A Deep Four-Stream Siamese Convolutional Neural Network with Joint Verification and Identification Loss for Person Re-detection

State-of-the-art person re-identification systems that employ a triplet based deep network suffer from a poor generalization capability. In this paper, we propose a four stream Siamese deep convolutional neural network for person redetection that jointly optimises verification and identification losses over a four image input group. Specifically, the proposed method overcomes the weakness of the typical triplet formulation by using groups of four images featuring two matched (i.e. the same identity) and two mismatched images. This allows us to jointly increase the interclass variations and reduce the intra-class variations in the learned feature space. The proposed approach also optimises over both the identification and verification losses, further minimising intra-class variation and maximising inter-class variation, improving overall performance. Extensive experiments on four challenging datasets, VIPeR, CUHK01, CUHK03 and PRID2011, demonstrates that the proposed approach achieves state-of-the-art performance.Comment: Published in WACV 201

Targeting Cell Wall Formation in the Oomycete Phytophthora cinnamomi for Disease Control

The oomycete Phytophthora genus comprises microorganisms that cause devastating plant diseases, such as late blight and root rot diseases, leading to significant agricultural economic losses, and causing extensive damages to ecosystems. To date, no practical method is available to prevent these diseases. Furthermore, current strategies to control Phytophthora-induced diseases are ineffective in the long term. These strategies currently rely on different classes of chemicals. However, repeated use of the same chemicals can lead to development of pesticide resistance in phytopathogens. This concern, combined with an increased awareness of alternative approaches that have minimal impact on biodiversity and human health, highlights that efficient methods for controlling diseases caused by Phytophthora are urgently required. Targeting cell wall biosynthesis is a promising strategy to combat these pathogens. Indeed, the inhibition of enzymes involved in carbohydrate biosynthesis affects the growth and survival of these pathogens, offering a promising avenue for the development of effective treatments. In recent years, plant antimicrobial peptides (AMPs) have been found to be effective against different phytopathogens. Well-known AMPs are plant defensins, a family of small cysteinerich peptides that can bind to chitin and cell wall glucans in fungi. However, knowledge about the inhibitory role of plant defensins in oomycetes is limited. As such, this work investigates the effects of the plant defensin NaD1 (Nicotiana alata defensin 1) on Phytophthora species, which may reveal novel opportunities for controlling plant diseases. Our findings demonstrate that NaD1 effectively inhibits the mycelial growth of Phytophthora cinnamomi, Phytophthora cambivora, Phytophthora nicotianae, and Phytophthora citricola. Exposure to NaD1 induced alterations in the growth and structure of P. cinnamomi, leading to suppressed apical dominance, hyper-branching, and changes in cell wall composition, likely due to disruption of calcium homeostasis. Transcriptomic analyses confirmed altered expression of genes involved in cellulose synthesis and calcium transport (Chapter 2), and uncovered changes in the transcriptome across the entire genome in hyphal cells exposed to NaD1, shedding light on the mechanism of action of this AMP. These differentially expressed genes can serve as candidates to study the efficacy of NaD1 against Phytophthora species (Chapter 4). In addition to NaD1, the effects of a chitin synthase inhibitor, nikkomycin Z, were also investigated. This study shows that nikkomycin Z causes strong growth inhibition of four Phytophthora species and induces abnormal hyphal growth. Exposure to this inhibitor decreases cellulose levels and affects the expression of genes related to vital functions such as cell wall biosynthesis, hexosamine biosynthesis and chitin deacetylation (Chapter 3). Altogether, the present work reveals critical information about the fundamental inhibitory mechanisms of NaD1 and nikkomycin Z on Phytophthora species, with a focus on cell wall biosynthesis. This work paves the way for the development of novel effective targets for oomycete disease control.Thesis (Ph.D.) -- University of Adelaide, School of Agriculture, Food & Wine, 202

Role of Elaborate Cardiotocography (CTG) in Pregnancy Management

Background: Elaborate Cardiotocography (CTG) is the most commonly used test for antepartum and intrapartum fetal surveillance because it gives information via the cerebro-cardiac response of fetal cerebral activity, which is modified by the hypoxia. Objective: This study was designed to compare the perinatal outcomes among the normal and abnormal CTG groups. Method: It was a prospective observational study carried out in the Department of obstetrics, BSMMU during the period July 2006 to July 2008. Hundred consecutive normal and hundred consecutive abnormal CTC tracings were collected from patients who were advised to perform CTG after admission. Both labour and non-labour patients were included. Interpretation of CTG was done based on FlGO recommendation (1987). Pregnancy and neonatal data were obtained and the findings were correlated with the FHR tracing. Statistical analysis was carried out by student's unpaired t-test, X2 and Z-test. Level of significance was set at P value < 0.05. Results: Out of 100 abnormal CTG, 30% had tachycardia, 42% had deceleration, 38% was non reactive, 4% had absence beat-to-beat variability and 4% had fetal bradycardia. There was significantly higher caesarean delivery, lower apgar score, higher requirement of neonatal resuscitation and admission at neonatal unit and higher perinatal death among the abnormal CTG group. The abnormal fetal outcome was found highest in heart rate deceleration group. Conclusion: CTG can be continued as a good screening test of fetal surveillance but it is not the sole criteria to influence the management of high-risk pregnancies. Abnormal CTG should be supplemented with other test before intervention. Key words: CTG; Perinatal outcome.DOI: 10.3329/bsmmuj.v2i1.3706 BSMMU J 2009; 2(1): 18-2

End-to-End Domain Adaptive Attention Network for Cross-Domain Person Re-Identification

Person re-identification (re-ID) remains challenging in a real-world scenario, as it requires a trained network to generalise to totally unseen target data in the presence of variations across domains. Recently, generative adversarial models have been widely adopted to enhance the diversity of training data. These approaches, however, often fail to generalise to other domains, as existing generative person re-identification models have a disconnect between the generative component and the discriminative feature learning stage. To address the on-going challenges regarding model generalisation, we propose an end-to-end domain adaptive attention network to jointly translate images between domains and learn discriminative re-id features in a single framework. To address the domain gap challenge, we introduce an attention module for image translation from source to target domains without affecting the identity of a person. More specifically, attention is directed to the background instead of the entire image of the person, ensuring identifying characteristics of the subject are preserved. The proposed joint learning network results in a significant performance improvement over state-of-the-art methods on several benchmark datasets.Comment: submitted to IEEE Transactions on Information Forensics and Securit

Chitosan biostimulant controls infection of cucumber by Phytophthora capsici through suppression of asexual reproduction of the pathogen

The biopolymer chitosan is a derivative of chitin, which can promote plant growth and protect plants from phytopathogens. This study aimed to evaluate the efficacy of chitosan as a biostimulant and a biorational agent to protect cucumber plants from damping-off disease caused by Phytophthora capsici. Cucumber seeds were treated with a range of chitosan concentrations, viz. 0, 125, 250, and 500 ppm, to evaluate effect on seed germination and fresh root and shoot weight of the seedlings. Chitosan significantly (p ≤ 0.05) enhanced seed germination and root and shoot growth of cucumber in a dose-dependent manner up to 500 ppm. Application of in vitro chitosan suspension onto P. capsici mycelial plug suppressed growth of mycelia, formation of sporangia, and release of P. capsici zoospores at 125–500 ppm concentrations. Cucumber seedlings from chitosan-treated seeds showed enhanced resistance to damping-off disease caused by P. capsici compared to untreated control. Cucumber seedlings from 500 ppm chitosan seed treatment showed 100% disease resistance against damping off caused by P. capsici. These results suggest that chitosan could be used as a natural and environmentally safe alternative to a synthetic growth promoter and pesticide for sustainable production of cucumber

Deep learning for person re-identification

This thesis addresses the problem of correctly re-identifying a target person in a crowded environment, in a multi-camera surveillance system to ensure the safety of people in mass gatherings. Using deep neural networks, we provide effective solutions to the challenges caused by variations in lighting conditions, viewing angles, background, and occlusion in a camera network, and demonstrate the efficacy of the novel algorithms and frameworks that we have developed for accurate person re-identification in real world scenarios

Allowing more solar power connected to the grid, using thermal and ageing models of distribution transformers.

Increasing amounts of solar power connected to the low-voltage network will adversely affect the performance of the network. The two impacts that will most often set the limit are overvoltage with the customers and overloading the distribution transformer. In this work, alternative methods have been studied for determining when a transformer is overloaded, to allow more solar power to be connected to the low-voltage network, i.e., increasing the hosting capacity for solar power.A limit-based method on the highest temperature inside the transformer (the hotspot temperature) and a method based on the loss-of-life of the transformer insulation due to hotspot temperatures above the design temperature are those alternative methods in this study. These methods are known as "dynamic transformer rating", a technology proposed in the literature but with very little practical experience in distribution networks.Two models were developed and implemented in MATLAB: a thermal model of the transformer calculating the hotspot temperature for a given time series of loading and ambient temperature; and a model for the loss-of-life of the winding insulation for given time series of the hotspot temperature. These models have been applied to existing distribution networks: measured consumption patterns with high time resolution (10-minute time step) for nine different distribution transformers for 1.5 years (network operator); measured ambient temperature (SMHI); and solar-power production calculated from satellite measurements (Renewables Ninja).For these nine distribution transformers, the time series of the hotspot temperature and the loss-of-life over the 1.5 years have been calculated for different values of the solar power installed capacity on the low-voltage side of the distribution transformer. The resulting time series are used to estimate the hosting capacity for solar power of a 200 kVA transformer. Using the existing design methods, the hosting capacity is 200 kW. Once that value is reached, the further connection of solar power should be stopped until a larger transformer is available. According to IEC design methods, the hosting capacity is about 270 kW using a limit to the hotspot temperature. This value somewhat depends on the loading patterns of the transformer before the connection of solar power. Once that value is reached, the further connection should again be stopped. Even for installed capacity exceeding 270 kW, the loss of life of the transformer insulation is still small and acceptable. This allows for further connection of PV without the immediate need to replace the transformer. Even values up to 350 or 400 kW may be acceptable, but a limit based on loss-of-life will require a detailed risk analysis as the pre-solar loading of the transformer is shown to play an important role.This work has shown that dynamic transformer rating allows more solar power to be connected to a distribution network than using classical rating methods without unacceptable risk for transformer loss-of-life

Allowing more solar power connected to the grid, using thermal and ageing models of distribution transformers.

Increasing amounts of solar power connected to the low-voltage network will adversely affect the performance of the network. The two impacts that will most often set the limit are overvoltage with the customers and overloading the distribution transformer. In this work, alternative methods have been studied for determining when a transformer is overloaded, to allow more solar power to be connected to the low-voltage network, i.e., increasing the hosting capacity for solar power.A limit-based method on the highest temperature inside the transformer (the hotspot temperature) and a method based on the loss-of-life of the transformer insulation due to hotspot temperatures above the design temperature are those alternative methods in this study. These methods are known as "dynamic transformer rating", a technology proposed in the literature but with very little practical experience in distribution networks.Two models were developed and implemented in MATLAB: a thermal model of the transformer calculating the hotspot temperature for a given time series of loading and ambient temperature; and a model for the loss-of-life of the winding insulation for given time series of the hotspot temperature. These models have been applied to existing distribution networks: measured consumption patterns with high time resolution (10-minute time step) for nine different distribution transformers for 1.5 years (network operator); measured ambient temperature (SMHI); and solar-power production calculated from satellite measurements (Renewables Ninja).For these nine distribution transformers, the time series of the hotspot temperature and the loss-of-life over the 1.5 years have been calculated for different values of the solar power installed capacity on the low-voltage side of the distribution transformer. The resulting time series are used to estimate the hosting capacity for solar power of a 200 kVA transformer. Using the existing design methods, the hosting capacity is 200 kW. Once that value is reached, the further connection of solar power should be stopped until a larger transformer is available. According to IEC design methods, the hosting capacity is about 270 kW using a limit to the hotspot temperature. This value somewhat depends on the loading patterns of the transformer before the connection of solar power. Once that value is reached, the further connection should again be stopped. Even for installed capacity exceeding 270 kW, the loss of life of the transformer insulation is still small and acceptable. This allows for further connection of PV without the immediate need to replace the transformer. Even values up to 350 or 400 kW may be acceptable, but a limit based on loss-of-life will require a detailed risk analysis as the pre-solar loading of the transformer is shown to play an important role.This work has shown that dynamic transformer rating allows more solar power to be connected to a distribution network than using classical rating methods without unacceptable risk for transformer loss-of-life

Semantic consistency and identity mapping multi-component generative adversarial network for person re-identification

In a real world environment, person re-identification (Re-ID) is a challenging task due to variations in lighting conditions, viewing angles, pose and occlusions. Despite recent performance gains, current person Re-ID algorithms still suffer heavily when encountering these variations. To address this problem, we propose a semantic consistency and identity mapping multi-component generative adversarial network (SC-IMGAN) which provides style adaptation from one to many domains. To ensure that transformed images are as realistic as possible, we propose novel identity mapping and semantic consistency losses to maintain identity across the diverse domains. For the Re-ID task, we propose a joint verification-identification quartet network which is trained with generated and real images, followed by an effective quartet loss for verification. Our proposed method outperforms state-of-the-art techniques on six challenging person Re-ID datasets: CUHK01, CUHK03, VIPeR, PRID2011, iLIDS and Market-1501

Pose-driven attention-guided image generation for person re-Identification

Person re-identification (re-ID) concerns the matching of subject images across different camera views in a multi camera surveillance system. One of the major challenges in person re-ID is pose variations across the camera network, which significantly affects the appearance of a person. Existing development data lack adequate pose variations to carry out effective training of person re-ID systems. To solve this issue, in this paper we propose an end-to-end pose-driven attention-guided generative adversarial network, to generate multiple poses of a person. We propose to attentively learn and transfer the subject pose through an attention mechanism. A semantic-consistency loss is proposed to preserve the semantic information of the person during pose transfer. To ensure fine image details are realistic after pose translation, an appearance discriminator is used while a pose discriminator is used to ensure the pose of the transferred images will exactly be the same as the target pose. We show that by incorporating the proposed approach in a person re-identification framework, realistic pose transferred images and state-of-the-art re-identification results can be achieved.</p