crude extract of Tephrosia vogelii and Derris trifoliata were evaluated for its efficacy in the control of red imported fire (RIFA), Solenopsis invicta under both laboratory and field conditions. The acetone extracts of D. trifoliata roots and T. vogelii leaves exhibited strong toxicity to macroergate and micrergate of RIFA. When active ingredients of the crude extracts were convert to rotenone, the activity of the acetone extracts were higher than that of rotenone technical material. At the same time, the extracts showed significant inhibitory effect on walking ability and grasping ability of worker ants and stronger than the effect of 98.6% rotenone technical material. Under field conditions, the 0.01% rotenone-bait, formulated with the acetone extract of D. trifoliata roots and T. vogelii leaves, had the same control effect on RIFA as that of 0.01% fipronil-bait when treated after 30 d. The bait formulated with the extract of D. trifoliata exhibited quicker and higher effect on RIFA than that of rotenone technical material. It was showed that the acetone extracts of D. trifoliata roots and T. vogelii leaves are able to control S. invicta under both laboratory and field conditions and can be used as an effective agent against RIFA