Advantages and Disadvantages on Contract Farming in Lao PDR

This research is a destination to know about the advantages and disadvantages of contract in Lao PDR, The destination has been proceeding by the standard approach by interview As well of farmer and company. On this basic of research revelation and probes into the path of contract farming which unified of farmer and company, The contract farming circulation and inheritable system under membership. The results found advantages and disadvantages of contract farming. Advantages on contract farming are farmer have product sales, Receive knowledge, The product meets standards set by the company, In the age of globalization pricing and delivery time clearly, Farmer improves management efficiency, It the efficiency of sharing from the company farm management, Skills was improved. And disadvantages is private sector usually of contract farming, High-risk farmers, Contract farming does not clearly and debts pesticides and insecticides. These are the advantages and disadvantages of contract farming in Lao PDR. Keywords: contract farming, advantages and disadvantages, Lao PDR

Decoupled DETR: Spatially Disentangling Localization and Classification for Improved End-to-End Object Detection

The introduction of DETR represents a new paradigm for object detection. However, its decoder conducts classification and box localization using shared queries and cross-attention layers, leading to suboptimal results. We observe that different regions of interest in the visual feature map are suitable for performing query classification and box localization tasks, even for the same object. Salient regions provide vital information for classification, while the boundaries around them are more favorable for box regression. Unfortunately, such spatial misalignment between these two tasks greatly hinders DETR's training. Therefore, in this work, we focus on decoupling localization and classification tasks in DETR. To achieve this, we introduce a new design scheme called spatially decoupled DETR (SD-DETR), which includes a task-aware query generation module and a disentangled feature learning process. We elaborately design the task-aware query initialization process and divide the cross-attention block in the decoder to allow the task-aware queries to match different visual regions. Meanwhile, we also observe that the prediction misalignment problem for high classification confidence and precise localization exists, so we propose an alignment loss to further guide the spatially decoupled DETR training. Through extensive experiments, we demonstrate that our approach achieves a significant improvement in MSCOCO datasets compared to previous work. For instance, we improve the performance of Conditional DETR by 4.5 AP. By spatially disentangling the two tasks, our method overcomes the misalignment problem and greatly improves the performance of DETR for object detection.Comment: accepted by ICCV202

Role of memantine in adult migraine: a systematic review and network meta-analysis to compare memantine with existing migraine preventive medications

BackgroundWhile memantine has been considered a promising drug for migraine prevention, no conclusive evidence exists comparing its efficacy with other migraine-preventive medications. This network meta-analysis (NMA) aimed to access the effectiveness and acceptability of memantine and other guideline-recommended prophylactic agents for migraine.MethodsWe searched the Cochrane Register of Controlled Trials, Embase, PubMed, and ClinicalTrials databases from their inception to 1 June 2024. Randomized placebo-controlled trials (RCTs) examining the pharmacological prevention of adult migraine patients were included. The primary efficacy outcome was the change in migraine days, and the primary safety outcome was withdrawal due to adverse events. Secondary outcomes included 50% response rates and frequency of any adverse events. The analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.ResultsThirty-eight RCTs, including a total of 13,223 participants, were analyzed. Our analysis showed that memantine demonstrated the second-largest reduction in migraine days [standardized mean difference (SMD): −0.83; 95% confidence interval (CI): −1.26, −0.41 compared with placebo] and the highest 50% response rates [odds ratio (OR): 5.58, 95% CI: 1.31 to 23.69] in all studied interventions. Moreover, among all interventions, memantine appeared to show the lowest dropout rate and moderate frequency of adverse events. However, its confidence intervals contained null values.ConclusionThis study provides prioritisation evidence for memantine in migraine prevention, as memantine can significantly decrease the frequency of migraine attacks, improves response rates, and fair acceptability. These beneficial effects were not inferior to currently recommended pharmacological regimens. However, due to the lack of long-term efficacy and safety data, as well as few direct comparisons with active control agents, the estimates of memantine may be overly optimistic. Clinicians should interpret the findings of current NMA cautiously and apply them in a relatively conservative manner

Parameter effects on the total intensity of H I Ly{\alpha} line for a modelled coronal mass ejection and its driven shock

The combination of the H I Ly{\alpha} (121.6 nm) line formation mechanism with ultraviolet (UV) Ly{\alpha} and white-light (WL) observations provides an effective method for determining the electron temperature of coronal mass ejections (CMEs). A key to ensuring the accuracy of this diagnostic technique is the precise calculation of theoretical Ly{\alpha} intensities. This study performs a modelled CME and its driven shock via the 3D MHD simulation. We generate synthetic UV and WL images of the CME and shock to quantify the impact of different assumptions on theoretical Ly{\alpha} intensities, such as the incident intensity of the Ly{\alpha} line (Idisk), the geometric scattering function (p({\theta})), and the kinetic temperature (Tn) assumed to be equal to the proton (Tp) or electron (Te) temperatures. By comparing differences of the Ly{\alpha} intensities under these assumptions, we find that: (1) Using the uniform or Carrington maps of the disk Ly{\alpha} emission underestimates the corona Ly{\alpha} intensity (< 10%) compared to the synchronic map, except for a slight overestimate (< 4%) in the partial CME core. The Carrington map yields lower uncertainties than the uniform disk. (2) The geometric scattering process has a minor impact on the Ly{\alpha} intensity, with a maximum relative uncertainty of < 5%. The Ly{\alpha} intensity is underestimated for the most part but overestimated in the CME core. (3) Compared to the assumption Tn = Tp, using Tn = Te leads to more complex relative uncertainties in CME Ly{\alpha} intensity. The CME core and void are both overestimated, with the maximum uncertainty in the core exceeding 50% and the void remaining below 35%. In the CME front, both over- and under-estimates exist with relative uncertainties of < 35%. The electron temperature assumption has a smaller impact on the shock, with an underestimated relative uncertainty of less than 20%.Comment: 22 pages, 9 figures, accepted by Solar Physic

Effects of the interaction between shade and drought on physiological characteristics in Calamus viminalis seedlings

Recently, the endangerment of wild rattan population draws attention on the conservation and sustainable utilization of rattan resources. Rattan growing usually faces the light and water stress. Therefore, we aim to explore the combined effects of shade and drought on seedling growth, thus providing a theoretical ground for the conservation and artificial cultivation of the rattan. The combined effects of shade and drought on physiological and biochemical traits were studied in two-years-old Calamus viminalis seedlings. Photosynthetic indices including Pn, Gs, Tr, and Ci and physiological indices including MDA, SOD, POD, CAT, and Pro were measured under four levels of water treatments and four levels of shade. Shade, drought and their interaction have a significant effect on C. viminalis seedlings growth. Generally, moderate shade could alleviate the impact induced by drought. However, mild drought usually enhances the effect caused by shading. The result showed that the shade decreased Pn, Gs, and Tr but increased Ci, MDA content and Pro content. Either with the shading or drought increasing, the activity of SOD, POD, and CAT firstly increase and then declined. Drought reduced Pn, Gs, Tr, and Ci but increased the content of MDA and Pro. Overall, the result suggests that 25-50% shading and 65% RSWC water treatment are most beneficial for the growth of C. viminalis seedlings

MSR-86K: An Evolving, Multilingual Corpus with 86,300 Hours of Transcribed Audio for Speech Recognition Research

Recently, multilingual artificial intelligence assistants, exemplified by ChatGPT, have gained immense popularity. As a crucial gateway to human-computer interaction, multilingual automatic speech recognition (ASR) has also garnered significant attention, as evidenced by systems like Whisper. However, the proprietary nature of the training data has impeded researchers' efforts to study multilingual ASR. This paper introduces MSR-86K, an evolving, large-scale multilingual corpus for speech recognition research. The corpus is derived from publicly accessible videos on YouTube, comprising 15 languages and a total of 86,300 hours of transcribed ASR data. We also introduce how to use the MSR-86K corpus and other open-source corpora to train a robust multilingual ASR model that is competitive with Whisper. MSR-86K will be publicly released on HuggingFace, and we believe that such a large corpus will pave new avenues for research in multilingual ASR.Comment: Accepted by InterSpeech 202

Peak-First CTC: Reducing the Peak Latency of CTC Models by Applying Peak-First Regularization

The CTC model has been widely applied to many application scenarios because of its simple structure, excellent performance, and fast inference speed. There are many peaks in the probability distribution predicted by the CTC models, and each peak represents a non-blank token. The recognition latency of CTC models can be reduced by encouraging the model to predict peaks earlier. Existing methods to reduce latency require modifying the transition relationship between tokens in the forward-backward algorithm, and the gradient calculation. Some of these methods even depend on the forced alignment results provided by other pretrained models. The above methods are complex to implement. To reduce the peak latency, we propose a simple and novel method named peak-first regularization, which utilizes a frame-wise knowledge distillation function to force the probability distribution of the CTC model to shift left along the time axis instead of directly modifying the calculation process of CTC loss and gradients. All the experiments are conducted on a Chinese Mandarin dataset AISHELL-1. We have verified the effectiveness of the proposed regularization on both streaming and non-streaming CTC models respectively. The results show that the proposed method can reduce the average peak latency by about 100 to 200 milliseconds with almost no degradation of recognition accuracy.Comment: Submitted to ICASSP 2023(5 pages, 2 figures

Towards Large-scale Masked Face Recognition

During the COVID-19 coronavirus epidemic, almost everyone is wearing masks, which poses a huge challenge for deep learning-based face recognition algorithms. In this paper, we will present our \textbf{championship} solutions in ICCV MFR WebFace260M and InsightFace unconstrained tracks. We will focus on four challenges in large-scale masked face recognition, i.e., super-large scale training, data noise handling, masked and non-masked face recognition accuracy balancing, and how to design inference-friendly model architecture. We hope that the discussion on these four aspects can guide future research towards more robust masked face recognition systems.Comment: the top1 solution for ICCV2021-MFR challeng

Teach-DETR: Better Training DETR with Teachers

In this paper, we present a novel training scheme, namely Teach-DETR, to learn better DETR-based detectors from versatile teacher detectors. We show that the predicted boxes from teacher detectors are effective medium to transfer knowledge of teacher detectors, which could be either RCNN-based or DETR-based detectors, to train a more accurate and robust DETR model. This new training scheme can easily incorporate the predicted boxes from multiple teacher detectors, each of which provides parallel supervisions to the student DETR. Our strategy introduces no additional parameters and adds negligible computational cost to the original detector during training. During inference, Teach-DETR brings zero additional overhead and maintains the merit of requiring no non-maximum suppression. Extensive experiments show that our method leads to consistent improvement for various DETR-based detectors. Specifically, we improve the state-of-the-art detector DINO with Swin-Large backbone, 4 scales of feature maps and 36-epoch training schedule, from 57.8% to 58.9% in terms of mean average precision on MSCOCO 2017 validation set. Code will be available at https://github.com/LeonHLJ/Teach-DETR