Hybrid Radio-map for Noise Tolerant Wireless Indoor Localization

In wireless networks, radio-map based locating techniques are commonly used to cope the complex fading feature of radio signal, in which a radio-map is built by calibrating received signal strength (RSS) signatures at training locations in the offline phase. However, in severe hostile environments, such as in ship cabins where severe shadowing, blocking and multi-path fading effects are posed by ubiquitous metallic architecture, even radio-map cannot capture the dynamics of RSS. In this paper, we introduced multiple feature radio-map location method for severely noisy environments. We proposed to add low variance signature into radio map. Since the low variance signatures are generally expensive to obtain, we focus on the scenario when the low variance signatures are sparse. We studied efficient construction of multi-feature radio-map in offline phase, and proposed feasible region narrowing down and particle based algorithm for online tracking. Simulation results show the remarkably performance improvement in terms of positioning accuracy and robustness against RSS noises than the traditional radio-map method.Comment: 6 pages, 11th IEEE International Conference on Networking, Sensing and Control, April 7-9, 2014, Miami, FL, US

Effects of two phosphorous sources in the diet on the growth performance, digestibility, and plasma physiological parameters of <em>Pelodiscus sinensis</em> juveniles

Phosphorus is an essential mineral for aquatic animals to maintain the health of the skeletal system and many physiological functions. This study assessed the effects of two inorganic phosphorus sources on growth performance, apparent phosphorus digestibility, whole-body proximate composition, and physiological status in juvenile *Pelodiscus sinensis*. Two experimental diets were supplemented with 4% calcium phosphate monobasic (MCP) and 5.47% calcium phosphate dibasic (DCP), respectively, to obtain equal total dietary phosphorus (2.20%). 96 turtles (initial body weight: 5.40±0.03g) were randomly distributed into 12 tanks and fed the corresponding diets for 60d. Results showed that phosphorus sources have not significantly influenced the growth parameters, including the specific growth rate, feeding rate, and feed conversion ratio (*P*\>0.05). No significant differences were observed in the hepatosomatic index and whole-body proximate compositions between MCP and DCP groups (*P*\>0.05). The apparent digestibility coefficients of dry matter and phosphorus in MCP group (53.22%) are slightly higher than that in DCP group (48.98%) but did not reach the statistically significant level (*P* \> 0.05). Turtles in MCP and DCP groups are the same in plasma physiological parameters and have equal alkaline phosphatase activities in plasma and liver (*P*\>0.05). In conclusion, calcium phosphate monobasic and calcium phosphate dibasic had the same biological phosphorus availability in diet for juvenile *Pelodiscus sinensis*

In vivo anti-inflammatory activity of Liquidambar formosana Hance infructescence extract

Purpose: To evaluate the anti-inflammatory activity of Liquidambar formosana  Hance infructescence (Liquidambaris fructus, ELF) in vivo, and clarify its underlying mechanisms. Methods: The in vivo anti-inflammatory activity of ELF was examined by xylene-induced ear swelling test in mice as well as carrageenan-induced paw edema method in rats. The levels of inflammatory cytokines (TNF-α, IL-1β, IL-6 and IL-10) in serum were measured by enzyme-linked immunosorbent assay (ELISA), while the expressions of COX-2, iNOS and NF-κB p65 in paw tissue of rats were evaluated by western blot.Results: After ELF treatment, the levels of TNF-α (p &lt; 0.001), IL-1β (p &lt; 0.001) and IL-6 (p &lt; 0.001) in serum decreased and the levels of anti-inflammatory cytokine IL-10 increased (p &lt; 0.01). In addition, ELF treatment resulted in decrease of COX-2 (p &lt; 0.01), iNOS (p &lt; 0.01) and NF-κB p65 (p &lt; 0.01) expressions in Wistar rats.Conclusion: The results reveal that ELF possesses significant anti-inflammatory effect in vivo. The anti-inflammatory activity is associated with the levels of TNF-α, IL-1β, IL-6 and IL-10 in serum. Furthermore, the suppression of NF-κB p65, iNOS and COX-2 is linked to its anti-inflammatory effect. These results provide a rationale for the use of Liquidambaris fructus in inflammatory disease in traditional medicine.Keywords: Anti-inflammatory activity, Liquidambaris fructus, Cytokines, Ear swelling test, Paw edem

GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Parts

For years, researchers have been devoted to generalizable object perception and manipulation, where cross-category generalizability is highly desired yet underexplored. In this work, we propose to learn such cross-category skills via Generalizable and Actionable Parts (GAParts). By identifying and defining 9 GAPart classes (lids, handles, etc.) in 27 object categories, we construct a large-scale part-centric interactive dataset, GAPartNet, where we provide rich, part-level annotations (semantics, poses) for 8,489 part instances on 1,166 objects. Based on GAPartNet, we investigate three cross-category tasks: part segmentation, part pose estimation, and part-based object manipulation. Given the significant domain gaps between seen and unseen object categories, we propose a robust 3D segmentation method from the perspective of domain generalization by integrating adversarial learning techniques. Our method outperforms all existing methods by a large margin, no matter on seen or unseen categories. Furthermore, with part segmentation and pose estimation results, we leverage the GAPart pose definition to design part-based manipulation heuristics that can generalize well to unseen object categories in both the simulator and the real world. Our dataset, code, and demos are available on our project page.Comment: To appear in CVPR 2023 (Highlight

UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy

In this work, we tackle the problem of learning universal robotic dexterous grasping from a point cloud observation under a table-top setting. The goal is to grasp and lift up objects in high-quality and diverse ways and generalize across hundreds of categories and even the unseen. Inspired by successful pipelines used in parallel gripper grasping, we split the task into two stages: 1) grasp proposal (pose) generation and 2) goal-conditioned grasp execution. For the first stage, we propose a novel probabilistic model of grasp pose conditioned on the point cloud observation that factorizes rotation from translation and articulation. Trained on our synthesized large-scale dexterous grasp dataset, this model enables us to sample diverse and high-quality dexterous grasp poses for the object point cloud.For the second stage, we propose to replace the motion planning used in parallel gripper grasping with a goal-conditioned grasp policy, due to the complexity involved in dexterous grasping execution. Note that it is very challenging to learn this highly generalizable grasp policy that only takes realistic inputs without oracle states. We thus propose several important innovations, including state canonicalization, object curriculum, and teacher-student distillation. Integrating the two stages, our final pipeline becomes the first to achieve universal generalization for dexterous grasping, demonstrating an average success rate of more than 60\% on thousands of object instances, which significantly outperforms all baselines, meanwhile showing only a minimal generalization gap.Comment: Accepted to CVPR 202

Amorphous photonic topological insulator

Photonic topological insulators (PTIs) exhibit robust photonic edge states protected by band topology, similar to electronic edge states in topological band insulators. Standard band theory does not apply to amorphous phases of matter, which are formed by non-crystalline lattices with no long-range positional order but only short-range order. Among other interesting properties, amorphous media exhibit transitions between glassy and liquid phases, accompanied by dramatic changes in short-range order. Here, we experimentally investigate amorphous variants of a Chern-number-based PTI. By tuning the disorder strength in the lattice, we demonstrate that photonic topological edge states can persist into the amorphous regime, prior to the glass-to-liquid transition. After the transition to a liquid-like lattice configuration, the signatures of topological edge states disappear. This interplay between topology and short-range order in amorphous lattices paves the way for new classes of non-crystalline topological photonic materials.Comment: 13 pages, 4 figure

Observation of photonic antichiral edge states

Chiral edge states are a hallmark feature of two-dimensional topological materials. Such states must propagate along the edges of the bulk either clockwise or counterclockwise, and thus produce oppositely propagating edge states along the two parallel edges of a strip sample. However, recent theories have predicted a counterintuitive picture, where the two edge states at the two parallel strip edges can propagate in the same direction; these anomalous topological edge states are named as antichiral edge states. Here we report the experimental observation of antichiral edge states in a gyromagnetic photonic crystal. The crystal consists of gyromagnetic cylinders in a honeycomb lattice, with the two triangular sublattices magnetically biased in opposite directions. With microwave measurement, unique properties of antichiral edge states have been observed directly, which include the titled dispersion, the chiral-like robust propagation in samples with certain shapes, and the scattering into backward bulk states at certain terminations. These results extend and supplement the current understanding of chiral edge states

ARNOLD: A Benchmark for Language-Grounded Task Learning With Continuous States in Realistic 3D Scenes

Understanding the continuous states of objects is essential for task learning and planning in the real world. However, most existing task learning benchmarks assume discrete(e.g., binary) object goal states, which poses challenges for the learning of complex tasks and transferring learned policy from simulated environments to the real world. Furthermore, state discretization limits a robot's ability to follow human instructions based on the grounding of actions and states. To tackle these challenges, we present ARNOLD, a benchmark that evaluates language-grounded task learning with continuous states in realistic 3D scenes. ARNOLD is comprised of 8 language-conditioned tasks that involve understanding object states and learning policies for continuous goals. To promote language-instructed learning, we provide expert demonstrations with template-generated language descriptions. We assess task performance by utilizing the latest language-conditioned policy learning models. Our results indicate that current models for language-conditioned manipulations continue to experience significant challenges in novel goal-state generalizations, scene generalizations, and object generalizations. These findings highlight the need to develop new algorithms that address this gap and underscore the potential for further research in this area. See our project page at: https://arnold-benchmark.github.ioComment: The first two authors contributed equally; 20 pages; 17 figures; project availalbe: https://arnold-benchmark.github.io