TBGC: Task-level Backbone-Oriented Gradient Clip for Multi-Task Foundation Model Learning

The AllInOne training paradigm squeezes a wide range of tasks into a unified model in a multi-task learning manner. However, optimization in multi-task learning is more challenge than single-task learning, as the gradient norm from different tasks may vary greatly, making the backbone overly biased towards one specific task. To address this issue, we propose the task-level backbone-oriented gradient clip paradigm, compared with the vanilla gradient clip method, it has two points of emphasis:1) gradient clip is performed independently for each task. 2) backbone gradients generated from each task are rescaled to the same norm scale. Based on the experimental results, we argue that the task-level backbone-oriented gradient clip paradigm can relieve the gradient bias problem to some extent. We also propose a novel multi-branch data augmentation strategy where conflict augmentations are placed in different branches. Our approach has been shown to be effective and finally achieve 1st place in the Leaderboard A and 2nd place in the Leaderboard B of the CVPR2023 Foundation Model Challenge. It's worth noting that instead of evaluating all three tasks(detection, segmentation and fine-grained classification) in Leaderboard A, the segmentation task is not evaluated in Leaderboard B, in which our team has a huge advantage.Comment: Foundation Model Challenge@CVPR2023, Accepted by CVPR2023 Worksho

User Mobility Detection using Foot Force Sensors and Mobile Phone GPS.

PhDA user (or human) mobility context is defined as a type of user context that describes a type of whole body posture (e.g., standing versus sitting) and/or a type of travel or transportation mode (e.g., walking, cycling, travel by bus, etc). Such a context can be derived from low-level sensor data and spatial contexts, including location coordinates, 3D-orientation, direction (with respect to magnetic north), velocity and acceleration. Different value-added services can be adapted to users’ mobility contexts such as assessing how eco-friendly our travel is, and adapting travel information services such as maps to different transportation modes. Current sensor-based methods for user mobility detection have several key limitations: narrow range of recognition, coarse user mobility recognition capability, and low recognition accuracy. In this thesis, a new Foot-Force and GPS (FF+GPS) sensor method is proposed to overcome these challenges that leverages a set of wearable FF sensors in combination with mobile phone GPS. The novelty of this approach is that it provides a more comprehensive recognition capability in terms of reliably recognising various fine-grained human postures and transportation modes. In addition, by comparing the new FF+GPS method with both an accelerometer (ACC) method (62% accuracy) and an ACC+GPS based method (70% accuracy) as baseline methods, it obtains a higher accuracy (90%) with less computational complexity, when tested on a dataset obtained from ten individuals. In addition, the new FF+GPS method has been further extended and evaluated. More specifically, the trade-off between the computation and resources needed to support lower versus higher number of features and sensors has been investigated. The improved FF+GPS method reduced the number of classification features from 31 to 12, reduced the number of FF sensors from 8 to 4, and reduced the use of GPS in mobility activity recognition

Plasma surface functionalization of carbon nanofibres with silver, palladium and platinum nanoparticles for cost-effective and high-performance supercapacitors

Due to their relatively low cost, large surface area and good chemical and physical properties, carbon nanofibers (CNFs) are attractive for the fabrication of electrodes for supercapacitors (SCs). However, their relatively low electrical conductivity has impeded their practical application. To this end, a novel active-screen plasma activation and deposition technology has been developed to deposit silver, platinum and palladium nanoparticles on activated CNFs surfaces to increase their specific surface area and electrical conductivity, thus improving the specific capacitance. The functionalised CNFs were fully characterised using scanning electron microscope (SEM), energy dispersive X-ray analysis (EDX) and X-ray diffraction (XRD) and their electrochemical properties were evaluated using cyclic voltammetry and electrochemical impedance spectroscopy. The results showed a significant improvement in specific capacitance, as well as electrochemical impedance over the untreated CNFs. The functionalisation of CNFs via environmental-friendly active-screen plasma technology provides a promising future for cost-effective supercapacitors with high power and energy density

Investigation of Electrode Electrochemical Reactions in CH3 NH3 PbBr3 Perovskite Single-Crystal Field-Effect Transistors.

Optoelectronic devices based on metal halide perovskites, including solar cells and light-emitting diodes, have attracted tremendous research attention globally in the last decade. Due to their potential to achieve high carrier mobilities, organic-inorganic hybrid perovskite materials can enable high-performance, solution-processed field-effect transistors (FETs) for next-generation, low-cost, flexible electronic circuits and displays. However, the performance of perovskite FETs is hampered predominantly by device instabilities, whose origin remains poorly understood. Here, perovskite single-crystal FETs based on methylammonium lead bromide are studied and device instabilities due to electrochemical reactions at the interface between the perovskite and gold source-drain top contacts are investigated. Despite forming the contacts by a gentle, soft lamination method, evidence is found that even at such "ideal" interfaces, a defective, intermixed layer is formed at the interface upon biasing of the device. Using a bottom-contact, bottom-gate architecture, it is shown that it is possible to minimize such a reaction through a chemical modification of the electrodes, and this enables fabrication of perovskite single-crystal FETs with high mobility of up to ≈15 cm2 V-1 s-1 at 80 K. This work addresses one of the key challenges toward the realization of high-performance solution-processed perovskite FETs

Design and Test of a Hybrid Foot Force Sensing and GPS System for Richer User Mobility Activity Recognition

Wearable and accompanied sensors and devices are increasingly being used for user activity recognition. However, typical GPS-based and accelerometer-based (ACC) methods face three main challenges: a low recognition accuracy; a coarse recognition capability, i.e., they cannot recognise both human posture (during travelling) and transportation mode simultaneously, and a relatively high computational complexity. Here, a new GPS and Foot-Force (GPS + FF) sensor method is proposed to overcome these challenges that leverages a set of wearable FF sensors in combination with GPS, e.g., in a mobile phone. User mobility activities that can be recognised include both daily user postures and common transportation modes: sitting, standing, walking, cycling, bus passenger, car passenger (including private cars and taxis) and car driver. The novelty of this work is that our approach provides a more comprehensive recognition capability in terms of reliably recognising both human posture and transportation mode simultaneously during travel. In addition, by comparing the new GPS + FF method with both an ACC method (62% accuracy) and a GPS + ACC based method (70% accuracy) as baseline methods, it obtains a higher accuracy (95%) with less computational complexity, when tested on a dataset obtained from ten individuals

Design and Test of a Hybrid Foot Force Sensing and GPS System for Richer User Mobility Activity Recognition

sensor

Comparison of Different Isolation Methods for Plasma-Derived Extracellular Vesicles in Patients with Hyperlipidemia

Extracellular vesicles are commonly found in human body fluids and can reflect current physiological conditions of human body and act as biomarkers of disease. The quality of isolated extracellular vesicles facilitates the early diagnosis of various diseases accompanied by hyperlipidemia. Nonetheless, there are no reports on which special methods are suitable for isolating extracellular vesicles from the plasma of patients with hyperlipidemia. Thus, this study compared three different research-based extracellular vesicle isolation approaches, namely ultracentrifugation (UC), polyethylene glycol (PEG) precipitation, and size exclusion chromatography (SEC), and determined which of them was the most effective method. We selected blood samples from 12 patients with clinically diagnosed hyperlipidemia and isolated plasma-derived extracellular vesicles using three methods. The morphology of the isolated extracellular vesicles was observed using transmission electron microscopy, while the concentration was detected by asymmetric flow field-flow fractionation and multi-angle light scattering. Marker proteins were identified by Western blotting, and protein composition was evaluated by silver staining. Both determined the contaminations in the extracellular vesicle samples. The results showed that the three methods can be successfully used for the isolation of extracellular vesicles. The extracellular vesicles isolated by UC were larger in size, and the yield was much lower. Although the yield of extracellular vesicles isolated by PEG precipitation was greatly improved, the contamination was increased. Of the three methods, only the SEC-isolated extracellular vesicles were characterized by high yield and low contamination. Therefore, our data suggested that the SEC was a more ideal method for isolating extracellular vesicles from the plasma of patients with hyperlipidemia

Pulsar identification based on generative adversarial network and residual network

The search for pulsars is an important area of study in modern astronomy. The amount of collected pulsar data is increasing exponentially as the performance of modern radio telescopes improves, necessitating the improvement of the original pulsar search methods. Artificial intelligence techniques are currently being used in pulsar candidate identification tasks. However, improving the accuracy of pulsar candidate identification using artificial intelligence techniques remains a challenge. Because the amount of collected data is so large, the number of real pulsar samples is very limited, which leads to a serious sample imbalance problem. Many existing methods ignore this issue, making it difficult for the model to reach the optimal solution. A framework combining generative adversarial networks and residual networks is proposed to greatly alleviate the problem of sample inequality. The framework first generates stable pulsar images using generative adversarial networks and then designs a deep neural network model based on residual networks to identify pulsar candidates using intra-block and inter-block residual connectivity. The ResNet approach has a better ability to fit the data than the CNN approach and can achieve the extraction of features with more classification ability with a smaller dataset. Meanwhile, the data expanded by the high-quality simulated samples generated by the generative adversarial network can provide richer identification features and improve the identification accuracy for pulsar candidates

Green manure increases peanut production by shaping the rhizosphere bacterial community and regulating soil metabolites under continuous peanut production systems

Abstract Background Green manure (GM) is a crop commonly grown during fallow periods, which has been applied in agriculture as a strategy to regulate nutrient cycling, improve organic matter, and enhance soil microbial biodiversity, but to date, few studies have examined the effects of GM treatments on rhizosphere soil bacterial community and soil metabolites from continuous cropping peanut field. Results: In this study, we found that the abundances of several functionally significant bacterial groups containing Actinobacteria, Acidobacteria, and genus Sphingomonas, which are associated with nitrogen cycling, were dramatically increased in GM-applied soils. Consistent with the bacterial community results, metabolomics analysis revealed a strong perturbation of nitrogen- or carbon-related metabolisms in GM-applied soils. The substantially up-regulated beneficial metabolites including sucrose, adenine, lysophosphatidylcholine (LPC), malic acid, and betaines in GM-applied soils may contribute to overcome continuous cropping obstacle. In contrast to peanut continuous cropping, planting winter wheat and oilseed rape in winter fallow period under continuous spring peanut production systems evidently improved the soil quality, concomitantly with raised peanut pod yield by 32.93% and 25.20%, in the 2020 season, respectively. Conclusions: GMs application is an effective strategy to overcome continuous cropping obstacle under continuous peanut production systems by improving nutrient cycling, soil metabolites, and rhizobacterial properties