A Transformer-Based Siamese Network for Change Detection

This paper presents a transformer-based Siamese network architecture (abbreviated by ChangeFormer) for Change Detection (CD) from a pair of co-registered remote sensing images. Different from recent CD frameworks, which are based on fully convolutional networks (ConvNets), the proposed method unifies hierarchically structured transformer encoder with Multi-Layer Perception (MLP) decoder in a Siamese network architecture to efficiently render multi-scale long-range details required for accurate CD. Experiments on two CD datasets show that the proposed end-to-end trainable ChangeFormer architecture achieves better CD performance than previous counterparts. Our code is available at https://github.com/wgcban/ChangeFormer.Comment: Accepted to International Geoscience and Remote Sensing Symposium (IGARSS), 2022. 4 pages, 2 figures. Code & trained models are available at https://github.com/wgcban/ChangeForme

Unite and Conquer: Plug & Play Multi-Modal Synthesis using Diffusion Models

Generating photos satisfying multiple constraints find broad utility in the content creation industry. A key hurdle to accomplishing this task is the need for paired data consisting of all modalities (i.e., constraints) and their corresponding output. Moreover, existing methods need retraining using paired data across all modalities to introduce a new condition. This paper proposes a solution to this problem based on denoising diffusion probabilistic models (DDPMs). Our motivation for choosing diffusion models over other generative models comes from the flexible internal structure of diffusion models. Since each sampling step in the DDPM follows a Gaussian distribution, we show that there exists a closed-form solution for generating an image given various constraints. Our method can unite multiple diffusion models trained on multiple sub-tasks and conquer the combined task through our proposed sampling strategy. We also introduce a novel reliability parameter that allows using different off-the-shelf diffusion models trained across various datasets during sampling time alone to guide it to the desired outcome satisfying multiple constraints. We perform experiments on various standard multimodal tasks to demonstrate the effectiveness of our approach. More details can be found in https://nithin-gk.github.io/projectpages/Multidiff/index.htmlComment: Accepted at CVPR 202

DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detection

Remote sensing change detection is crucial for understanding the dynamics of our planet's surface, facilitating the monitoring of environmental changes, evaluating human impact, predicting future trends, and supporting decision-making. In this work, we introduce a novel approach for change detection that can leverage off-the-shelf, unlabeled remote sensing images in the training process by pre-training a Denoising Diffusion Probabilistic Model (DDPM) - a class of generative models used in image synthesis. DDPMs learn the training data distribution by gradually converting training images into a Gaussian distribution using a Markov chain. During inference (i.e., sampling), they can generate a diverse set of samples closer to the training distribution, starting from Gaussian noise, achieving state-of-the-art image synthesis results. However, in this work, our focus is not on image synthesis but on utilizing it as a pre-trained feature extractor for the downstream application of change detection. Specifically, we fine-tune a lightweight change classifier utilizing the feature representations produced by the pre-trained DDPM alongside change labels. Experiments conducted on the LEVIR-CD, WHU-CD, DSIFN-CD, and CDD datasets demonstrate that the proposed DDPM-CD method significantly outperforms the existing state-of-the-art change detection methods in terms of F1 score, IoU, and overall accuracy, highlighting the pivotal role of pre-trained DDPM as a feature extractor for downstream applications. We have made both the code and pre-trained models available at https://github.com/wgcban/ddpm-cdComment: Code available at: https://github.com/wgcban/ddpm-c

Growth of children receiving a dehydrated potato-soy protein concentrate or corn-soy blend as part of a food aid program in Northern Senegal

Rations distributed by food aid programs are intended to improve the growth of undernourished children. In practice, food programs target individual children and provide a supplement to the family that is intended to increase the energy and nutrient intake of undernourished children. Multiple food rations are available yet few studies have compared their differential effect on the growth of children. The objective of the study was to compare growth in undernourished Senegalese children who received a newly developed dehydrated potato-soy protein concentrate blend (PSB) to those supplemented with the currently available corn-soy blend (CSB). The first child at each site was randomly assigned to receive PSB or CSB and subsequent children alternately received PSB or CSB. Eligibility for obtaining the food ration was basedon criteria determined by the USAID (P.L. 480) Title II Food Aid Program. Children received iso-caloric amounts of the two supplements each month (23,000kcals). Weight, height and mid-upper arm circumference (MUAC) were taken over a fourmonth period. Z-scores were calculated for weight-for-age (WAZ), weight-for-height (WHZ) and for length/height-for-age measures (HAZ).The study was conducted at 7 clinics which served as food distribution sites in northern Senegal. The study enrolled348 children 18-56 months old with a weight-for-age z-score below the �yellow� zone of the locally available growth chart (equivalent to WAZ < -1.0). WAZ and HAZ significantly increased over time but there was no difference between the two ration groups. In a subset of 280 children (145 PSB, 135 CSB) who attended all four appointments and received the full complement of ration, there was significant and equivalent increase for both groups in WAZ and WHZ. These findings indicate thatchildren participating in the food aid program significantly improved their growth over a four-month period. Using the new PSB as a ration had the same impact on growth as the standard CSB and required less fuel to prepare

Prévalence de la tuberculose et de la brucellose chez les animaux sélectionnés du projet d´appui au développement de l´élevage du Zébu maure (PRODEZEM) dans le cercle de Nara: Prevalence of tuberculosis and brucellosis in selected animals of the project to support the development of Moorish Zebu breeding (PRODEZEM) in the Nara circle

Introduction: La tuberculose et la brucellose bovines représentent des contraintes majeures au dévelop-pement de l’élevage bovin laitier au Mali. A Nara, pour fixer la race Zébu maure, le dépistage de ces deux maladies zoonotiques bovines a été entrepris sur les bovins du projet. L’objectif de ce travail était de déterminer leurs prévalences dans les noyaux sélectionnés. Méthodes: Une étude transversale de type descriptif a été menée sur les bovins des noyaux de zébus maures. Les tests de tuberculination et de Rose Bengale ont été effectués sur les bovins des noyaux du projet. Les prévalences de chaque maladie ont été obtenues en faisant le rapport entre le nombre de cas positif sur le nombre total d’animaux testés. Résultats: Au total, les tests de dépistage ont concerné 1112 sujets de 50 noyaux présélectionnés dans les cinq communes du cercle de Nara. Les prévalences de la tuberculose et la brucellose bovines sont respectivement de 0,90 % et de 0,27 %. Concernant la tuberculose, le maximum de cas a été observé à Niamana (4 cas) et le minimum à Guenéibe (1 cas). Par rapport à la brucellose, le maximum de cas a été enregistré dans la commune de Guiré (2 cas). Conclusion: Cette étude a montré de faibles taux d’infection de la tuberculose et de la brucellose chez les bovins du projet. L’étude a en outre permis d’avoir de nouvelles connaissances sur l’épidémiologie de ces maladies zoonotiques dans les noyaux sélectionnés du projet. Background: Bovine tuberculosis and brucellosis are major constraints to the development of dairy cattle farming in Mali. In Nara, in order to establish the Moorish Zebu breed, screening for these two zoonotic bovine diseases was undertaken on the cattle project. The objective of this study was to determine the prevalence in the selected nuclei. Methods: A descriptive cross-sectional study was conducted on cattle in the Moorish Zebu nuclei. Tuberculin and Rose Bengal tests were carried out on cattle in the project nuclei. The prevalence of each disease were obtained as the ratio of the number of positive cases to the total number of animals tested. Results: A total of 1112 animals from 50 pre-selected nuclei in the five communes of the Nara circle were tested. The prevalence of bovine tuberculosis and brucellosis were 0.90% and 0.27% respectively. As regards tuberculosis, the maximum number of cases was observed in Niamana (4 cases) and the minimum in Guenéibe (1 case). With regard to brucellosis, the maximum number of cases was recorded in the commune of Guiré (2 cases). Conclusion: This study showed low infection rates of tuberculosis and brucellosis in the project cattle. It also provided new insights into the epidemiology of these zoonotic diseases in the selected nuclei of the project

High frequency of Plasmodium falciparum chloroquine resistance marker (pfcrt T76 mutation) in Yemen: An urgent need to re-examine malaria drug policy

<p>Abstract</p> <p>Background</p> <p>Malaria remains a significant health problem in Yemen with <it>Plasmodium falciparum </it>being the predominant species which is responsible for 90% of the malaria cases. Despite serious concerns regarding increasing drug resistance, chloroquine is still used for the prevention and treatment of malaria in Yemen. This study was carried out to determine the prevalence of choloroquine resistance (CQR) of <it>P. falciparum </it>isolated from Yemen based on the <it>pfcrt </it>T76 mutation.</p> <p>Methods</p> <p>A cross-sectional study was carried out among 511 participants from four governorates in Yemen. Blood samples were screened using microscopic and species-specific nested PCR based on the 18S rRNA gene to detect and identify <it>Plasmodium </it>species. Blood samples positive for <it>P. falciparum </it>were used for detecting the <it>pfcrt </it>T76 mutation using nested-PCR.</p> <p>Results</p> <p>The prevalence of <it>pfcrt </it>T76 mutation was 81.5% (66 of 81 isolates). Coastal areas/foothills had higher prevalence of <it>pfcrt </it>T76 mutation compared to highland areas (90.5% <it>vs </it>71.8%) (p = 0.031). The <it>pfcrt </it>T76 mutation had a significant association with parasitaemia (p = 0.045). Univariate analysis shows a significant association of <it>pfcrt </it>T76 mutation with people aged > 10 years (OR = 9, 95% CI = 2.3 - 36.2, p = 0.001), low household income (OR = 5, 95% CI = 1.3 - 19.5, p = 0.027), no insecticide spray (OR = 3.7, 95% CI = 1.16 - 11.86, p = 0.025) and not sleeping under insecticide treated nets (ITNs) (OR = 4.8, 95% CI = 1.38 - 16.78, p = 0.01). Logistic regression model confirmed age > 10 years and low household income as predictors of <it>pfcrt </it>T76 mutation in Yemen <it>P. falciparum </it>isolates.</p> <p>Conclusions</p> <p>The high prevalence of <it>pfcrt </it>T76 mutation in Yemen could be a predictive marker for the prevalence of <it>P. falciparum </it>CQR. This finding shows the necessity for an in-vivo therapeutic efficacy test for CQ.<it> P. falciparum </it>CQR should be addressed in the national strategy to control malaria.</p

Image Generation with Multimodal Priors using Denoising Diffusion Probabilistic Models

Image synthesis under multi-modal priors is a useful and challenging task that has received increasing attention in recent years. A major challenge in using generative models to accomplish this task is the lack of paired data containing all modalities (i.e. priors) and corresponding outputs. In recent work, a variational auto-encoder (VAE) model was trained in a weakly supervised manner to address this challenge. Since the generative power of VAEs is usually limited, it is difficult for this method to synthesize images belonging to complex distributions. To this end, we propose a solution based on a denoising diffusion probabilistic models to synthesise images under multi-model priors. Based on the fact that the distribution over each time step in the diffusion model is Gaussian, in this work we show that there exists a closed-form expression to the generate the image corresponds to the given modalities. The proposed solution does not require explicit retraining for all modalities and can leverage the outputs of individual modalities to generate realistic images according to different constraints. We conduct studies on two real-world datasets to demonstrate the effectiveness of our approac

Sistem Peramalan Cuaca dengan Fuzzy Mamdani (Studi Kasus: BMKG Lasiana)

The weather is one part of human daily life. Many people who depend their lives on the weather to do every activity. Therefore, knowing the weather forecasting will give consideration to the community to be able to carry out various activities of human life such as in the field of aviation, shipping, agriculture, processed industries and others that depend on weather conditions. For this reason, the Indonesian BMKG has the duty to provide weather forecast information based on existing meteorological data using complex calculations. This study aims to build a system that will be an alternative for BMKG in forecasting weather using fuzzy based on four supporting criteria, namely air temperature, humidity, wind speed and air pressure. In doing weather forecasts using mamdani fuzzy there are several steps, namely determining the fuzzy set, the application of the implication function using the MIN function, the composition of the rules using the MAX function, and finally the Defuzzification process using the MOM method. This system will produce weather forecast results based on data on air temperature, air humidity, wind speed and air pressure that have been entered by the system user by showing the membership level of the predicted results. Based on testing that has been done, it is concluded that the system built using mamdani fuzzy can do a good weather forecast with a system accuracy rate of 61,062% using daily weather data as many as 1826 data in 2013-2017, with the lowest accuracy level found in 2015 with an accuracy rate of 54,247 % and highest accuracy in 2017 amounted to 65.207%