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The COVID-19 pandemic has been inflicted upon humanity by the SARS-CoV-2 virus, the latest insidious incarnation of the coronaviruses group. While in its wake intense scientific research has produced breakthrough vaccines and cures, there still exists an immediate need to further understand the origin, mechanobiology and biochemistry, and destiny of this virus so that future pandemics arising from similar coronaviruses may be contained more effectively. In this Perspective, we discuss the various evidential findings of virus propagation and connect them to respective underpinning cellular biomechanical states leading to corresponding manifestations of the viral activity. We further propose avenues to tackle the virus, including from a "musical" vantage point, and contain its relentless strides that are currently afflicting the global populace.

The air cushion vehicle, or "HOVERCRAFT," as it is commonly referred as, is the newest vehicle on the road today. This car is not just novel, but it is also distinct from other, more traditional vehicles. It is a terrestrial vehicle in that it does not require surface contact for traction and may move freely across a variety of surfaces while being supported constantly on a self-supporting platform. Hovercrafts are utilized in a variety of situations when persons and equipment must go by water while still being able to load and unload on land. They may even be used as battleships. Hovercrafts are thrilling to fly, and the sensation of easily transitioning from land to water and back is unrivalled. A hovercraft, also known as an air-cushion vehicle (ACV), is a ship that can move over any flat surface while being supported by a cushion of slow moving high- pressure air that is ejected downwards against the surface below and confined within a "SKIRT."

The air cushion vehicle, or "HOVERCRAFT," as it is commonly referred as, is the newest vehicle on the road today. This car is not just novel, but it is also distinct from other, more traditional vehicles. It is a terrestrial vehicle in that it does not require surface contact for traction and may move freely across a variety of surfaces while being supported constantly on a self-supporting platform. Hovercrafts are utilized in a variety of situations when persons and equipment must go by water while still being able to load and unload on land. They may even be used as battleships. Hovercrafts are thrilling to fly, and the sensation of easily transitioning from land to water and back is unrivalled. A hovercraft, also known as an air-cushion vehicle (ACV), is a ship that can move over any flat surface while being supported by a cushion of slow moving high- pressure air that is ejected downwards against the surface below and confined within a "SKIRT."

The air cushion vehicle, or "HOVERCRAFT," as it is commonly referred as, is the newest vehicle on the road today. This car is not just novel, but it is also distinct from other, more traditional vehicles. It is a terrestrial vehicle in that it does not require surface contact for traction and may move freely across a variety of surfaces while being supported constantly on a self-supporting platform. Hovercrafts are utilized in a variety of situations when persons and equipment must go by water while still being able to load and unload on land. They may even be used as battleships. Hovercrafts are thrilling to fly, and the sensation of easily transitioning from land to water and back is unrivalled. A hovercraft, also known as an air-cushion vehicle (ACV), is a ship that can move over any flat surface while being supported by a cushion of slow moving high- pressure air that is ejected downwards against the surface below and confined within a "SKIRT."

The air cushion vehicle, or "HOVERCRAFT," as it is commonly referred as, is the newest vehicle on the road today. This car is not just novel, but it is also distinct from other, more traditional vehicles. It is a terrestrial vehicle in that it does not require surface contact for traction and may move freely across a variety of surfaces while being supported constantly on a self-supporting platform. Hovercrafts are utilized in a variety of situations when persons and equipment must go by water while still being able to load and unload on land. They may even be used as battleships. Hovercrafts are thrilling to fly, and the sensation of easily transitioning from land to water and back is unrivalled. A hovercraft, also known as an air-cushion vehicle (ACV), is a ship that can move over any flat surface while being supported by a cushion of slow moving high- pressure air that is ejected downwards against the surface below and confined within a "SKIRT."

© 2020, The Author(s). Sustained proliferation is a significant driver of cancer progression. Cell-cycle advancement is coupled with cell size, but it remains unclear how multiple cells interact to control their volume in 3D clusters. In this study, we propose a mechano-osmotic model to investigate the evolution of volume dynamics within multicellular systems. Volume control depends on an interplay between multiple cellular constituents, including gap junctions, mechanosensitive ion channels, energy-consuming ion pumps, and the actomyosin cortex, that coordinate to manipulate cellular osmolarity. In connected cells, we show that mechanical loading leads to the emergence of osmotic pressure gradients between cells with consequent increases in cellular ion concentrations driving swelling. We identify how gap junctions can amplify spatial variations in cell volume within multicellular spheroids and, further, describe how the process depends on proliferation-induced solid stress. Our model may provide new insight into the role of gap junctions in breast cancer progression.

Deep learning (DL), an emerging area of investigation in the fields of machine learning and artificial intelligence, has markedly advanced over the past years. DL techniques are being applied to assist medical professionals and researchers in improving clinical diagnosis, disease prediction and drug discovery. It is expected that DL will help to provide actionable knowledge from a variety of 'big data', including metabolomics data. In this review, we discuss the applicability of DL to metabolomics, while presenting and discussing several examples from recent research. We emphasize the use of DL in tackling bottlenecks in metabolomics data acquisition, processing, metabolite identification, as well as in metabolic phenotyping and biomarker discovery. Finally, we discuss how DL is used in genome-scale metabolic modelling and in interpretation of metabolomics data. The DL-based approaches discussed here may assist computational biologists with the integration, prediction and drawing of statistical inference about biological outcomes, based on metabolomics data.

The air cushion vehicle, or "HOVERCRAFT," as it is commonly referred as, is the newest vehicle on the road today. This car is not just novel, but it is also distinct from other, more traditional vehicles. It is a terrestrial vehicle in that it does not require surface contact for traction and may move freely across a variety of surfaces while being supported constantly on a self-supporting platform. Hovercrafts are utilized in a variety of situations when persons and equipment must go by water while still being able to load and unload on land. They may even be used as battleships. Hovercrafts are thrilling to fly, and the sensation of easily transitioning from land to water and back is unrivalled. A hovercraft, also known as an air-cushion vehicle (ACV), is a ship that can move over any flat surface while being supported by a cushion of slow moving high- pressure air that is ejected downwards against the surface below and confined within a "SKIRT."

The study of ferroelectric domains is essential for understanding the orientation of electric dipoles. This orientation of electric dipole shows the defect and impurities formation in a ferroelectric single crystal. Domain study is important for studying formations of defects and movement of dipoles in Al-doped KNbO3 single crystal. The Al-doped KNbO3 ferroelectric Single-crystal prepared using flux methods; then it chemically etched using methyl alcohol and nitric acid as an etchant. The electric field of 50 V/cm, 60 V/cm, and 70 V/cm was applied to the doped crystal. The variations of domain structure after the applying electric field are observed using the Trinocular microscopy method

The study of ferroelectric domains is essential for understanding the orientation of electric dipoles. This orientation of electric dipole shows the defect and impurities formation in a ferroelectric single crystal. Domain study is important for studying formations of defects and movement of dipoles in Al-doped KNbO3 single crystal. The Al-doped KNbO3 ferroelectric Single-crystal prepared using flux methods; then it chemically etched using methyl alcohol and nitric acid as an etchant. The electric field of 50 V/cm, 60 V/cm, and 70 V/cm was applied to the doped crystal. The variations of domain structure after the applying electric field are observed using the Trinocular microscopy method

The discovery of intrinsic magnetic topological order in MnBi2Te4 has invigorated the search for materials with coexisting magnetic and topological phases. These multiorder quantum materials are expected to exhibit new topological phases that can be tuned with magnetic fields, but the search for such materials is stymied by difficulties in predicting magnetic structure and stability. Here, we compute more than 27,000 unique magnetic orderings for more than 3000 transition metal oxides in the Materials Project database to determine their magnetic ground states and estimate their effective exchange parameters and critical temperatures. We perform a high-throughput band topology analysis of centrosymmetric magnetic materials, calculate topological invariants, and identify 18 new candidate ferromagnetic topological semimetals, axion insulators, and antiferromagnetic topological insulators. To accelerate future efforts, machine learning classifiers are trained to predict both magnetic ground states and magnetic topological order without requiring first-principles calculations.

Altered microarchitecture of collagen type I is a hallmark of wound healing and cancer that is commonly attributed to myofibroblasts. However, it remains unknown which effect collagen microarchitecture has on myofibroblast differentiation. Here, we combined experimental and computational approaches to investigate the hypothesis that the microarchitecture of fibrillar collagen networks mechanically regulates myofibroblast differentiation of adipose stromal cells (ASCs) independent of bulk stiffness. Collagen gels with controlled fiber thickness and pore size were microfabricated by adjusting the gelation temperature while keeping their concentration constant. Rheological characterization and simulation data indicated that networks with thicker fibers and larger pores exhibited increased strain-stiffening relative to networks with thinner fibers and smaller pores. Accordingly, ASCs cultured in scaffolds with thicker fibers were more contractile, expressed myofibroblast markers, and deposited more extended fibronectin fibers. Consistent with elevated myofibroblast differentiation, ASCs in scaffolds with thicker fibers exhibited a more proangiogenic phenotype that promoted endothelial sprouting in a contractility-dependent manner. Our findings suggest that changes of collagen microarchitecture regulate myofibroblast differentiation and fibrosis independent of collagen quantity and bulk stiffness by locally modulating cellular mechanosignaling. These findings have implications for regenerative medicine and anticancer treatments.

The discovery of intrinsic magnetic topological order in MnBi2Te4 has invigorated the search for materials with coexisting magnetic and topological phases. These multiorder quantum materials are expected to exhibit new topological phases that can be tuned with magnetic fields, but the search for such materials is stymied by difficulties in predicting magnetic structure and stability. Here, we compute more than 27,000 unique magnetic orderings for more than 3000 transition metal oxides in the Materials Project database to determine their magnetic ground states and estimate their effective exchange parameters and critical temperatures. We perform a high-throughput band topology analysis of centrosymmetric magnetic materials, calculate topological invariants, and identify 18 new candidate ferromagnetic topological semimetals, axion insulators, and antiferromagnetic topological insulators. To accelerate future efforts, machine learning classifiers are trained to predict both magnetic ground states and magnetic topological order without requiring first-principles calculations.

Altered microarchitecture of collagen type I is a hallmark of wound healing and cancer that is commonly attributed to myofibroblasts. However, it remains unknown which effect collagen microarchitecture has on myofibroblast differentiation. Here, we combined experimental and computational approaches to investigate the hypothesis that the microarchitecture of fibrillar collagen networks mechanically regulates myofibroblast differentiation of adipose stromal cells (ASCs) independent of bulk stiffness. Collagen gels with controlled fiber thickness and pore size were microfabricated by adjusting the gelation temperature while keeping their concentration constant. Rheological characterization and simulation data indicated that networks with thicker fibers and larger pores exhibited increased strain-stiffening relative to networks with thinner fibers and smaller pores. Accordingly, ASCs cultured in scaffolds with thicker fibers were more contractile, expressed myofibroblast markers, and deposited more extended fibronectin fibers. Consistent with elevated myofibroblast differentiation, ASCs in scaffolds with thicker fibers exhibited a more proangiogenic phenotype that promoted endothelial sprouting in a contractility-dependent manner. Our findings suggest that changes of collagen microarchitecture regulate myofibroblast differentiation and fibrosis independent of collagen quantity and bulk stiffness by locally modulating cellular mechanosignaling. These findings have implications for regenerative medicine and anticancer treatments.

The discovery of intrinsic magnetic topological order in MnBi2Te4 has invigorated the search for materials with coexisting magnetic and topological phases. These multiorder quantum materials are expected to exhibit new topological phases that can be tuned with magnetic fields, but the search for such materials is stymied by difficulties in predicting magnetic structure and stability. Here, we compute more than 27,000 unique magnetic orderings for more than 3000 transition metal oxides in the Materials Project database to determine their magnetic ground states and estimate their effective exchange parameters and critical temperatures. We perform a high-throughput band topology analysis of centrosymmetric magnetic materials, calculate topological invariants, and identify 18 new candidate ferromagnetic topological semimetals, axion insulators, and antiferromagnetic topological insulators. To accelerate future efforts, machine learning classifiers are trained to predict both magnetic ground states and magnetic topological order without requiring first-principles calculations.

Now-a-days development of chatbot using different methods become trendier, till now many conversational chatbots were designed for the replacement of traditional chatbots. A chatbot is a software that is capable of communicating and performing actions like the way human do. Chatbot will give response for the query given by the user and it also capable of executing tasks. Chatbots developed in olden days are so difficult to perform task but chatbot developed in recent years are good in performance and its development also become easier because of wide availability of development platforms and wide availability of source code. There are many methods to develop chatbot, it can be developed using either natural language processing (NLP) or deep learning. In this project we developed a chatbot using machine learning which helps to give information about our college.

Deep learning models have shown impressive performance across a spectrum of computer vision applications including medical diagnosis and autonomous driving. One of the major concerns that these models face is their susceptibility to adversarial attacks. Realizing the importance of this issue, more researchers are working towards developing robust models that are less affected by adversarial attacks. Adversarial training method shows promising results in this direction. In adversarial training regime, models are trained with mini-batches augmented with adversarial samples. Fast and simple methods (e.g., single-step gradient ascent) are used for generating adversarial samples, in order to reduce computational complexity. It is shown that models trained using single-step adversarial training method (adversarial samples are generated using non-iterative method) are pseudo robust. Further, this pseudo robustness of models is attributed to the gradient masking effect. However, existing works fail to explain when and why gradient masking effect occurs during single-step adversarial training. In this work, (i) we show that models trained using single-step adversarial training method learn to prevent the generation of single-step adversaries, and this is due to over-fitting of the model during the initial stages of training, and (ii) to mitigate this effect, we propose a single-step adversarial training method with dropout scheduling. Unlike models trained using existing single-step adversarial training methods, models trained using the proposed single-step adversarial training method are robust against both single-step and multi-step adversarial attacks, and the performance is on par with models trained using computationally expensive multi-step adversarial training methods, in white-box and black-box settings., Comment: CVPR 2020

As humans, we inherently perceive images based on their predominant features, and ignore noise embedded within lower bit planes. On the contrary, Deep Neural Networks are known to confidently misclassify images corrupted with meticulously crafted perturbations that are nearly imperceptible to the human eye. In this work, we attempt to address this problem by training networks to form coarse impressions based on the information in higher bit planes, and use the lower bit planes only to refine their prediction. We demonstrate that, by imposing consistency on the representations learned across differently quantized images, the adversarial robustness of networks improves significantly when compared to a normally trained model. Present state-of-the-art defenses against adversarial attacks require the networks to be explicitly trained using adversarial samples that are computationally expensive to generate. While such methods that use adversarial training continue to achieve the best results, this work paves the way towards achieving robustness without having to explicitly train on adversarial samples. The proposed approach is therefore faster, and also closer to the natural learning process in humans., Comment: CVPR 2020

The progress in the last decade has enabled machine learning models to achieve impressive performance across a wide range of tasks in Computer Vision. However, a plethora of works have demonstrated the susceptibility of these models to adversarial samples. Adversarial training procedure has been proposed to defend against such adversarial attacks. Adversarial training methods augment mini-batches with adversarial samples, and typically single-step (non-iterative) methods are used for generating these adversarial samples. However, models trained using single-step adversarial training converge to degenerative minima where the model merely appears to be robust. The pseudo robustness of these models is due to the gradient masking effect. Although multi-step adversarial training helps to learn robust models, they are hard to scale due to the use of iterative methods for generating adversarial samples. To address these issues, we propose three different types of regularizers that help to learn robust models using single-step adversarial training methods. The proposed regularizers mitigate the effect of gradient masking by harnessing on properties that differentiate a robust model from that of a pseudo robust model. Performance of models trained using the proposed regularizers is on par with models trained using computationally expensive multi-step adversarial training methods.

Now-a-days development of chatbot using different methods become trendier, till now many conversational chatbots were designed for the replacement of traditional chatbots. A chatbot is a software that is capable of communicating and performing actions like the way human do. Chatbot will give response for the query given by the user and it also capable of executing tasks. Chatbots developed in olden days are so difficult to perform task but chatbot developed in recent years are good in performance and its development also become easier because of wide availability of development platforms and wide availability of source code. There are many methods to develop chatbot, it can be developed using either natural language processing (NLP) or deep learning. In this project we developed a chatbot using machine learning which helps to give information about our college.

The simulation of the physical movement of multi-body systems at an atomistic level, with forces calculated from a quantum mechanical description of the electrons, motivates a graph partitioning problem studied in this article. Several advanced algorithms relying on evaluations of matrix polynomials have been published in the literature for such simulations. We aim to use a special type of graph partitioning in order to efficiently parallelize these computations. For this, we create a graph representing the zero-nonzero structure of a thresholded density matrix, and partition that graph into several components. Each separate submatrix (corresponding to each subgraph) is then substituted into the matrix polynomial, and the result for the full matrix polynomial is reassembled at the end from the individual polynomials. This paper starts by introducing a rigorous definition as well as a mathematical justification of this partitioning problem. We assess the performance of several methods to compute graph partitions with respect to both the quality of the partitioning and their runtime.

The simulation of the physical movement of multi-body systems at an atomistic level, with forces calculated from a quantum mechanical description of the electrons, motivates a graph partitioning problem studied in this article. Several advanced algorithms relying on evaluations of matrix polynomials have been published in the literature for such simulations. We aim to use a special type of graph partitioning in order to efficiently parallelize these computations. For this, we create a graph representing the zero-nonzero structure of a thresholded density matrix, and partition that graph into several components. Each separate submatrix (corresponding to each subgraph) is then substituted into the matrix polynomial, and the result for the full matrix polynomial is reassembled at the end from the individual polynomials. This paper starts by introducing a rigorous definition as well as a mathematical justification of this partitioning problem. We assess the performance of several methods to compute graph partitions with respect to both the quality of the partitioning and their runtime.

Though Deep Neural Networks (DNN) show excellent performance across various computer vision tasks, several works show their vulnerability to adversarial samples, i.e., image samples with imperceptible noise engineered to manipulate the network's prediction. Adversarial sample generation methods range from simple to complex optimization techniques. Majority of these methods generate adversaries through optimization objectives that are tied to the pre-softmax or softmax output of the network. In this work we, (i) show the drawbacks of such attacks, (ii) propose two new evaluation metrics: Old Label New Rank (OLNR) and New Label Old Rank (NLOR) in order to quantify the extent of damage made by an attack, and (iii) propose a new adversarial attack FDA: Feature Disruptive Attack, to address the drawbacks of existing attacks. FDA works by generating image perturbation that disrupt features at each layer of the network and causes deep-features to be highly corrupt. This allows FDA adversaries to severely reduce the performance of deep networks. We experimentally validate that FDA generates stronger adversaries than other state-of-the-art methods for image classification, even in the presence of various defense measures. More importantly, we show that FDA disrupts feature-representation based tasks even without access to the task-specific network or methodology. Code available at: https://github.com/BardOfCodes/fda, Comment: Accepted in ICCV;19. Code Available at https://github.com/BardOfCodes/fda

In a sequence of recent results (PODC 2015 and PODC 2016), the running time of the fastest algorithm for the minimum spanning tree (MST) problem in the Congested Clique model was first improved to O(log(log(log(n)))) from O(log(log(n))) (Hegeman et al., PODC 2015) and then to O(log^*(n)) (Ghaffari and Parter, PODC 2016). All of these algorithms use Theta(n^2) messages independent of the number of edges in the input graph. This paper positively answers a question raised in Hegeman et al., and presents the first "super-fast" MST algorithm with o(m) message complexity for input graphs with m edges. Specifically, we present an algorithm running in O(log^*(n)) rounds, with message complexity ~O(sqrt{m * n}) and then build on this algorithm to derive a family of algorithms, containing for any epsilon, 0 < epsilon <= 1, an algorithm running in O(log^*(n)/epsilon) rounds, using ~O(n^{1 + epsilon}/epsilon) messages. Setting epsilon = log(log(n))/log(n) leads to the first sub-logarithmic round Congested Clique MST algorithm that uses only ~O(n) messages. Our primary tools in achieving these results are (i) a component-wise bound on the number of candidates for MST edges, extending the sampling lemma of Karger, Klein, and Tarjan (Karger, Klein, and Tarjan, JACM 1995) and (ii) Theta(log(n))-wise-independent linear graph sketches (Cormode and Firmani, Dist. Par. Databases, 2014) for generating MST candidate edges.

Contractile cells can reorganize fibrous extracellular matrices and form dense tracts of fibers between neighboring cells. These tracts guide the development of tubular tissue structures and provide paths for the invasion of cancer cells. Here, we studied the mechanisms of the mechanical plasticity of collagen tracts formed by contractile premalignant acinar cells and fibroblasts. Using fluorescence microscopy and second harmonic generation, we quantified the collagen densification, fiber alignment, and strains that remain within the tracts after cellular forces are abolished. We explained these observations using a theoretical fiber network model that accounts for the stretch-dependent formation of weak cross-links between nearby fibers. We tested the predictions of our model using shear rheology experiments. Both our model and rheological experiments demonstrated that increasing collagen concentration leads to substantial increases in plasticity. We also considered the effect of permanent elongation of fibers on network plasticity and derived a phase diagram that classifies the dominant mechanisms of plasticity based on the rate and magnitude of deformation and the mechanical properties of individual fibers. Plasticity is caused by the formation of new cross-links if moderate strains are applied at small rates or due to permanent fiber elongation if large strains are applied over short periods. Finally, we developed a coarse-grained model for plastic deformation of collagen networks that can be employed to simulate multicellular interactions in processes such as morphogenesis, cancer invasion, and fibrosis.

Adversarial samples are perturbed inputs crafted to mislead the machine learning systems. A training mechanism, called adversarial training, which presents adversarial samples along with clean samples has been introduced to learn robust models. In order to scale adversarial training for large datasets, these perturbations can only be crafted using fast and simple methods (e.g., gradient ascent). However, it is shown that adversarial training converges to a degenerate minimum, where the model appears to be robust by generating weaker adversaries. As a result, the models are vulnerable to simple black-box attacks. In this paper we, (i) demonstrate the shortcomings of existing evaluation policy, (ii) introduce novel variants of white-box and black-box attacks, dubbed gray-box adversarial attacks" based on which we propose novel evaluation method to assess the robustness of the learned models, and (iii) propose a novel variant of adversarial training, named Graybox Adversarial Training" that uses intermediate versions of the models to seed the adversaries. Experimental evaluation demonstrates that the models trained using our method exhibit better robustness compared to both undefended and adversarially trained model, Comment: Accepted to ECCV 2018

In a sequence of recent results (PODC 2015 and PODC 2016), the running time of the fastest algorithm for the minimum spanning tree (MST) problem in the Congested Clique model was first improved to O(log(log(log(n)))) from O(log(log(n))) (Hegeman et al., PODC 2015) and then to O(log^*(n)) (Ghaffari and Parter, PODC 2016). All of these algorithms use Theta(n^2) messages independent of the number of edges in the input graph. This paper positively answers a question raised in Hegeman et al., and presents the first "super-fast" MST algorithm with o(m) message complexity for input graphs with m edges. Specifically, we present an algorithm running in O(log^*(n)) rounds, with message complexity ~O(sqrt{m * n}) and then build on this algorithm to derive a family of algorithms, containing for any epsilon, 0 < epsilon <= 1, an algorithm running in O(log^*(n)/epsilon) rounds, using ~O(n^{1 + epsilon}/epsilon) messages. Setting epsilon = log(log(n))/log(n) leads to the first sub-logarithmic round Congested Clique MST algorithm that uses only ~O(n) messages. Our primary tools in achieving these results are (i) a component-wise bound on the number of candidates for MST edges, extending the sampling lemma of Karger, Klein, and Tarjan (Karger, Klein, and Tarjan, JACM 1995) and (ii) Theta(log(n))-wise-independent linear graph sketches (Cormode and Firmani, Dist. Par. Databases, 2014) for generating MST candidate edges.

We study a graph partitioning problem motivated by the simulation of the physical movement of multi-body systems on an atomistic level, where the forces are calculated from a quantum mechanical description of the electrons. Several advanced algorithms have been published in the literature for such simulations that are based on evaluations of matrix polynomials. We aim at efficiently parallelizing these computations by using a special type of graph partitioning. For this, we represent the zero-nonzero structure of a thresholded matrix as a graph and partition that graph into several components. The matrix polynomial is then evaluated for each separate submatrix corresponding to the subgraphs and the evaluated submatrix polynomials are used to assemble the final result for the full matrix polynomial. The paper provides a rigorous definition as well as a mathematical justification of this partitioning problem. We use several algorithms to compute graph partitions and experimentally evaluate their performance with respect to the quality of the partition obtained with each method and the time needed to produce it.

We study a graph partitioning problem motivated by the simulation of the physical movement of multi-body systems on an atomistic level, where the forces are calculated from a quantum mechanical description of the electrons. Several advanced algorithms have been published in the literature for such simulations that are based on evaluations of matrix polynomials. We aim at efficiently parallelizing these computations by using a special type of graph partitioning. For this, we represent the zero-nonzero structure of a thresholded matrix as a graph and partition that graph into several components. The matrix polynomial is then evaluated for each separate submatrix corresponding to the subgraphs and the evaluated submatrix polynomials are used to assemble the final result for the full matrix polynomial. The paper provides a rigorous definition as well as a mathematical justification of this partitioning problem. We use several algorithms to compute graph partitions and experimentally evaluate their performance with respect to the quality of the partition obtained with each method and the time needed to produce it.

In a sequence of recent results (PODC 2015 and PODC 2016), the running time of the fastest algorithm for the \emph{minimum spanning tree (MST)} problem in the \emph{Congested Clique} model was first improved to $O(\log \log \log n)$ from $O(\log \log n)$ (Hegeman et al., PODC 2015) and then to $O(\log^* n)$ (Ghaffari and Parter, PODC 2016). All of these algorithms use $\Theta(n^2)$ messages independent of the number of edges in the input graph. This paper positively answers a question raised in Hegeman et al., and presents the first "super-fast" MST algorithm with $o(m)$ message complexity for input graphs with $m$ edges. Specifically, we present an algorithm running in $O(\log^* n)$ rounds, with message complexity $\tilde{O}(\sqrt{m \cdot n})$ and then build on this algorithm to derive a family of algorithms, containing for any $\varepsilon$, $0 < \varepsilon \le 1$, an algorithm running in $O(\log^* n/\varepsilon)$ rounds, using $\tilde{O}(n^{1 + \varepsilon}/\varepsilon)$ messages. Setting $\varepsilon = \log\log n/\log n$ leads to the first sub-logarithmic round Congested Clique MST algorithm that uses only $\tilde{O}(n)$ messages. Our primary tools in achieving these results are (i) a component-wise bound on the number of candidates for MST edges, extending the sampling lemma of Karger, Klein, and Tarjan (Karger, Klein, and Tarjan, JACM 1995) and (ii) $\Theta(\log n)$-wise-independent linear graph sketches (Cormode and Firmani, Dist.~Par.~Databases, 2014) for generating MST candidate edges., Comment: Full version of FST-TCS 2016 paper with the same title

Machaon M Bonafede,1 Vivek B Kalra,2 Jeffrey D Miller,1 Laurie L Fajardo3 1Truven Health Analytics, Cambridge, MA, 2Yale University School of Medicine, New Haven, CT, 3Department of Radiology, University of Iowa College of Medicine, Iowa City, IA, USA Purpose: The objective of this study was to conduct a value analysis of digital breast tomosynthesis (DBT) for breast cancer screening among women enrolled in US commercial health insurance plans to assess the potential budget impact associated with the clinical benefits of DBT. Methods: An economic model was developed to estimate the system-wide financial impact of DBT as a breast cancer screening modality within a hypothetical US managed care plan with one million members. Two scenarios were considered for women in the health plan who undergo annual screening mammography, ie, full field digital mammography (FFDM) and combined FFDM + DBT. The model focused on two main drivers of DBT value, ie, the capacity for DBT to reduce the number of women recalled for additional follow-up imaging and diagnostic services and the capacity of DBT to facilitate earlier diagnosis of cancer at less invasive stages where treatment costs are lower. Model inputs were derived from published sources and from analyses of the Truven Health MarketScan® Research Databases (2010–2012). Comparative clinical and economic outcomes were simulated for one year following screening and compared on an incremental basis. Results: Base-case analysis results show that 4,523 women in the hypothetical million member health plan who are screened using DBT avoid the use of follow-up services. The overall benefit of DBT was calculated at $78.53 per woman screened. Adjusting for a hypothetical $50 incremental cost of the DBT examination, this translates to $28.53 savings per woman screened, or $0.20 savings per member per month across the plan population and an overall cost savings to the plan of $2.4 million per year. Conclusion: The results o

This paper considers the \textit{minimum spanning tree (MST)} problem in the Congested Clique model and presents an algorithm that runs in $O(\log \log \log n)$ rounds, with high probability. Prior to this, the fastest MST algorithm in this model was a deterministic algorithm due to Lotker et al.~(SIAM J on Comp, 2005) from about a decade ago. A key step along the way to designing this MST algorithm is a \textit{connectivity verification} algorithm that not only runs in $O(\log \log \log n)$ rounds with high probability, but also has low message complexity. This allows the fast computation of an MST by running multiple instances of the connectivity verification algorithm in parallel. These results depend on a new edge-sampling theorem, developed in the paper, that says that if each edge $e = \{u, v\}$ is sampled independently with probability $c \log^2 n/\min\{\mbox{degree}(u), \mbox{degree}(v)\}$ (for a large enough constant $c$) then all cuts of size at least $n$ are approximated in the sampled graph. This sampling theorem is inspired by series of papers on graph sparsification via random edge sampling due to Karger~(STOC 1994), Bencz\'ur and Karger~(STOC 1996, arxiv 2002), and Fung et al.~(STOC 2011). The edge sampling techniques in these papers use probabilities that are functions of edge-connectivity or a related measure called edge-strength. For the purposes of this paper, these edge-connectivity measures seem too costly to compute and the main technical contribution of this paper is to show that degree-based edge-sampling suffices to approximate large cuts.

This paper presents constant-time and near-constant-time distributed algorithms for a variety of problems in the congested clique model. We show how to compute a 3-ruling set in expected $O(\log \log \log n)$ rounds and using this, we obtain a constant-approximation to metric facility location, also in expected $O(\log \log \log n)$ rounds. In addition, assuming an input metric space of constant doubling dimension, we obtain constant-round algorithms to compute constant-factor approximations to the minimum spanning tree and the metric facility location problems. These results significantly improve on the running time of the fastest known algorithms for these problems in the congested clique setting., Comment: Full version of DISC 2014 paper. Updated Sep 2018 to reflect the fact that using the Ghaffari et al. congested clique MIS algorithm from PODC 2018, it is possible to compute a 2-ruling set in the congested clique in O(logloglog n) rounds with high probability

The objective of the present study was to develop matrix tablets of salbutmol sulphate sustained release dosage form, for the treatment of Chronic Obstructive Pulmonary Disease (COPD). Compatibility study was performed through Fourier Transformer Infrared spectroscopy revealed that there no interaction between drug and polymers. Matrix tablets were prepared by wet granulation method using different concentration of Hydroxypropylmethylcellulose K100M (HPMC K100M), HPMC K15M and Ethyl Cellulose (EC). Prepared formulations were subjected to Pre-compression parameters like angle of repose, bulk and tapped density, Hausner’s ratio and car’s index and post-compression parameters like hardness, friability, thickness, % drug content, weight variation, swelling index. All the formulations resulted in acceptable limits. Tablets were subjected to In-Vitro drug release in 0.1 N HCl (pH 1.2) for first 2 hours followed by phosphate buffer (pH 6.8) for remaining 10 hours. In-vitro drug release data were fitting to zero order and Higuchi equation indicated that diffusion along with erosion could be the mechanism of drug release. It was observed that formulation F2 containing HPMC K100M exhibited the best release profile and able to sustain the drug release for prolong period of time. Swelling study suggested that when the matrix tablets come in contact with the dissolution medium, they take up water and swells, forming a gel layer around the matrix and simultaneously erosion also takes place. KEYWORDS: Matrix tablet, salbutmol sulphate, Hydroxypropylmethylcellulose K100M, Hydroxypropylmethylcellulose K15M and Ethyl Cellulose.

The objective of the present study was to develop matrix tablets of salbutmol sulphate sustained release dosage form, for the treatment of Chronic Obstructive Pulmonary Disease (COPD). Compatibility study was performed through Fourier Transformer Infrared spectroscopy revealed that there no interaction between drug and polymers. Matrix tablets were prepared by wet granulation method using different concentration of Hydroxypropylmethylcellulose K100M (HPMC K100M), HPMC K15M and Ethyl Cellulose (EC). Prepared formulations were subjected to Pre-compression parameters like angle of repose, bulk and tapped density, Hausner’s ratio and car’s index and post-compression parameters like hardness, friability, thickness, % drug content, weight variation, swelling index. All the formulations resulted in acceptable limits. Tablets were subjected to In-Vitro drug release in 0.1 N HCl (pH 1.2) for first 2 hours followed by phosphate buffer (pH 6.8) for remaining 10 hours. In-vitro drug release data were fitting to zero order and Higuchi equation indicated that diffusion along with erosion could be the mechanism of drug release. It was observed that formulation F2 containing HPMC K100M exhibited the best release profile and able to sustain the drug release for prolong period of time. Swelling study suggested that when the matrix tablets come in contact with the dissolution medium, they take up water and swells, forming a gel layer around the matrix and simultaneously erosion also takes place. KEYWORDS: Matrix tablet, salbutmol sulphate, Hydroxypropylmethylcellulose K100M, Hydroxypropylmethylcellulose K15M and Ethyl Cellulose.

Vivek B Wani,1,2 Khalid Al Sabti,1,3 Niranjan Kumar,1 Seemant Raizada,1 Jamal Al Kandari,1 Mohammad Al Harbi,4 Rima Sawaan,5 Usha Rajaram,6 Niran Al-Naqeeb,7 Mumtaz Shukkur81Department of Ophthalmology, Al Bahar Ophthalmology Center, Kuwait city, Kuwait; 2Department of Ophthalmology, Al Adan Hospital, Al Adan, Kuwait; 3Department of Surgery, Faculty of Medicine, Jabriya, Kuwait; 4Department of Neonatology, Al Sabah Maternity Hospital, Kuwait City, Kuwait; 5Department of Neonatology, Al Farwaniya Hospital, Al Farwaniya, Kuwait; 6Department of Neonatology, Al Jahra Hospital, Al Jahra, Kuwait; 7Department of Neonatology, Al Adan Hospital, Al Adan, Kuwait; 8Department of Community Medicine, Faculty of Medicine, Jabriya, KuwaitPurpose: The purpose was to report the structural, visual, and refractive outcomes of infants treated for retinopathy of prematurity (ROP) with laser and to identify the risk factors for unfavorable outcomes.Materials and methods: The charts of infants with severe ROP treated by diode laser in a tertiary center during the period April 1999 to November 2003 were reviewed. Treated infants were followed up for fundus examination, visual acuity assessment, and cycloplegic refraction. Data regarding ocular risk factors, like zones of ROP and the extent of extraretinal proliferations, and data regarding various systemic risk factors were collected. A minimum follow up of 6 months was needed for inclusion in the study of structural outcome. A minimum follow up of 24 months was needed for the study of visual and refractive outcomes. The outcomes measured were: rate of unfavorable structural outcome, unfavorable visual outcome (visual acuity < 20/40), and high myopia (myopia ≥ 5 diopters). The ocular and systemic risk factors were studied for their significance in the development of unfavorable outcomes.Results: Two hundred seventy eyes of 148 infants were treated for severe ROP, out of which 20 eyes (7.4%) had unfavorable structural outcome.

Maize is the dominant staple crop in most regions of Africa. The nutritional value of maize protein, however, is deficient in the essential amino acids such as lysine and tryptophan. This study was conducted to (1) investigate the level of variability among elite QPM inbred lines, (2) analyze combining ability of the inbred lines and G x E interaction of the resulting hybrids under stress and optimal conditions, (3) study the associations of parental genetic distances with F1 performance, heterosis and SCA of the hybrids under stress and optimal environments, and (4) investigate the effects of low N stress on endosperm modification, protein quantity and quality of QPM and identify best donor parents. The genetic variability of 35 maize inbred lines (32 QPM and three normal maize line checks) was studied using morpho-agronomic traits and SSR markers. Both methods indicated genetic variability among the lines. UPGMA cluster analysis based on morphoagronomic data grouped the inbred lines into four clusters and two outliers mainly based on grain yield, days to anthesis, plant height and leaf area. SSR markers grouped the inbred lines into six clusters which were different from the morpho-agronomic clustering. SSR markers grouped the inbred lines more efficiently in accordance with pedigree relationships. A diallel analysis of 15 QPM inbred lines showed significant GCA and SCA mean squares for most traits under optimal environments. GCA effects were significant while SCA effects were not significant for most traits across sites with drought and low N stress. Additive genetic effects were important under stress, and both additive and non-additive genetic effects were important under optimal conditions. Inbred lines VL054178, VL05482, VL05561, VL05483, CML511, CML159, CML491 and VL06375, which had good GCA effects for most traits under stress and non-stress conditions can potentially be used in QPM breeding programs in Africa and similar environments worldwide. Mean MPH ra, International Maize and Wheat Improvement Centre (CIMMYT), Ethiopian Institute of Agricultural Research (EIAR)

Commonly used ferroelectric perovskites are also wide-band-gap semiconductors. In such materials, the polarization and the space-charge distribution are intimately coupled, and this Letter studies them simultaneously with no a priori ansatz on either. In particular, we study the structure of domain walls and the depletion layers that form at the metal-ferroelectric interfaces. We find the coupling between polarization and space charges leads to the formation of charge double layers at the 90° domain walls, which, like the depletion layers, are also decorated by defects like oxygen vacancies. In contrast, the 180° domain walls do not interact with the defects or space charges. Implications of these results to domain switching and fatigue in ferroelectric devices are discussed.

Commonly used ferroelectric perovskites are also wide-band-gap semiconductors. In such materials, the polarization and the space-charge distribution are intimately coupled, and this Letter studies them simultaneously with no a priori ansatz on either. In particular, we study the structure of domain walls and the depletion layers that form at the metal-ferroelectric interfaces. We find the coupling between polarization and space charges leads to the formation of charge double layers at the 90° domain walls, which, like the depletion layers, are also decorated by defects like oxygen vacancies. In contrast, the 180° domain walls do not interact with the defects or space charges. Implications of these results to domain switching and fatigue in ferroelectric devices are discussed.