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The rapid rise of the digital economy is aided by the new generation of information networks. Optical Fiber networks are foundation stone of cost-effective sustainable mode of communication of our society by leveraging high bandwidth, low latency, reliability, security, stability, enabling long-term economic growth through cutting-edge services and applications for industries, business and users. F5G intends to lay the groundwork for a systematic structure to the growth of fixed networks, including generational planning and pushing the technology's innovations and expansion into as many industries as feasible through Fiber-To-The-Everywhere-and-Everything. Fixed networks have become a crucial public infrastructure in the growing global digitization process, and their development level is an essential metric for evaluating a country's overall strength. This article introduces the trend for F5G networks along with recommendations for their development.

Software Defined Networking (SDN) is the novel networking paradigm where decoupling of the control plane from the data plane has its inherent advantages. Controller Placement Problem (CPP) involves placing the optimal number of controllers at the appropriate locations while meeting prerequisites such as latency, load balancing, energy and computational time. To achieve scalability, deployment of multiple controllers on large-scale SDN is one of the key challenges. CPP can be addressed as a multi-objective combinatorial optimization problem whose solution is a trade-off between multiple optimization parameters. In this paper, a novel population-based meta-heuristic algorithm viz. Naked Mole-Rat (NMR) Algorithm has been proposed to optimize the location for controller placement based on Switch-Controller (SC), Controller-Controller (CC) latency while maintaining load balancing among the controllers. The ideas and mechanisms are illustrated using two publicly available standard topologies viz. Ernet and Savvis. The controller localization approach implemented with NMR algorithm has slightly a better result as compared with the Bat algorithm.

Titanium nitride (TiN) materials have gained an interest over the past years due to their unique characteristics, such as thermal stability, extreme hardness, low production cost, and comparable optical properties to gold. In the present study, TiN nanoparticles were synthesized via a thermal benzene route to obtain black nanoparticles. Scanning electron microscopy (SEM) was carried out to examine the morphology. Further microscopic characterization was done where the final product was drop cast onto double-sided conductive carbon tape and sputter-coated with gold/palladium at a thickness of 4 nm for characterization by field emission scanning electron microscopy (FE-SEM) with energy dispersive X-Ray spectroscopy (EDS) that revealed they are spherical. ImageJ software was used to measure the average size of the particles to be 79 nm in diameter. EDS was used to determine the elements present in the sample and concluded that there were no impurities. Further characterization by Fourier Transform infrared (FTIR) spectroscopy was carried out to identify the characteristic peaks of TiN. X-ray diffraction (XRD) revealed typical peaks of cubic phase titanium nitride, and crystallite size was determined to be 14 nm using the Debye-Scherrer method. Dynamic light scattering (DLS) analysis revealed the size distribution of the TiN nanoparticles, with nanoparticles averaging at 154 nm in diameter. Zeta potential concluded the surface of the TiN nanoparticles is negatively charged.

Group A Streptococcus (GAS) infection is associated with multiple clinical sequelae, including different subtypes of psoriasis. Such post-streptococcal disorders have been long known but are largely unexplained. CD1a is expressed at constitutively high levels by Langerhans cells and presents lipid antigens to T cells, but the potential relevance to GAS infection has not been studied. Here, we investigated whether GAS-responsive CD1a-restricted T cells contribute to the pathogenesis of psoriasis. Healthy individuals had high frequencies of circulating and cutaneous GAS-responsive CD4+ and CD8+ T cells with rapid effector functions, including the production of interleukin-22 (IL-22). Human skin and blood single-cell CITE-seq analyses of IL-22-producing T cells showed a type 17 signature with proliferative potential, whereas IFN-γ-producing T cells displayed cytotoxic T lymphocyte characteristics. Furthermore, individuals with psoriasis had significantly higher frequencies of circulating GAS-reactive T cells, enriched for markers of activation, cytolytic potential, and tissue association. In addition to responding to GAS, subsets of expanded GAS-reactive T cell clones/lines were found to be autoreactive, which included the recognition of the self-lipid antigen lysophosphatidylcholine. CD8+ T cell clones/lines produced cytolytic mediators and lysed infected CD1a-expressing cells. Furthermore, we established cutaneous models of GAS infection in a humanized CD1a transgenic mouse model and identified enhanced and prolonged local and systemic inflammation, with resolution through a psoriasis-like phenotype. Together, these findings link GAS infection to the CD1a pathway and show that GAS infection promotes the proliferation and activation of CD1a-autoreactive T cells, with relevance to post-streptococcal disease, including the pathogenesis and treatment of psoriasis.

Real-world objects perform complex motions that involve multiple independent motion components. For example, while talking, a person continuously changes their expressions, head, and body pose. In this work, we propose a novel method to decompose motion in videos by using a pretrained image GAN model. We discover disentangled motion subspaces in the latent space of widely used style-based GAN models that are semantically meaningful and control a single explainable motion component. The proposed method uses only a few $(\approx10)$ ground truth video sequences to obtain such subspaces. We extensively evaluate the disentanglement properties of motion subspaces on face and car datasets, quantitatively and qualitatively. Further, we present results for multiple downstream tasks such as motion editing, and selective motion transfer, e.g. transferring only facial expressions without training for it., AI for content creation, CVPRW-2023

One of the important and challenging tasks in cloud computing is to obtain the usefulness of cloud by implementing several specifications for our needs, to meet the present growing demands, and to minimize energy consumption as much as possible and ensure proper utilization of computing resources. An excellent mapping scheme has been derived which maps virtual machines (VMs) to physical machines (PMs), which is also known as virtual machine (VM) placement, and this needs to be implemented. The tremendous diversity of computing resources, tasks, and virtualization processes in the cloud causes the consolidation method to be more complex, tedious, and problematic. An algorithm for reducing energy use and resource allocation is proposed for implementation in this article. This algorithm was developed with the help of a Cloud System Model, which enables mapping between VMs and PMs and among tasks of VMs. The methodology used in this algorithm also supports lowering the number of PMs that are in an active state and optimizes the total time taken to process a set of tasks (also known as makespan time). Using the CloudSim Simulator tool, we evaluated and assessed the energy consumption and makespan time. The results are compiled and then compared graphically with respect to other existing energy-efficient VM placement algorithms.

Abstract The future U.S. electric grid is being transformed with deep decarbonization of generation (i.e., removing or reducing reliance on fossil fuels and replacing them with renewable and clean energy resources), which in practice is not achievable without a dramatic increase in the reliance on long-duration energy storage (LDES) technologies. Regulators at both the state and federal level are well advised to take steps to address current policy gaps, build frameworks that will enable a greater role for LDES to contribute to grid reliability and be fairly compensated for its grid services.. Decarbonization by definition is dependent on an increasing reliance on variable renewable energy, primarily wind and solar resources, that needs to be stored for longer durations to maintain electric grid reliability and provide operational flexibility to grid operators. However, despite the growing realization of the need for long-duration energy storage (LDES) technologies, a persistent gap of policy levers at the federal and state level creates a vacuum in terms of defining how and where LDES technologies can be utilized to support the electric grid, along with an inadequate regulatory framework wherein these resources will need to be valued and compensated for the services they can provide. This paper—which is primarily intended for US decision makers, but should be of value for all energy professionals and the general public—addresses policy gaps, needs, and opportunities for LDES that require urgent attention from US-based policymakers at the federal and state level. This paper also provides background information on how the US E&U industry is structured and regulated, along with perspectives on LDES technologies and applications, all of which have direct relevance to the paper’s primary focus on the need for LDES policymaking. Discussion Despite a generally accepted future need for long-duration energy storage (LDES) technologies that is directly tied to the rapid of renewable resources on the U.S. electric grid, there is a lack of policymaking, market designs, and compensation mechanisms for LDES technologies. Decarbonization (i.e., the goal of removing or reducing reliance on fossil fuels) cannot be achieved at the aggressive levels envisioned without utilizing LDES. Policymakers must take steps now to build frameworks that recognize the unique ways in which LDES will increasingly contribute to grid reliability and resilience, and receive appropriate compensation for the services it provides. Graphical abstract

The usage of artificial intelligence (AI) technologies that depend on massive volumes of data, which are frequently made available through IoT, is thus directly related to the creation of smart governments. In order to increase the effectiveness of governance and the standard of living for citizens, Internet - of - things enabled AI technologies can be used in several important areas of smart government. In order to provide fulfilled government functions, AI and its subdomain innovations have the opportunity to address a number of current organizational inadequacies. In this chapter the foundations, benefits, and challenges implementing these technologies in the public sector or government are discussed. Following that, the various AI, Ml technologies and IoT frameworks for smart governance are explored. Then the focus is on the applications and use cases of IoT, AI and machine learning for smart governance.

Randomized smoothing (RS) is a well known certified defense against adversarial attacks, which creates a smoothed classifier by predicting the most likely class under random noise perturbations of inputs during inference. While initial work focused on robustness to $\ell_2$ norm perturbations using noise sampled from a Gaussian distribution, subsequent works have shown that different noise distributions can result in robustness to other $\ell_p$ norm bounds as well. In general, a specific noise distribution is optimal for defending against a given $\ell_p$ norm based attack. In this work, we aim to improve the certified adversarial robustness against multiple perturbation bounds simultaneously. Towards this, we firstly present a novel \textit{certification scheme}, that effectively combines the certificates obtained using different noise distributions to obtain optimal results against multiple perturbation bounds. We further propose a novel \textit{training noise distribution} along with a \textit{regularized training scheme} to improve the certification within both $\ell_1$ and $\ell_2$ perturbation norms simultaneously. Contrary to prior works, we compare the certified robustness of different training algorithms across the same natural (clean) accuracy, rather than across fixed noise levels used for training and certification. We also empirically invalidate the argument that training and certifying the classifier with the same amount of noise gives the best results. The proposed approach achieves improvements on the ACR (Average Certified Radius) metric across both $\ell_1$ and $\ell_2$ perturbation bounds.

We present a framework to use recently introduced Capsule Networks for solving the problem of Optical Flow, one of the fundamental computer vision tasks. Most of the existing state of the art deep architectures either uses a correlation oepration to match features from them. While correlation layer is sensitive to the choice of hyperparameters and does not put a prior on the underlying structure of the object, spatio temporal features will be limited by the network's receptive field. Also, we as humans look at moving objects as whole, something which cannot be encoded by correlation or spatio temporal features. Capsules, on the other hand, are specialized to model seperate entities and their pose as a continuous matrix. Thus, we show that a simpler linear operation over poses of the objects detected by the capsules in enough to model flow. We show reslts on a small toy dataset where we outperform FlowNetC and PWC-Net models., 8 pages, 7 figures

India's dense human and animal populations, agricultural economy, changing environment, and social dynamics support conditions for emergence/re-emergence of zoonotic diseases that necessitate a One Health (OH) approach for control. In addition to OH national level frameworks, effective OH driven strategies that promote local intersectoral coordination and collaboration are needed to truly address zoonotic diseases in India. We conducted a literature review to assess the landscape of OH activities at local levels in India that featured intersectoral coordination and collaboration and supplemented it with our own experience conducting OH related activities with local partners. We identified key themes and examples in local OH activities. Our landscape assessment demonstrated that intersectoral collaboration primarily occurs through specific research activities and during outbreaks, however, there is limited formal coordination among veterinary, medical, and environmental professionals on the day-to-day prevention and detection of zoonotic diseases at district/sub-district levels in India. Examples of local OH driven intersectoral coordination include the essential role of veterinarians in COVID-19 diagnostics, testing of human samples in veterinary labs for Brucella and leptospirosis in Punjab and Tamil Nadu, respectively, and implementation of OH education targeted to school children and farmers in rural communities. There is an opportunity to strengthen local intersectoral coordination between animal, human and environmental health sectors by building on these activities and formalizing the existing collaborative networks. As India moves forward with broad OH initiatives, OH networks and experience at the local level from previous or ongoing activities can support implementation from the ground up.

In this research work concrete cube has been prepared by doing partial replacement for cement using nano materials like Multi-Walled Carbon Nano Tubes (MWCNTs), titanium dioxide (TiO2), and copper oxide (CuO) at different percentages like 0.1, 0.25, and 0.5%. Basic test has been done for materials and fresh concrete, followed by compressive strength test for concrete cubes. MWCNT gives more result compare to TiO2 and CuO. Just to reduce cement usage, fly ash has been used by keeping the compressive strength and the amount of nano materials as constant up to 35% of MWCNTs, TiO2, and CuO, with fly ash replaced for cement to obtain the normal strength. Cost analysis has been done to find out the economic efficiency of nano material concrete cube due to which TiO2 with fly ash cost 14.45Rs to prepare single cube, as MWCNTs and CuO with fly ash cost 726.4 and 17.64Rs to prepare single concrete cube. Almost TiO2 cube preparation took 10% reduction for the preparation of concrete cube compare to normal concrete cube has taken 15.96Rs. So we concluded that TiO2 with fly ash nano material concrete is the economical efficient concrete compared to MWCNTs and CuO with fly ash.

In this study adsorption technique was used for the removal of antibiotic pollutants from wastewater. Cephalexin (CEX) was selected for the current study purpose as it is commonly used drug. Commercial activated carbon (AC) was used as the nano-adsorbent considering its high degree of porosity, availability, economic considerations, and environmental friendliness. Batch adsorption of cephalexin by commercially prepared activated carbon was used for the study. The parameters considered in the experiment include time, pH, concentration of adsorbent, adsorbate, and temperature of 298.15 K was maintained. It was found experimentally that with pH in acidic range (3-7), contact time of 1 hour, and 5mg per 20 ml dosage of adsorbent, the maximum removal efficiency of 87% was achieved. Isotherm and kinetic studies were carried out in this study. Keeping all these parameters constant, the method was tried out on stimulated hospital effluent and removal efficiency of 85% was obtained against cephalexin.

Staphylococcus aureus is one of the major pathogen causing infections in human ranging from mild to severe life-threatening conditions. Methicillin-Resistant Staphylococcus aureus (MRSA) is an important nosocomial pathogen with high morbidity and mortality in both hospital and community settings. Total 600 nasal swabs were collected from patient visitors and Healthcare workers. Of these, 184 S.aureus (30.66%) were isolated. All S.aureus isolates screened for MRSA and 73 (39.67%) isolates showed MRSA by Cefoxitin disc diffusion method and PCR. 21 (28.76%) isolates detected pvl gene of the 73 isolated MRSA i.e., CA-MRSA. All MRSA isolates were typed into SCCmec element (I to V). Of these SCCmec type III was found more prevalent than other SCCmec types and 3 isolates were not typeable. MRSA still remains a significant problem in public Healthcare settings. Screening of MRSA among Healthcare Workers and patient visitors is mandatory to prevent the spread of CA-MRSA in hospitals.

One of the key factors that has helped the state of Tamil Nadu to make significant progress in the health sector, especially in maternal health, is an enabling political environment in the state that has prioritised programmes for the welfare of women and children, irrespective of the party in power. This article reviews 10 key innovations in maternal health and tribal health introduced in the state of Tamil Nadu from 2005–2006 to 2020–2021. The specific questions addressed are as follows: what are the special innovative schemes introduced by the state of Tamil Nadu to promote maternal health? Whether and to what extent utilisation of public delivery system for maternal services has increased over the past 15 years or so? The overall impact of these initiatives on the maternal health of the state is assessed by analysing two indicators: trends in maternal mortality ratio (MMR) and financial burden due to delivery in public and private facilities. MMR in the state of Tamil Nadu is steadily falling—from 111 in 2004–2006 to 60 in 2016–2018. While average out-of-pocket expenditure (OOPE) during delivery in the public sector has increased from ₹2,454 in 2014 to ₹3,465 in 2017–2018, in the private sector, it has increased from ₹32,182 in 2014 to ₹34,635 in 2017–2018. OOPE in private facilities is nearly ten times higher than OOPE in public facilities, in both rural and urban areas. While the overall status of maternal health has improved significantly in the state, there are wide variations within and across districts. However, significant improvements in the overall health status can be achieved only if such inequities are reduced systematically, and efforts are being made to reduce such inequities.

Background The emergence of resistance to commonly used antibiotics by human infections occurred mostly due to their overuse, that prompted individuals to pursue novel and innovative treatments. The phytochemical characteristics, antibacterial activity, and cytotoxicity of MCF7 cells were evaluated in two Pleurotus spp. mycelial extracts in this work. Methods Pleurotus djamor and Pleurotus florida mycelial extracts from pure cultures were tested for antibacterial activity by a well-diffusion assay and antimicrobial activity against mold fungi was evaluated for biomass inhibition. Mycelial extracts were obtained from dichloromethane extracts and their biophysical characteristics are analyzed by UV–vis spectrum and FTIR analysis. By spraying detection reagents onto TLC plates, the chemicals in dichloromethane extraction of chosen mushroom fungus mycelia were identified. Using the MTT test, the cytotoxic effect of dichloromethane extracts of selected mushroom fungi was evaluated on MCF7 Cell lines. Results Mycelial extracts of P. djamor and P. florida exhibited significant antimicrobial effect on the bacterial and fungal pathogens tested. Dichloromethane mycelial extracts were obtained using soxhlet extraction which response positive for various phytochemical analysis. Detection of metabolites in thin layer chromatography using spray reagents documented one of few first accounts on flavonoids, anthroquinone and terpenoid compounds in P. djamor and P. florida. P. djamor and P. florida had dose-dependent antiproliferative activity against MCF7 cells, with an inhibitory impact of 55.72% and 64.47% percent at 125 μg/mL, respectively. Conclusion The study has reported the identification with the potent biological activity of some of the key bioactive components present in DCM extracts from the mycelia of P. djamor and P. florida.

Generalization of neural networks is crucial for deploying them safely in the real world. Common training strategies to improve generalization involve the use of data augmentations, ensembling and model averaging. In this work, we first establish a surprisingly simple but strong benchmark for generalization which utilizes diverse augmentations within a training minibatch, and show that this can learn a more balanced distribution of features. Further, we propose Diversify-Aggregate-Repeat Training (DART) strategy that first trains diverse models using different augmentations (or domains) to explore the loss basin, and further Aggregates their weights to combine their expertise and obtain improved generalization. We find that Repeating the step of Aggregation throughout training improves the overall optimization trajectory and also ensures that the individual models have a sufficiently low loss barrier to obtain improved generalization on combining them. We shed light on our approach by casting it in the framework proposed by Shen et al. and theoretically show that it indeed generalizes better. In addition to improvements in In- Domain generalization, we demonstrate SOTA performance on the Domain Generalization benchmarks in the popular DomainBed framework as well. Our method is generic and can easily be integrated with several base training algorithms to achieve performance gains., CVPR 2023. First two authors contributed equally

In this paper, we propose to develop a method to address unsupervised domain adaptation (UDA) in a practical setting of continual learning (CL). The goal is to update the model on continually changing domains while preserving domain-specific knowledge to prevent catastrophic forgetting of past-seen domains. To this end, we build a framework for preserving domain-specific features utilizing the inherent model capacity via pruning. We also perform effective inference using a novel batch-norm based metric to predict the final model parameters to be used accurately. Our approach achieves not only state-of-the-art performance but also prevents catastrophic forgetting of past domains significantly. Our code is made publicly available., Comment: CVPR CLVision Workshop 2023, For code see https://github.com/PrasannaB29/PACDA

StyleGANs are at the forefront of controllable image generation as they produce a latent space that is semantically disentangled, making it suitable for image editing and manipulation. However, the performance of StyleGANs severely degrades when trained via class-conditioning on large-scale long-tailed datasets. We find that one reason for degradation is the collapse of latents for each class in the $\mathcal{W}$ latent space. With NoisyTwins, we first introduce an effective and inexpensive augmentation strategy for class embeddings, which then decorrelates the latents based on self-supervision in the $\mathcal{W}$ space. This decorrelation mitigates collapse, ensuring that our method preserves intra-class diversity with class-consistency in image generation. We show the effectiveness of our approach on large-scale real-world long-tailed datasets of ImageNet-LT and iNaturalist 2019, where our method outperforms other methods by $\sim 19\%$ on FID, establishing a new state-of-the-art., Comment: CVPR 2023. Project Page: https://rangwani-harsh.github.io/NoisyTwins/

We present a short overview of the TeV-Halos objects as a discovery and a relevant contribution of the High Altitude Water \v{C}erenkov (HAWC) observatory to TeV astrophysics. We discuss history, discovery, knowledge, and the next step through a new and more detailed analysis than the original study in 2017. TeV-Halos will contribute to resolving the problem of the local positron excess observed on the Earth. To clarify the latter, understanding the diffusion process is mandatory., Comment: Work presented in the 21st International Symposium on Very High Energy Cosmic Ray Interactions(ISVHECRI 2022) as part of the Ph. D. Thesis of Ramiro Torres-Escobedo (SJTU, Shanghai, China). Accepted for publication in SciPost Physics Proceedings (ISSN 2666-4003). 11 pages, 3 Figures. Short overview of HAWC and TeV Halos objects until 2022

Recent progress in generative models has resulted in models that produce both realistic as well as relevant images for most textual inputs. These models are being used to generate millions of images everyday, and hold the potential to drastically impact areas such as generative art, digital marketing and data augmentation. Given their outsized impact, it is important to ensure that the generated content reflects the artifacts and surroundings across the globe, rather than over-representing certain parts of the world. In this paper, we measure the geographical representativeness of common nouns (e.g., a house) generated through DALL.E 2 and Stable Diffusion models using a crowdsourced study comprising 540 participants across 27 countries. For deliberately underspecified inputs without country names, the generated images most reflect the surroundings of the United States followed by India, and the top generations rarely reflect surroundings from all other countries (average score less than 3 out of 5). Specifying the country names in the input increases the representativeness by 1.44 points on average for DALL.E 2 and 0.75 for Stable Diffusion, however, the overall scores for many countries still remain low, highlighting the need for future models to be more geographically inclusive. Lastly, we examine the feasibility of quantifying the geographical representativeness of generated images without conducting user studies., Comment: Preprint, 15 pages, 9 figures

Jackfruit (Artocarpus heterophyllus Lam.) is a popular tropical large fruit of the Moraceae family. The ripe bulbs are sweet delicious to eat besides a variety of preparations. The seeds are discarded as waste. The study was conducted with different heat processing methods like blanching (BL), roasting (RO) and pressure-cooking (PC) to know the flour yield on dry-milling, nutritional composition and its acceptable level. On milling maximum flour recovery of 93.30% (PC), 98.76% (RO) and 96% (BL) were observed. Among the minerals estimated (ICPMS) the potassium content of 9245.90 mg/kg, 7382.24mg/kg, 8232.31mg/kg in BL, RO and PC seed flours were reported. Magnesium content of 942.84 mg/kg, 775.32 mg/kg and 886.14mg/kg observed in BL, RO and PC flours, the highest sodium content of 243.69 mg/kg in PC, followed by 193.23 mg/kg and 46.44 mg/kg in RO and BL samples. Calcium content of 82.01 mg/kg, 60.48 mg/kg, 83.33 mg/kg was observed in BL, RO, PC flour samples were noticed. Iron (43.36 mg/100g BL, 20.10 mg/kg RO, 16.20 mg/kg PC), Aluminium (31.36 mg/kg BL, 16.69 mg/kg RO, 5.50 PC). Zinc (12.46 mg/kg BL, 10.18 mg/kg RO, 16.48 mg/kg PC), Manganese (4.80 mg/kg BL, 2.61 mg/kg RO, 3.81 mg/kg PC), Boron (6.25 mg/kg BL, 2.29 mg/kg RO, 3.46 mg/kg PC), Barium (2.37 mg/kg BL, 1.12 mg/kg RO, 1.88 mg/kg PC), Copper (4.86 mg/kg BL, 2.78mg/kg RO, 3.52mg/kg PC) etc., other elements in traces. The findings suggest that a maximum of 50% PC Jack seed flour was found accepted in the production of functional breadsticks having protein 9.78g/100g, carbohydrates 75.55g/100g and energy 405 K.cal/100g or as a functional food ingredient in different food products.

Due to its excellent physicochemical properties, cerium oxide (CeO2) has attracted much attention in recent years. CeO2 nanomaterials (nanoceria) are widely being used, which has resulted in them getting released to the environment, and exposure to humans (mostly via inhalation) is a major concern. In the present study, CeO2 nanoparticles were synthesized by hydroxide-mediated method and were further characterized by Scanning Electron Microscopy (SEM), Field Emission Scanning Electron Microscopy (FESEM), Energy Dispersive X-ray Spectroscopy (EDX), Fourier Transform Infrared Spectroscopy (FTIR), and X-ray Diffraction Spectroscopy (XRD). Human lung epithelial (Beas-2B) cells were used to assess the cytotoxicity and biocompatibility activity of CeO2 nanoparticles. 3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide (MTT) and Live/Dead assays were performed to determine the cytotoxicity and biocompatibility of CeO2 nanoparticles. Generation of reactive oxygen species (ROS) by cerium oxide nanoparticles was assessed by ROS assay. MTT assay and Live/Dead assays showed no significant induction of cell death even at higher concentrations (100 μg per 100 μL) upon exposure to Beas-2B cells. ROS assay revealed that CeO2 nanoparticles did not induce ROS that contribute to the oxidative stress and inflammation leading to various disease conditions. Thus, CeO2 nanoparticles could be used in various applications including biosensors, cancer therapy, catalytic converters, sunscreen, and drug delivery.

Real-world datasets exhibit imbalances of varying types and degrees. Several techniques based on re-weighting and margin adjustment of loss are often used to enhance the performance of neural networks, particularly on minority classes. In this work, we analyze the class-imbalanced learning problem by examining the loss landscape of neural networks trained with re-weighting and margin-based techniques. Specifically, we examine the spectral density of Hessian of class-wise loss, through which we observe that the network weights converge to a saddle point in the loss landscapes of minority classes. Following this observation, we also find that optimization methods designed to escape from saddle points can be effectively used to improve generalization on minority classes. We further theoretically and empirically demonstrate that Sharpness-Aware Minimization (SAM), a recent technique that encourages convergence to a flat minima, can be effectively used to escape saddle points for minority classes. Using SAM results in a 6.2\% increase in accuracy on the minority classes over the state-of-the-art Vector Scaling Loss, leading to an overall average increase of 4\% across imbalanced datasets. The code is available at: https://github.com/val-iisc/Saddle-LongTail., NeurIPS 2022. Code: https://github.com/val-iisc/Saddle-LongTail

Background: A proposed Lung Cancer Detection System (LCDS) lacks sensitivity and accuracy due to low spatial resolution in Positron Emission Tomography (PET) image and low contrast in Computed Tomography (CT) image. Method: Such an issue has been resolved by creating a modified co-learning learning technique which will be based on ten Convolutional Neural Network (CNN) (Alexnet, Vgg16, Vgg19, Squeezenet, Googlenet, Inceptionv3, Mobilenetv2, Densenet201, Resnet18, Xception) models. This technique encodes modality specific features and utilizes them to acquire a spatially varying fusion map. These fusion maps are multiplied using modality feature map for an utilization of image analysis. Result: By the use of modified co-learning technique, this system attained (Densenet201) 97.3% sensitivity, 98.2% accuracy and 1.6 false positives per scan in PET/CT. Conclusion: A proposed LCDS attained tremendously minimum false positive rate and it is a promising technique in support of cancerous recognition due to improved sensitivity and accuracy.

Developments in vacuum storage technology present an opportunity to achieve significant improvements on protection, preservation and storage of agricultural commodities for residential and commercial use. Sub-baric storage is a environmental friendly, non-residue organic technology which provides chemical-free and insect contamination-free products. Due to creation of vacuum, there is a change in the environment inside the storage structure. This study therefore contributes an important knowledge and method in the development, fabrication and application of a sub baric storage bin (SBSB) as a best alternative to the commonly used traditional and modern storage structure. In its embodiment, the work focuses on the design and fabrication of the sub-baric storage bin to provide efficient storage of food grains by preventing the use of pesticides and insecticides and to reduce material loss during storage, a sub-baric storage bin of 500 kg capacity was designed and developed. The developed storage bin consists of storage chamber (500 kg), Vacuum pump, suction blower, grain inlet with pipe for loading, grain outlet for unloading, vacuum gauge, thermocouple, control panel, agitator, air filter, two inlet valves for gas infusion, vacuum release valve and SS mobile skid. The designed sub-baric storage bin is cylindrical in geometry with conical shape at bottom side and flat circular plate on top side and the storage bin has capacity of 500 kg to store food grains with hopper angle of 60°. The storage bin was designed in such a way that, it has provision for both bulk and bag storage and to work from 0-650 mm Hg vacuum. The developed SBSB was subjected to hydraulic pressure test and vacuum drop test to ensure a safe operation. It was observed that there was no implosion (compression) or explosion confirming to the fact that the design was adequate and also safe to operate. Also, there were no signs of bulging, buckling or any deformations observed in any of the components or the pipe lines, connections, fixtures or fasteners. Hence, it was concluded that the designed equipment could be operated safely at 650 mm Hg vacuum pressure satisfying all the applicable safety assurances and standards relevant to the industry.

Universal Domain Adaptation (UniDA) deals with the problem of knowledge transfer between two datasets with domain-shift as well as category-shift. The goal is to categorize unlabeled target samples, either into one of the "known" categories or into a single "unknown" category. A major problem in UniDA is negative transfer, i.e. misalignment of "known" and "unknown" classes. To this end, we first uncover an intriguing tradeoff between negative-transfer-risk and domain-invariance exhibited at different layers of a deep network. It turns out we can strike a balance between these two metrics at a mid-level layer. Towards designing an effective framework based on this insight, we draw motivation from Bag-of-visual-Words (BoW). Word-prototypes in a BoW-like representation of a mid-level layer would represent lower-level visual primitives that are likely to be unaffected by the category-shift in the high-level features. We develop modifications that encourage learning of word-prototypes followed by word-histogram based classification. Following this, subsidiary prototype-space alignment (SPA) can be seen as a closed-set alignment problem, thereby avoiding negative transfer. We realize this with a novel word-histogram-related pretext task to enable closed-set SPA, operating in conjunction with goal task UniDA. We demonstrate the efficacy of our approach on top of existing UniDA techniques, yielding state-of-the-art performance across three standard UniDA and Open-Set DA object recognition benchmarks., NeurIPS 2022. Project page: https://sites.google.com/view/spa-unida

Blockchain has become important in business strategy and in technological discussions in industry, academia and government. Irrespective of differing views and beliefs about cryptocurrency, it has triggered significant interest and investment in research in the blockchain space. Enterprise-grade blockchains are being developed to meet specific business requirements. However, the limitations of scalability and interoperability remain design challenges. Technically, interoperability could address scalability by connecting multiple, application-specific blockchain networks. Interoperability is, therefore, emerging as one of the critical requirements of blockchain technology because of the need to address data and value silos. The general definition of interoperability, which stems from an information systems perspective, will not work for blockchain systems. The main challenges are accessing and accepting data from other networks. A preliminary study conducted by the author showed that these networks need to be integrated to interoperate. This research identifies three modes of integration with different security assumptions to address the data access issue. With the data acceptance issue, the value (asset) that each system carries needs to be categorised. Based on the category of asset, specific protocols need to be used to swap or transfer value. This paper proposes an asset classification and design decision framework for cross-chain integration that identifies the type of transfer and its security assumptions in any given scenario. In brief, this thesis proposes a novel protocol to transfer assets between blockchains while preserving the properties needed for value transfer. This protocol is built on the assumption that the integration process happens in multiple steps between mutually agreed parties, and the users initiate the trade. This protocol transfers assets from one network to another in a way that burns the asset on one network and creates proof that will be ported to another network through this integration system. The recipient’s system then mints the same asset on its network with the aid of the proof. The feasibility of the transfer process was tested by deploying the protocol in a test network and evaluating the outcome. A theoretical analysis of the transfer process proves the desired properties, including atomicity, correctness, and security, exist. Formal verification of the protocol in various adversarial conditions has been done. This research shows that atomicity, which is commonly believed to hold in the time-lock mechanism, does not hold in general cross-chain situations. This work verifies that this property only holds under certain assumptions. It also shows that the other two desired properties, correctness and security, hold in the proposed protocol.

Unconstrained Image generation with high realism is now possible using recent Generative Adversarial Networks (GANs). However, it is quite challenging to generate images with a given set of attributes. Recent methods use style-based GAN models to perform image editing by leveraging the semantic hierarchy present in the layers of the generator. We present Few-shot Latent-based Attribute Manipulation and Editing (FLAME), a simple yet effective framework to perform highly controlled image editing by latent space manipulation. Specifically, we estimate linear directions in the latent space (of a pre-trained StyleGAN) that controls semantic attributes in the generated image. In contrast to previous methods that either rely on large-scale attribute labeled datasets or attribute classifiers, FLAME uses minimal supervision of a few curated image pairs to estimate disentangled edit directions. FLAME can perform both individual and sequential edits with high precision on a diverse set of images while preserving identity. Further, we propose a novel task of Attribute Style Manipulation to generate diverse styles for attributes such as eyeglass and hair. We first encode a set of synthetic images of the same identity but having different attribute styles in the latent space to estimate an attribute style manifold. Sampling a new latent from this manifold will result in a new attribute style in the generated image. We propose a novel sampling method to sample latent from the manifold, enabling us to generate a diverse set of attribute styles beyond the styles present in the training set. FLAME can generate diverse attribute styles in a disentangled manner. We illustrate the superior performance of FLAME against previous image editing methods by extensive qualitative and quantitative comparisons. FLAME also generalizes well on multiple datasets such as cars and churches., Project page: https://sites.google.com/view/flamelatentediting

Introduction: Acute Lower Respiratory Tract Infection (ALRTI) is the most common cause of deathin children under five. Various demographic, socio-economic and environmental factors have beenassociated with ALRTI, with conflicting results. Children with congenital heart diseases are morevulnerable to lower respiratory tract infections following the fact that they had an anatomical defectthat causes hemodynamic disturbance of lung circulation and mucosal oedema finally leads todecreased lung compliance and recurrent lower respiratory tract infections. Methods: Children agedbetween1 month and 18 years with clinically established congenital heart disease confirmed byechocardiography and presenting with signs and symptoms suggestive of lower respiratory tractinfection were enrolled in the study. A detailed history was taken and routine blood investigationswere done in all cases. Chest X-Ray was done to confirm the clinical diagnosis. Results: Mean ageof the study subjects was 2 years. VSD was seen in 64.9% of cases while 10.5% of cases had VSDand ASD. The most common presenting symptoms were: cough (98.2%), reduced feeding (97.4%),fever (89.5%), running nose (67.5%) and wheeze (49.1%). Leucocytosis and raised ESR were seenin 66.7% and 64.9% cases respectively. In Throat swab Commensals were seen in 36% of caseswhile gram-positive cocci were seen in 3.5% cases respectively. In blood culture Streptococcuspneumoniae being the commonest organism isolated. Bilateral and unilateral opacities on chest x-ray were seen in 16.7% and 12.3% cases respectively. Conclusion: Bronchopneumonia was thecommonest LRTI in children with the predominance of VSD.

Introduction: Congenital heart disease (CHD) if diagnosed clinically early and confirmed by ChestX-ray and ECHO, can be appropriately managed. Objectives: To observe the clinical profile of CHDin clinically diagnosed CHD in children To correlate the clinical profile with radiological andechocardiographic findings in the study group. Methods: It’s a cross-sectional study. The durationwas 18 months. All children in the age group of 1 month to 18years of age with history & clinicalfindings suggestive of CHDs were taken, assessed, subjected to Chest X-ray & 2 DECHO & werecorrelate. Results: Total no. of cases admitted during the study period was 5423, 624 had CHDamong which 50 fulfilling the criteria were taken. Clinically CCHD was in 34%, ACHD in 66%. Clinicalcorrelation with X-ray was 83% and with ECHO was 87 %. Conclusion: ACHD is more common,Chest x-ray findings correlated closely. ECHO correlation with the clinical diagnosis was veryaccurate especially in isolated lesions and some cases were identified which had been missedclinically. Hence Clinical Diagnosis with X-ray and ECHO helps in increased identification & earlierintervention of CHDs.

Background: Inflammatory diseases are one of the major concerns of today’s world; major disorders caused by inflammation include, allergy, asthma, arthritis, hepatitis, autoimmune diseases, celiac disease, etc. During most of these events, many proteins and molecules expression are modulated and one such protein is AP-1 (c-Fos-c-Jun heterodimer complex). AP-1 is a dimeric protein activated by several physiological stimuli and environmental insults such as growth factors, polypeptide hormones, neurotransmitters, cytokines, cell-matrix interactions, UV irradiations, viral and bacterial infections. Objective: Present study is mainly focused on designing small molecule analogs to inhibit the c- Fos-c-Jun complex, as the complex is involved in many inflammatory diseases and precisely involved in disease progression. Therefore, it had been considered as a therapeutic target for more than a decade. Materials and Methods: In the present study, an attempt was made to design the analogs of referral drug T-5224. 31 analogs of T-5224 were designed by chemoinformatics approach and subjected to ADMETox for screening. Results: Among the 16 compounds that were found to pass the evaluation, all 16 compounds passed the toxicity evaluation except the 7th molecule. The molecular docking study showed that compounds 1, 2 and 16 had high inhibition constant. Conclusion: The preliminary results suggest the compounds 1, 2 and 16 have the potential ligand binding capacity with the cFos-cJun complex. Further analysis, with advanced tools, may result in potential small molecules to inhibit the c-Fos-c-Jun complex.

Field pea is the most commonly grown temperate pulse crop, with close to 15 million tons produced globally in 2020. Varieties improved through breeding are important to ensure ongoing improvements in yield and disease resistance. Genomic selection (GS) is a modern breeding approach that could substantially improve the rate of genetic gain for grain yield, and its deployment depends on the prediction accuracy (PA) that can be achieved. In our study, four yield trials representing breeding lines' advancement stages of the breeding program (S0, S1, S2, and S3) were assessed with grain yield, aerial high-throughput phenotyping (normalized difference vegetation index, NDVI), and bacterial blight disease scores (BBSC). Low-to-moderate broad-sense heritability (0.31–0.71) and narrow-sense heritability (0.13–0.71) were observed, as the estimated additive and non-additive genetic components for the three traits varied with the different models fitted. The genetic correlations among the three traits were high, particularly in the S0–S2 stages. NDVI and BBSC were combined to investigate the PA for grain yield by univariate and multivariate GS models, and multivariate models showed higher PA than univariate models in both cross-validation and forward prediction methods. A 6–50% improvement in PA was achieved when multivariate models were deployed. The highest PA was indicated in the forward prediction scenario when the training population consisted of early generation breeding stages with the multivariate models. Both NDVI and BBSC are commonly used traits that could be measured in the early growth stage; however, our study suggested that NDVI is a more useful trait to predict grain yield with high accuracy in the field pea breeding program, especially in diseased trials, through its incorporation into multivariate models.

Domain adversarial training has been ubiquitous for achieving invariant representations and is used widely for various domain adaptation tasks. In recent times, methods converging to smooth optima have shown improved generalization for supervised learning tasks like classification. In this work, we analyze the effect of smoothness enhancing formulations on domain adversarial training, the objective of which is a combination of task loss (eg. classification, regression, etc.) and adversarial terms. We find that converging to a smooth minima with respect to (w.r.t.) task loss stabilizes the adversarial training leading to better performance on target domain. In contrast to task loss, our analysis shows that converging to smooth minima w.r.t. adversarial loss leads to sub-optimal generalization on the target domain. Based on the analysis, we introduce the Smooth Domain Adversarial Training (SDAT) procedure, which effectively enhances the performance of existing domain adversarial methods for both classification and object detection tasks. Our analysis also provides insight into the extensive usage of SGD over Adam in the community for domain adversarial training., ICML 2022. Code: https://github.com/val-iisc/SDAT

The advances in monocular 3D human pose estimation are dominated by supervised techniques that require large-scale 2D/3D pose annotations. Such methods often behave erratically in the absence of any provision to discard unfamiliar out-of-distribution data. To this end, we cast the 3D human pose learning as an unsupervised domain adaptation problem. We introduce MRP-Net that constitutes a common deep network backbone with two output heads subscribing to two diverse configurations; a) model-free joint localization and b) model-based parametric regression. Such a design allows us to derive suitable measures to quantify prediction uncertainty at both pose and joint level granularity. While supervising only on labeled synthetic samples, the adaptation process aims to minimize the uncertainty for the unlabeled target images while maximizing the same for an extreme out-of-distribution dataset (backgrounds). Alongside synthetic-to-real 3D pose adaptation, the joint-uncertainties allow expanding the adaptation to work on in-the-wild images even in the presence of occlusion and truncation scenarios. We present a comprehensive evaluation of the proposed approach and demonstrate state-of-the-art performance on benchmark datasets., Comment: CVPR 2022. Project page: https://sites.google.com/view/mrp-net

The increasing demand of high speed data results into extensive enhancement on different telecommunication technologies through wireline and wireless technologies. Optical Fiber technology is being popular for fixed broadband technologies and for backhaul network data for network convergence and media device interaction. Fiber to the home (FTTH) is gaining momentum of deployments in many countries all around the world. Passive optical network (PON) utilizes point to multipoint (P2MP) topology and is becoming suitable, cost effective, and promising solutions as compared to existing copper based telecommunication infrastructure. PON architecture is cheaper than other architectures due to dynamic bandwidth allocation and common resources that can be used by different subscribers and especially for home subscribers. This paper presents a study on the effective deployment of PON based FTTH network at Nepal by referring the deployment scenario of Nepal Telecom (NT), while this network design, deployment, and implementation provides a lesson learn for cost effective deployment of such network to other stakeholders of developing countries having similar territory and implementation challenges.

Carboxyl esterase (E.C.No.3.1.1.1) was partially purified from Adenanthera pavonina (Saga) using ammonium sulfate fractionation (0-60%) and DEAE (diethyl aminoethyl) ion exchange chromatography, the purified enzyme was characterized. One major saga-esterase was identified with Fold purification of 29. Molecular weight of the Ap-esterase was determined using Sephadex G-25 gel filtration and SDS-PAGE (Sodium dodecyl sulfate polyacryamide gel electrophoresis) which was found to be 26.0 k Da. Optimal activity of the saga-esterase occurred when the pH 7.0 at a temperature of 55°C. The activation energy for the hydrolysis of α-naphthyl acetate was determined to be 1.10 kcal/ mol. The Michaelis Menton constant (Km) and Vmax of the saga-esterase was 0.4µmoles and 105 IU respectively. In addition, the isoelectric point is at pH > 9 and immuno-blot using polyclonal antibodies showed that the saga-esterase was widely distributed in seeds but not in leaves. The saga-esterase inhibited by organophosphate and carbamate pesticides, which can be substituted for acetylcholinesterase.