Characteristics of multiple‐year nitrous oxide emissions from conventional vegetable fields in southeastern China

The annual and interannual characteristics of nitrous oxide (N2O) emissions from conventional vegetable fields are poorly understood. We carried out 4 year measurements of N2O fluxes from a conventional vegetable cultivation area in the Yangtze River delta. Under fertilized conditions subject to farming practices, approximately 86% of the annual total N2O release occurred following fertilization events. The direct emission factors (EFd) of the 12 individual vegetable seasons investigated ranged from 0.06 to 14.20%, with a mean of 3.09% and a coefficient of variation (CV) of 142%. The annual EFd varied from 0.59 to 4.98%, with a mean of 2.88% and an interannual CV of 74%. The mean value is much larger than the latest default value (1.00%) of the Intergovernmental Panel on Climate Change. Occasional application of lagoon‐stored manure slurry coupled with other nitrogen fertilizers, or basal nitrogen addition immediately followed by heavy rainfall, accounted for a substantial portion of the large EFds observed in warm seasons. The large CVs suggest that the emission factors obtained from short‐term observations that poorly represent seasonality and/or interannual variability will inevitably yield large uncertainties in inventory estimation. The results of this study indicate that conventional vegetable fields associated with intensive nitrogen addition, as well as occasional applications of manure slurry, may substantially account for regional N2O emissions. However, this conclusion needs to be further confirmed through studies at multiple field sites. Moreover, further experimental studies are needed to test the mitigation options suggested by this study for N2O emissions from open vegetable fields

An Event-Based Neurobiological Recognition System with Orientation Detector for Objects in Multiple Orientations

A new multiple orientation event-based neurobiological recognition system is proposed by integrating recognition and tracking function in this paper, which is used for asynchronous address-event representation (AER) image sensors. The characteristic of this system has been enriched to recognize the objects in multiple orientations with only training samples moving in a single orientation. The system extracts multi-scale and multi-orientation line features inspired by models of the primate visual cortex. An orientation detector based on modified Gaussian blob tracking algorithm is introduced for object tracking and orientation detection. The orientation detector and feature extraction block work in simultaneous mode, without any increase in categorization time. An addresses lookup table (addresses LUT) is also presented to adjust the feature maps by addresses mapping and reordering, and they are categorized in the trained spiking neural network. This recognition system is evaluated with the MNIST dataset which have played important roles in the development of computer vision, and the accuracy is increase owing to the use of both ON and OFF events. AER data acquired by a DVS are also tested on the system, such as moving digits, pokers, and vehicles. The experimental results show that the proposed system can realize event-based multi-orientation recognition.The work presented in this paper makes a number of contributions to the event-based vision processing system for multi-orientation object recognition. It develops a new tracking-recognition architecture to feedforward categorization system and an address reorder approach to classify multi-orientation objects using event-based data. It provides a new way to recognize multiple orientation objects with only samples in single orientation

Suppressing electron disorder-induced heating of ultracold neutral plasma via optical lattice

Disorder-induced heating (DIH) prevents ultracold neutral plasma into electron strong coupling regime. Here we propose a scheme to suppress electronic DIH via optical lattice. We simulate the evolution dynamics of ultracold neutral plasma constrained by three-dimensional optical lattice using classical molecular dynamics method. The results show that for experimentally achievable condition, electronic DIH is suppressed by a factor of 1.3, and the Coulomb coupling strength can reach to 0.8 which is approaching the strong coupling regime. Suppressing electronic DIH via optical lattice may pave a way for the research of electronic strongly coupled plasma

Conformal Prediction for Deep Classifier via Label Ranking

Conformal prediction is a statistical framework that generates prediction sets containing ground-truth labels with a desired coverage guarantee. The predicted probabilities produced by machine learning models are generally miscalibrated, leading to large prediction sets in conformal prediction. In this paper, we empirically and theoretically show that disregarding the probabilities' value will mitigate the undesirable effect of miscalibrated probability values. Then, we propose a novel algorithm named $\textit{Sorted Adaptive prediction sets}$ (SAPS), which discards all the probability values except for the maximum softmax probability. The key idea behind SAPS is to minimize the dependence of the non-conformity score on the probability values while retaining the uncertainty information. In this manner, SAPS can produce sets of small size and communicate instance-wise uncertainty. Theoretically, we provide a finite-sample coverage guarantee of SAPS and show that the expected value of set size from SAPS is always smaller than APS. Extensive experiments validate that SAPS not only lessens the prediction sets but also broadly enhances the conditional coverage rate and adaptation of prediction sets

Scalable Architecture for CPS: A Case Study of Small Autonomous Helicopter

Building a scalable and highly integrated systems is an important research direction and one of key technologies in Cyber-Physical Systems (CPS). Autonomous helicopter is a typical CPS application and its flight presents challenges in flight control system design with scalability. In this paper, we present the integration architecture of hardware and software for the flight control system based TREX 600 helicopter. In order to enhance scalability, the flight control system uses the PC104 and the ARM which is exerted to process the measurement data, including the position, attitude, height etc. The flight control is developed based multi-loop decoupling PI control which is easy to be implemented. Finally, the flight control system is successfully verified in the actual autonomous flight control experiment

DNAGPT: A Generalized Pre-trained Tool for Versatile DNA Sequence Analysis Tasks

Pre-trained large language models demonstrate potential in extracting information from DNA sequences, yet adapting to a variety of tasks and data modalities remains a challenge. To address this, we propose DNAGPT, a generalized DNA pre-training model trained on over 200 billion base pairs from all mammals. By enhancing the classic GPT model with a binary classification task (DNA sequence order), a numerical regression task (guanine-cytosine content prediction), and a comprehensive token language, DNAGPT can handle versatile DNA analysis tasks while processing both sequence and numerical data. Our evaluation of genomic signal and region recognition, mRNA abundance regression, and artificial genomes generation tasks demonstrates DNAGPT's superior performance compared to existing models designed for specific downstream tasks, benefiting from pre-training using the newly designed model structure