A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-Resolutions

High-resolution representations are important for vision-based robotic grasping problems. Existing works generally encode the input images into low-resolution representations via sub-networks and then recover high-resolution representations. This will lose spatial information, and errors introduced by the decoder will be more serious when multiple types of objects are considered or objects are far away from the camera. To address these issues, we revisit the design paradigm of CNN for robotic perception tasks. We demonstrate that using parallel branches as opposed to serial stacked convolutional layers will be a more powerful design for robotic visual grasping tasks. In particular, guidelines of neural network design are provided for robotic perception tasks, e.g., high-resolution representation and lightweight design, which respond to the challenges in different manipulation scenarios. We then develop a novel grasping visual architecture referred to as HRG-Net, a parallel-branch structure that always maintains a high-resolution representation and repeatedly exchanges information across resolutions. Extensive experiments validate that these two designs can effectively enhance the accuracy of visual-based grasping and accelerate network training. We show a series of comparative experiments in real physical environments at Youtube: https://youtu.be/Jhlsp-xzHFY

Vision-Based Reactive Planning and Control of Quadruped Robots in Unstructured Dynamic Environments

Quadruped robots have received increasing attention for the past few years. However, existing works primarily focus on static environments or assume the robot has full observations of the environment. This limits their practical applications since real-world environments are often dynamic and partially observable. To tackle these issues, vision-based reactive planning and control (V-RPC) is developed in this work. The V-RPC comprises two modules: offline pre-planning and online reactive planning. The pre-planning phase generates a reference trajectory over continuous workspace via sampling-based methods using prior environmental knowledge, given an LTL specification. The online reactive module dynamically adjusts the reference trajectory and control based on the robot's real-time visual perception to adapt to environmental changes

Region Proposal Rectification Towards Robust Instance Segmentation of Biological Images

Top-down instance segmentation framework has shown its superiority in object detection compared to the bottom-up framework. While it is efficient in addressing over-segmentation, top-down instance segmentation suffers from over-crop problem. However, a complete segmentation mask is crucial for biological image analysis as it delivers important morphological properties such as shapes and volumes. In this paper, we propose a region proposal rectification (RPR) module to address this challenging incomplete segmentation problem. In particular, we offer a progressive ROIAlign module to introduce neighbor information into a series of ROIs gradually. The ROI features are fed into an attentive feed-forward network (FFN) for proposal box regression. With additional neighbor information, the proposed RPR module shows significant improvement in correction of region proposal locations and thereby exhibits favorable instance segmentation performances on three biological image datasets compared to state-of-the-art baseline methods. Experimental results demonstrate that the proposed RPR module is effective in both anchor-based and anchor-free top-down instance segmentation approaches, suggesting the proposed method can be applied to general top-down instance segmentation of biological images. Code is available

Acoustic separation of circulating tumor cells

Circulating tumor cells (CTCs) are important targets for cancer biology studies. To further elucidate the role of CTCs in cancer metastasis and prognosis, effective methods for isolating extremely rare tumor cells from peripheral blood must be developed. Acoustic-based methods, which are known to preserve the integrity, functionality, and viability of biological cells using label-free and contact-free sorting, have thus far not been successfully developed to isolate rare CTCs using clinical samples from cancer patients owing to technical constraints, insufficient throughput, and lack of long-term device stability. In this work, we demonstrate the development of an acoustic-based microfluidic device that is capable of high-throughput separation of CTCs from peripheral blood samples obtained from cancer patients. Our method uses tilted-angle standing surface acoustic waves. Parametric numerical simulations were performed to design optimum device geometry, tilt angle, and cell throughput that is more than 20 times higher than previously possible for such devices. We first validated the capability of this device by successfully separating low concentrations (~100 cells/mL) of a variety of cancer cells from cell culture lines from WBCs with a recovery rate better than 83%. We then demonstrated the isolation of CTCs in blood samples obtained from patients with breast cancer. Our acoustic-based separation method thus offers the potential to serve as an invaluable supplemental tool in cancer research, diagnostics, drug efficacy assessment, and therapeutics owing to its excellent biocompatibility, simple design, and label-free automated operation while offering the capability to isolate rare CTCs in a viable state.National Institutes of Health (U.S.) (Grant 1 R01 GM112048-01A1)National Institutes of Health (U.S.) (Grant 1R33EB019785-01)National Science Foundation (U.S.)Penn State Center for Nanoscale Science (Materials Research Science and Engineering Center Grant DMR-0820404)National Institutes of Health (U.S.) (Grant U01HL114476

Epigenetic control of embryo–uterine crosstalk at peri-implantation

Abstract(#br)Embryo implantation is one of the pivotal steps during mammalian pregnancy, since the quality of embryo implantation determines the outcome of ongoing pregnancy and fetal development. A large number of factors, including transcription factors, signalling transduction components, and lipids, have been shown to be indispensable for embryo implantation. Increasing evidence also suggests the important roles of epigenetic factors in this critical event. This review focuses on recent findings about the involvement of epigenetic regulators during embryo implantation

DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

The rapid development of open-source large language models (LLMs) has been truly remarkable. However, the scaling law described in previous literature presents varying conclusions, which casts a dark cloud over scaling LLMs. We delve into the study of scaling laws and present our distinctive findings that facilitate scaling of large scale models in two commonly used open-source configurations, 7B and 67B. Guided by the scaling laws, we introduce DeepSeek LLM, a project dedicated to advancing open-source language models with a long-term perspective. To support the pre-training phase, we have developed a dataset that currently consists of 2 trillion tokens and is continuously expanding. We further conduct supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) on DeepSeek LLM Base models, resulting in the creation of DeepSeek Chat models. Our evaluation results demonstrate that DeepSeek LLM 67B surpasses LLaMA-2 70B on various benchmarks, particularly in the domains of code, mathematics, and reasoning. Furthermore, open-ended evaluations reveal that DeepSeek LLM 67B Chat exhibits superior performance compared to GPT-3.5

Rapid Glacier Shrinkage in the Gongga Mountains in the Last 27 Years

Glaciers are an important part of the cryosphere and important reservoirs of fresh water on Earth. Glaciers in the Gongga Mountains, located in the southeastern Tibetan Plateau, have been experiencing dramatic changes and substantially shrinking over the past two decades. We analyzed the glacier change over the Gongga Mountains using the Landsat data from 1994 to 2021 (interval of 4 or 5 years), with Gaofen-1 (GF-1) data to evaluate the uncertainty. The glacier shrinkage under different terrain conditions, including altitudes, slope, and slope direction, was further explored. Finally, we evaluated the response of glacier shrinkage to climate change using precipitation and temperature data for nearly 30 years. Results show that the glaciers in the Gongga Mountains are experiencing an accelerating ablation, with a glacier area of ~240 km2 in 1994 and ~212 km2 in 2021 (an average annual shrinkage rate of 1.04 km2/a). The shrinkage mainly occurs in areas with altitudes of 5000–5300 m and a slope of 30–40°. Moreover, the shrinkage is strongly related to the recent warming of the climate, with the warming rate being 0.19 °C/10a, while precipitation remains almost constant during 1978–2019. The results provide a scientific basis for water resources management, ecological environmental protection, and natural disaster protection in southeast Tibet for decision making

When Transformer Meets Robotic Grasping: Exploits Context for Efficient Grasp Detection

In this paper, we present a transformer-based architecture, namely TF-Grasp, for robotic grasp detection. The developed TF-Grasp framework has two elaborate designs making it well suitable for visual grasping tasks. The first key design is that we adopt the local window attention to capture local contextual information and detailed features of graspable objects. Then, we apply the cross window attention to model the long-term dependencies between distant pixels. Object knowledge, environmental configuration, and relationships between different visual entities are aggregated for subsequent grasp detection. The second key design is that we build a hierarchical encoder-decoder architecture with skip-connections, delivering shallow features from encoder to decoder to enable a multi-scale feature fusion. Due to the powerful attention mechanism, the TF-Grasp can simultaneously obtain the local information (i.e., the contours of objects), and model long-term connections such as the relationships between distinct visual concepts in clutter. Extensive computational experiments demonstrate that the TF-Grasp achieves superior results versus state-of-art grasping convolutional models and attain a higher accuracy of 97.99% and 94.6% on Cornell and Jacquard grasping datasets, respectively. Real-world experiments using a 7DoF Franka Emika Panda robot also demonstrate its capability of grasping unseen objects in a variety of scenarios. The code and pre-trained models will be available at https://github.com/WangShaoSUN/grasp-transforme