ShapeShift: Superquadric-based Object Pose Estimation for Robotic Grasping

Object pose estimation is a critical task in robotics for precise object manipulation. However, current techniques heavily rely on a reference 3D object, limiting their generalizability and making it expensive to expand to new object categories. Direct pose predictions also provide limited information for robotic grasping without referencing the 3D model. Keypoint-based methods offer intrinsic descriptiveness without relying on an exact 3D model, but they may lack consistency and accuracy. To address these challenges, this paper proposes ShapeShift, a superquadric-based framework for object pose estimation that predicts the object's pose relative to a primitive shape which is fitted to the object. The proposed framework offers intrinsic descriptiveness and the ability to generalize to arbitrary geometric shapes beyond the training set

Modification of Transition-Metal Redox by Interstitial Water in Hexacyanometalate Electrodes for Sodium-Ion Batteries.

A sodium-ion battery (SIB) solution is attractive for grid-scale electrical energy storage. Low-cost hexacyanometalate is a promising electrode material for SIBs because of its easy synthesis and open framework. Most hexacyanometalate-based SIBs work with aqueous electrolyte, and interstitial water in the material has been found to strongly affect the electrochemical profile, but the mechanism remains elusive. Here we provide a comparative study of the transition-metal redox in hexacyanometalate electrodes with and without interstitial water based on soft X-ray absorption spectroscopy and theoretical calculations. We found distinct transition-metal redox sequences in hydrated and anhydrated NaxMnFe(CN)6·zH2O. The Fe and Mn redox in hydrated electrodes are separated and are at different potentials, leading to two voltage plateaus. On the contrary, mixed Fe and Mn redox in the same potential range is found in the anhydrated system. This work reveals for the first time how transition-metal redox in batteries is strongly affected by interstitial molecules that are seemingly spectators. The results suggest a fundamental mechanism based on three competing factors that determine the transition-metal redox potentials. Because most hexacyanometalate electrodes contain water, this work directly reveals the mechanism of how interstitial molecules could define the electrochemical profile, especially for electrodes based on transition-metal redox with well-defined spin states

MICK: A Meta-Learning Framework for Few-shot Relation Classification with Small Training Data

Few-shot relation classification seeks to classify incoming query instances after meeting only few support instances. This ability is gained by training with large amount of in-domain annotated data. In this paper, we tackle an even harder problem by further limiting the amount of data available at training time. We propose a few-shot learning framework for relation classification, which is particularly powerful when the training data is very small. In this framework, models not only strive to classify query instances, but also seek underlying knowledge about the support instances to obtain better instance representations. The framework also includes a method for aggregating cross-domain knowledge into models by open-source task enrichment. Additionally, we construct a brand new dataset: the TinyRel-CM dataset, a few-shot relation classification dataset in health domain with purposely small training data and challenging relation classes. Experimental results demonstrate that our framework brings performance gains for most underlying classification models, outperforms the state-of-the-art results given small training data, and achieves competitive results with sufficiently large training data

Experimental Study on Non-Darcian Flow in Phyllite Bimrocks With the Orientation of Blocks

Phyllite bimrocks are widely distributed in the eastern margin of the Tibetan Plateau, and it is the main geomaterial for landslides, slopes, dam basement and subgrades in this area. However, the flow behavior of phyllite bimrocks is unknown, especially the flow behavior of phyllite bimrocks with the orientation of blocks. This paper reports the coupling characteristics of flow and orientation of blocks in phyllite bimrocks. The flow behavior of phyllite bimrocks with different block percentages and block sizes was studied by a series of permeability experiments. A large-scale permeability apparatus was designed, and specimens with varying percentages of block and block sizes were produced by the same dip angle of blocks and compaction degree. Based on the Reynolds number analysis, it was found that the flow in phyllite bimrocks becomes laminar to turbulent under lower hydraulic gradient, and the flow behavior of phyllite bimrocks does not obey Darcy’s law. Furthermore, the Forchheimer equation is better at analyzing the flow behavior of phyllite bimrocks compared with Izbash equation. In addition, based on the coefficients a in the Forchheimer equation, the hydraulic conductivity of phyllite bimrocks can be calculated. The calculation result shows that when the percentage of blocks is 25%, the hydraulic conductivity reaches the minimum. Besides, the hydraulic conductivity increases approximately linear with the block size increase. On the basis of previous studies, coefficients A and B of the Forchheimer equation are detected by the normalized objective function analysis. The results would provide a valuable reference for risk assessment and prevention of phyllite bimrock slope

MMRNet: Improving Reliability for Multimodal Object Detection and Segmentation for Bin Picking via Multimodal Redundancy

Recently, there has been tremendous interest in industry 4.0 infrastructure to address labor shortages in global supply chains. Deploying artificial intelligence-enabled robotic bin picking systems in real world has become particularly important for reducing stress and physical demands of workers while increasing speed and efficiency of warehouses. To this end, artificial intelligence-enabled robotic bin picking systems may be used to automate order picking, but with the risk of causing expensive damage during an abnormal event such as sensor failure. As such, reliability becomes a critical factor for translating artificial intelligence research to real world applications and products. In this paper, we propose a reliable object detection and segmentation system with MultiModal Redundancy (MMRNet) for tackling object detection and segmentation for robotic bin picking using data from different modalities. This is the first system that introduces the concept of multimodal redundancy to address sensor failure issues during deployment. In particular, we realize the multimodal redundancy framework with a gate fusion module and dynamic ensemble learning. Finally, we present a new label-free multi-modal consistency (MC) score that utilizes the output from all modalities to measure the overall system output reliability and uncertainty. Through experiments, we demonstrate that in an event of missing modality, our system provides a much more reliable performance compared to baseline models. We also demonstrate that our MC score is a more reliability indicator for outputs during inference time compared to the model generated confidence scores that are often over-confident

Bridging the Granularity Gap for Acoustic Modeling

While Transformer has become the de-facto standard for speech, modeling upon the fine-grained frame-level features remains an open challenge of capturing long-distance dependencies and distributing the attention weights. We propose \textit{Progressive Down-Sampling} (PDS) which gradually compresses the acoustic features into coarser-grained units containing more complete semantic information, like text-level representation. In addition, we develop a representation fusion method to alleviate information loss that occurs inevitably during high compression. In this way, we compress the acoustic features into 1/32 of the initial length while achieving better or comparable performances on the speech recognition task. And as a bonus, it yields inference speedups ranging from 1.20$\times$ to 1.47$\times$. By reducing the modeling burden, we also achieve competitive results when training on the more challenging speech translation task.Comment: ACL 2023 Finding

Plant-frugivore network simplification under habitat fragmentation leaves a small core of interacting generalists

Habitat fragmentation impacts seed dispersal processes that are important in maintaining biodiversity and ecosystem functioning. However, it is still unclear how habitat fragmentation affects frugivorous interactions due to the lack of high-quality data on plant-frugivore networks. Here we recorded 10,117 plant-frugivore interactions from 22 reservoir islands and six nearby mainland sites using the technology of arboreal camera trapping to assess the effects of island area and isolation on the diversity, structure, and stability of plant-frugivore networks. We found that network simplification under habitat fragmentation reduces the number of interactions involving specialized species and large-bodied frugivores. Small islands had more connected, less modular, and more nested networks that consisted mainly of small-bodied birds and abundant plants, as well as showed evidence of interaction release (i.e., dietary expansion of frugivores). Our results reveal the importance of preserving large forest remnants to support plant-frugivore interaction diversity and forest functionality.Fil: Li, Wande. East China Normal University; ChinaFil: Zhu, Chen. College Of Life Sciences, Zhejiang University; ChinaFil: Grass, Ingo. Universidad de Hohenheim; AlemaniaFil: Vazquez, Diego P.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Provincia de Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Universidad Nacional de Cuyo. Instituto Argentino de Investigaciones de las Zonas Áridas; ArgentinaFil: Wang, Duorun. East China Normal University; ChinaFil: Zhao, Yuhao. East China Normal University; ChinaFil: Zeng, Di. East China Normal University; ChinaFil: Kang, Yi. East China Normal University; ChinaFil: Ding, Ping. No especifíca;Fil: Si, Xingfeng. East China Normal University; Chin

Endoplasmic Reticulum Stress-Related Signature Predicts Prognosis and Drug Response in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney cancer. The maximum number of deaths associated with kidney cancer can be attributed to ccRCC. Disruption of cellular proteostasis results in endoplasmic reticulum (ER) stress, which is associated with various aspects of cancer. It is noteworthy that the role of ER stress in the progression of ccRCC remains unclear. We classified 526 ccRCC samples identified from the TCGA database into the C1 and C2 subtypes by consensus clustering of the 295 ER stress-related genes. The ccRCC samples belonging to subtype C2 were in their advanced tumor stage and grade. These samples were characterized by poor prognosis and malignancy immune microenvironment. The upregulation of the inhibitory immune checkpoint gene expression and unique drug sensitivity were also observed. The differentially expressed genes between the two clusters were explored. An 11-gene ER stress-related prognostic risk model was constructed following the LASSO regression and Cox regression analyses. In addition, a nomogram was constructed by integrating the clinical parameters and risk scores. The calibration curves, ROC curves, and DCA curves helped validate the accuracy of the prediction when both the TCGA dataset and the external E-MTAB-1980 dataset were considered. Moreover, we analyzed the differentially expressed genes common to the E-MTAB-1980 and TCGA datasets to screen out new therapeutic compounds. In summary, our study can potentially help in the comprehensive understanding of ER stress in ccRCC and serve as a reference for future studies on novel prognostic biomarkers and treatments