BEVStereo++: Accurate Depth Estimation in Multi-view 3D Object Detection via Dynamic Temporal Stereo

Bounded by the inherent ambiguity of depth perception, contemporary multi-view 3D object detection methods fall into the performance bottleneck. Intuitively, leveraging temporal multi-view stereo (MVS) technology is the natural knowledge for tackling this ambiguity. However, traditional attempts of MVS has two limitations when applying to 3D object detection scenes: 1) The affinity measurement among all views suffers expensive computational cost; 2) It is difficult to deal with outdoor scenarios where objects are often mobile. To this end, we propose BEVStereo++: by introducing a dynamic temporal stereo strategy, BEVStereo++ is able to cut down the harm that is brought by introducing temporal stereo when dealing with those two scenarios. Going one step further, we apply Motion Compensation Module and long sequence Frame Fusion to BEVStereo++, which shows further performance boosting and error reduction. Without bells and whistles, BEVStereo++ achieves state-of-the-art(SOTA) on both Waymo and nuScenes dataset

DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detection

Many point-based 3D detectors adopt point-feature sampling strategies to drop some points for efficient inference. These strategies are typically based on fixed and handcrafted rules, making difficult to handle complicated scenes. Different from them, we propose a Dynamic Ball Query (DBQ) network to adaptively select a subset of input points according to the input features, and assign the feature transform with suitable receptive field for each selected point. It can be embedded into some state-of-the-art 3D detectors and trained in an end-to-end manner, which significantly reduces the computational cost. Extensive experiments demonstrate that our method can reduce latency by 30%-60% on KITTI and Waymo datasets. Specifically, the inference speed of our detector can reach 162 FPS and 30 FPS with negligible performance degradation on KITTI and Waymo datasets, respectively

Depth determination of the Moho interface beneath the Tibetan plateau and other areas of China

We apply the adaptive moving window method of Sun et al. to the most recent catalog data and the data recorded by portable stations to construct the velocity structure of the crust and upper mantle, and to determine the depth of the Moho interface beneath the Tibetan plateau and other areas of China. We first select 2 600 locations in the study region with 1° intervals, then at each location invert for a five-layer 1-D P-wave velocity model from the surface down to the uppermost mantle by performing a Monte Carlo random search. The Moho depth at each location is then determined, and the Moho interface beneath the study region is obtained through proper interpolation with certain smoothing. Compared to depths obtained by previous studies, our results show more accurate Moho depths in the Tibetan plateau, Tianshan region and other areas of the study region.United States. Defense Threat Reduction Agency (Contract DTRA01-00-C-0024)Chinese Academy of Sciences (Fund KJCX2-EW-121

Recognition of nonproline N-terminal residues by the Pro/N-degron pathway

Eukaryotic N-degron pathways are proteolytic systems whose unifying feature is their ability to recognize proteins containing N-terminal (Nt) degradation signals called N-degrons, and to target these proteins for degradation by the 26S proteasome or autophagy. GID4, a subunit of the GID ubiquitin ligase, is the main recognition component of the proline (Pro)/N-degron pathway. GID4 targets proteins through their Nt-Pro residue or a Pro at position 2, in the presence of specific downstream sequence motifs. Here we show that human GID4 can also recognize hydrophobic Nt-residues other than Pro. One example is the sequence Nt-IGLW, bearing Nt-Ile. Nt-IGLW binds to wild-type human GID4 with a K_d of 16 μM, whereas the otherwise identical Nt-Pro–bearing sequence PGLW binds to GID4 more tightly, with a K_d of 1.9 μM. Despite this difference in affinities of GID4 for Nt-IGLW vs. Nt-PGLW, we found that the GID4-mediated Pro/N-degron pathway of the yeast Saccharomyces cerevisiae can target an Nt-IGLW–bearing protein for rapid degradation. We solved crystal structures of human GID4 bound to a peptide bearing Nt-Ile or Nt-Val. We also altered specific residues of human GID4 and measured the affinities of resulting mutant GID4s for Nt-IGLW and Nt-PGLW, thereby determining relative contributions of specific GID4 residues to the GID4-mediated recognition of Nt-Pro vs. Nt-residues other than Pro. These and related results advance the understanding of targeting by the Pro/N-degron pathway and greatly expand the substrate recognition range of the GID ubiquitin ligase in both human and yeast cells

Recognition of nonproline N-terminal residues by the Pro/N-degron pathway

Eukaryotic N-degron pathways are proteolytic systems whose unifying feature is their ability to recognize proteins containing N-terminal (Nt) degradation signals called N-degrons, and to target these proteins for degradation by the 26S proteasome or autophagy. GID4, a subunit of the GID ubiquitin ligase, is the main recognition component of the proline (Pro)/N-degron pathway. GID4 targets proteins through their Nt-Pro residue or a Pro at position 2, in the presence of specific downstream sequence motifs. Here we show that human GID4 can also recognize hydrophobic Nt-residues other than Pro. One example is the sequence Nt-IGLW, bearing Nt-Ile. Nt-IGLW binds to wild-type human GID4 with a K_d of 16 μM, whereas the otherwise identical Nt-Pro–bearing sequence PGLW binds to GID4 more tightly, with a K_d of 1.9 μM. Despite this difference in affinities of GID4 for Nt-IGLW vs. Nt-PGLW, we found that the GID4-mediated Pro/N-degron pathway of the yeast Saccharomyces cerevisiae can target an Nt-IGLW–bearing protein for rapid degradation. We solved crystal structures of human GID4 bound to a peptide bearing Nt-Ile or Nt-Val. We also altered specific residues of human GID4 and measured the affinities of resulting mutant GID4s for Nt-IGLW and Nt-PGLW, thereby determining relative contributions of specific GID4 residues to the GID4-mediated recognition of Nt-Pro vs. Nt-residues other than Pro. These and related results advance the understanding of targeting by the Pro/N-degron pathway and greatly expand the substrate recognition range of the GID ubiquitin ligase in both human and yeast cells

ATP-citrate lyase inhibitor improves ectopic lipid accumulation in the kidney in a db/db mouse model

AimWe evaluated a novel treatment for obesity-related renal, an ATP-citrate lyase (ACL) inhibitor, to attenuate ectopic lipid accumulation (ELA) in the kidney and the ensuing inflammation.Materials and methodsAn ACL inhibitor was administered intragastrically to 12-week-old db/db mice for 30 days. The appearance of ELA was observed by staining kidney sections with Oil Red O, and the differences in tissue lipid metabolites were assessed by mass spectrometry. The anti-obesity and renoprotection effects of ACL inhibitors were observed by histological examination and multiple biochemical assays.ResultsUsing the AutoDock Vina application, we determined that among the four known ACL inhibitors (SB-204990, ETC-1002, NDI-091143, and BMS-303141), BMS-303141 had the highest affinity for ACL and reduced ACL expression in the kidneys of db/db mice. We reported that BMS-303141 administration could decrease the levels of serum lipid and renal lipogenic enzymes acetyl-CoA carboxylase (ACC), fatty acid synthase (FAS), HMG-CoA reductase (HMGCR), and diminish renal ELA in db/db mice. In addition, we found that reducing ELA improved renal injuries, inflammation, and tubulointerstitial fibrosis.ConclusionACL inhibitor BMS-303141 protects against obesity-related renal injuries

DreamLLM: Synergistic Multimodal Comprehension and Creation

This paper presents DreamLLM, a learning framework that first achieves versatile Multimodal Large Language Models (MLLMs) empowered with frequently overlooked synergy between multimodal comprehension and creation. DreamLLM operates on two fundamental principles. The first focuses on the generative modeling of both language and image posteriors by direct sampling in the raw multimodal space. This approach circumvents the limitations and information loss inherent to external feature extractors like CLIP, and a more thorough multimodal understanding is obtained. Second, DreamLLM fosters the generation of raw, interleaved documents, modeling both text and image contents, along with unstructured layouts. This allows DreamLLM to learn all conditional, marginal, and joint multimodal distributions effectively. As a result, DreamLLM is the first MLLM capable of generating free-form interleaved content. Comprehensive experiments highlight DreamLLM's superior performance as a zero-shot multimodal generalist, reaping from the enhanced learning synergy.Comment: see project page at https://dreamllm.github.io

Cold sintered LiMgPO4 based composites for low temperature co‐fired ceramic (LTCC) applications

Cold sintered, Li2MoO4-based ceramics have recently been touted as candidates for electronic packaging and low temperature co-fired ceramic (LTCC) technology but MoO3 is an expensive and endangered raw material, not suited for large scale commercialization. Here, we present cold sintered temperature-stable composites based on LiMgPO4 (LMP) in which the Mo (and Li) concentration has been reduced, thereby significantly decreasing raw material costs. Optimum compositions, 0.5LMP-0.1CaTiO3-0.4K2MoO4 (LMP-CTO-KMO), achieved 97% density at <300°C and 600 MPa for 60 minutes. Raman spectroscopy, X-ray diffraction, scanning electron microscopy, and energy dispersive X-ray mapping confirmed the coexistence of end-members, LMP, CTO, and KMO, with no interdiffusion and parasitic phases. Composites exhibited temperature coefficient of resonant frequency ~ –6 ppm/°C, relative permittivity ~9.1, and Q × f values ~8500 GHz, properties suitable for LTCC technology and competitive with commercial incumbents