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Showing papers by "Rutgers University published in 2018"


Journal ArticleDOI
27 Apr 2018
TL;DR: This report provides updated ASD prevalence estimates for children aged 8 years during the 2014 surveillance year, on the basis of DSM-IV-TR criteria, and describes characteristics of the population of children with ASD.
Abstract: Problem/condition Autism spectrum disorder (ASD). Period covered 2014. Description of system The Autism and Developmental Disabilities Monitoring (ADDM) Network is an active surveillance system that provides estimates of the prevalence of autism spectrum disorder (ASD) among children aged 8 years whose parents or guardians reside within 11 ADDM sites in the United States (Arizona, Arkansas, Colorado, Georgia, Maryland, Minnesota, Missouri, New Jersey, North Carolina, Tennessee, and Wisconsin). ADDM surveillance is conducted in two phases. The first phase involves review and abstraction of comprehensive evaluations that were completed by professional service providers in the community. Staff completing record review and abstraction receive extensive training and supervision and are evaluated according to strict reliability standards to certify effective initial training, identify ongoing training needs, and ensure adherence to the prescribed methodology. Record review and abstraction occurs in a variety of data sources ranging from general pediatric health clinics to specialized programs serving children with developmental disabilities. In addition, most of the ADDM sites also review records for children who have received special education services in public schools. In the second phase of the study, all abstracted information is reviewed systematically by experienced clinicians to determine ASD case status. A child is considered to meet the surveillance case definition for ASD if he or she displays behaviors, as described on one or more comprehensive evaluations completed by community-based professional providers, consistent with the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) diagnostic criteria for autistic disorder; pervasive developmental disorder-not otherwise specified (PDD-NOS, including atypical autism); or Asperger disorder. This report provides updated ASD prevalence estimates for children aged 8 years during the 2014 surveillance year, on the basis of DSM-IV-TR criteria, and describes characteristics of the population of children with ASD. In 2013, the American Psychiatric Association published the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), which made considerable changes to ASD diagnostic criteria. The change in ASD diagnostic criteria might influence ADDM ASD prevalence estimates; therefore, most (85%) of the records used to determine prevalence estimates based on DSM-IV-TR criteria underwent additional review under a newly operationalized surveillance case definition for ASD consistent with the DSM-5 diagnostic criteria. Children meeting this new surveillance case definition could qualify on the basis of one or both of the following criteria, as documented in abstracted comprehensive evaluations: 1) behaviors consistent with the DSM-5 diagnostic features; and/or 2) an ASD diagnosis, whether based on DSM-IV-TR or DSM-5 diagnostic criteria. Stratified comparisons of the number of children meeting either of these two case definitions also are reported. Results For 2014, the overall prevalence of ASD among the 11 ADDM sites was 16.8 per 1,000 (one in 59) children aged 8 years. Overall ASD prevalence estimates varied among sites, from 13.1-29.3 per 1,000 children aged 8 years. ASD prevalence estimates also varied by sex and race/ethnicity. Males were four times more likely than females to be identified with ASD. Prevalence estimates were higher for non-Hispanic white (henceforth, white) children compared with non-Hispanic black (henceforth, black) children, and both groups were more likely to be identified with ASD compared with Hispanic children. Among the nine sites with sufficient data on intellectual ability, 31% of children with ASD were classified in the range of intellectual disability (intelligence quotient [IQ] 85). The distribution of intellectual ability varied by sex and race/ethnicity. Although mention of developmental concerns by age 36 months was documented for 85% of children with ASD, only 42% had a comprehensive evaluation on record by age 36 months. The median age of earliest known ASD diagnosis was 52 months and did not differ significantly by sex or race/ethnicity. For the targeted comparison of DSM-IV-TR and DSM-5 results, the number and characteristics of children meeting the newly operationalized DSM-5 case definition for ASD were similar to those meeting the DSM-IV-TR case definition, with DSM-IV-TR case counts exceeding DSM-5 counts by less than 5% and approximately 86% overlap between the two case definitions (kappa = 0.85). Interpretation Findings from the ADDM Network, on the basis of 2014 data reported from 11 sites, provide updated population-based estimates of the prevalence of ASD among children aged 8 years in multiple communities in the United States. The overall ASD prevalence estimate of 16.8 per 1,000 children aged 8 years in 2014 is higher than previously reported estimates from the ADDM Network. Because the ADDM sites do not provide a representative sample of the entire United States, the combined prevalence estimates presented in this report cannot be generalized to all children aged 8 years in the United States. Consistent with reports from previous ADDM surveillance years, findings from 2014 were marked by variation in ASD prevalence when stratified by geographic area, sex, and level of intellectual ability. Differences in prevalence estimates between black and white children have diminished in most sites, but remained notable for Hispanic children. For 2014, results from application of the DSM-IV-TR and DSM-5 case definitions were similar, overall and when stratified by sex, race/ethnicity, DSM-IV-TR diagnostic subtype, or level of intellectual ability. Public health action Beginning with surveillance year 2016, the DSM-5 case definition will serve as the basis for ADDM estimates of ASD prevalence in future surveillance reports. Although the DSM-IV-TR case definition will eventually be phased out, it will be applied in a limited geographic area to offer additional data for comparison. Future analyses will examine trends in the continued use of DSM-IV-TR diagnoses, such as autistic disorder, PDD-NOS, and Asperger disorder in health and education records, documentation of symptoms consistent with DSM-5 terminology, and how these trends might influence estimates of ASD prevalence over time. The latest findings from the ADDM Network provide evidence that the prevalence of ASD is higher than previously reported estimates and continues to vary among certain racial/ethnic groups and communities. With prevalence of ASD ranging from 13.1 to 29.3 per 1,000 children aged 8 years in different communities throughout the United States, the need for behavioral, educational, residential, and occupational services remains high, as does the need for increased research on both genetic and nongenetic risk factors for ASD.

3,967 citations


Posted Content
TL;DR: Self-Attention Generative Adversarial Network (SAGAN) as mentioned in this paper uses attention-driven, long-range dependency modeling for image generation tasks and achieves state-of-the-art results.
Abstract: In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. In SAGAN, details can be generated using cues from all feature locations. Moreover, the discriminator can check that highly detailed features in distant portions of the image are consistent with each other. Furthermore, recent work has shown that generator conditioning affects GAN performance. Leveraging this insight, we apply spectral normalization to the GAN generator and find that this improves training dynamics. The proposed SAGAN achieves the state-of-the-art results, boosting the best published Inception score from 36.8 to 52.52 and reducing Frechet Inception distance from 27.62 to 18.65 on the challenging ImageNet dataset. Visualization of the attention layers shows that the generator leverages neighborhoods that correspond to object shapes rather than local regions of fixed shape.

2,106 citations


Journal ArticleDOI
Daniel J. Benjamin1, James O. Berger2, Magnus Johannesson1, Magnus Johannesson3, Brian A. Nosek4, Brian A. Nosek5, Eric-Jan Wagenmakers6, Richard A. Berk7, Kenneth A. Bollen8, Björn Brembs9, Lawrence D. Brown7, Colin F. Camerer10, David Cesarini11, David Cesarini12, Christopher D. Chambers13, Merlise A. Clyde2, Thomas D. Cook14, Thomas D. Cook15, Paul De Boeck16, Zoltan Dienes17, Anna Dreber3, Kenny Easwaran18, Charles Efferson19, Ernst Fehr20, Fiona Fidler21, Andy P. Field17, Malcolm R. Forster22, Edward I. George7, Richard Gonzalez23, Steven N. Goodman24, Edwin J. Green25, Donald P. Green26, Anthony G. Greenwald27, Jarrod D. Hadfield28, Larry V. Hedges14, Leonhard Held20, Teck-Hua Ho29, Herbert Hoijtink30, Daniel J. Hruschka31, Kosuke Imai32, Guido W. Imbens24, John P. A. Ioannidis24, Minjeong Jeon33, James Holland Jones34, Michael Kirchler35, David Laibson36, John A. List37, Roderick J. A. Little23, Arthur Lupia23, Edouard Machery38, Scott E. Maxwell39, Michael A. McCarthy21, Don A. Moore40, Stephen L. Morgan41, Marcus R. Munafò42, Shinichi Nakagawa43, Brendan Nyhan44, Timothy H. Parker45, Luis R. Pericchi46, Marco Perugini47, Jeffrey N. Rouder48, Judith Rousseau49, Victoria Savalei50, Felix D. Schönbrodt51, Thomas Sellke52, Betsy Sinclair53, Dustin Tingley36, Trisha Van Zandt16, Simine Vazire54, Duncan J. Watts55, Christopher Winship36, Robert L. Wolpert2, Yu Xie32, Cristobal Young24, Jonathan Zinman44, Valen E. Johnson1, Valen E. Johnson18 
University of Southern California1, Duke University2, Stockholm School of Economics3, University of Virginia4, Center for Open Science5, University of Amsterdam6, University of Pennsylvania7, University of North Carolina at Chapel Hill8, University of Regensburg9, California Institute of Technology10, New York University11, Research Institute of Industrial Economics12, Cardiff University13, Northwestern University14, Mathematica Policy Research15, Ohio State University16, University of Sussex17, Texas A&M University18, Royal Holloway, University of London19, University of Zurich20, University of Melbourne21, University of Wisconsin-Madison22, University of Michigan23, Stanford University24, Rutgers University25, Columbia University26, University of Washington27, University of Edinburgh28, National University of Singapore29, Utrecht University30, Arizona State University31, Princeton University32, University of California, Los Angeles33, Imperial College London34, University of Innsbruck35, Harvard University36, University of Chicago37, University of Pittsburgh38, University of Notre Dame39, University of California, Berkeley40, Johns Hopkins University41, University of Bristol42, University of New South Wales43, Dartmouth College44, Whitman College45, University of Puerto Rico46, University of Milan47, University of California, Irvine48, Paris Dauphine University49, University of British Columbia50, Ludwig Maximilian University of Munich51, Purdue University52, Washington University in St. Louis53, University of California, Davis54, Microsoft55
TL;DR: The default P-value threshold for statistical significance is proposed to be changed from 0.05 to 0.005 for claims of new discoveries in order to reduce uncertainty in the number of discoveries.
Abstract: We propose to change the default P-value threshold for statistical significance from 0.05 to 0.005 for claims of new discoveries.

1,586 citations



Journal ArticleDOI
TL;DR: In this paper, the authors performed a prospective trial involving 10,273 women with hormone-receptor-positive, human epidermal growth factor receptor 2 (HER2)-negative, axillary node-negative breast cancer.
Abstract: Background The recurrence score based on the 21-gene breast cancer assay predicts chemotherapy benefit if it is high and a low risk of recurrence in the absence of chemotherapy if it is low; however, there is uncertainty about the benefit of chemotherapy for most patients, who have a midrange score. Methods We performed a prospective trial involving 10,273 women with hormone-receptor–positive, human epidermal growth factor receptor 2 (HER2)–negative, axillary node–negative breast cancer. Of the 9719 eligible patients with follow-up information, 6711 (69%) had a midrange recurrence score of 11 to 25 and were randomly assigned to receive either chemoendocrine therapy or endocrine therapy alone. The trial was designed to show noninferiority of endocrine therapy alone for invasive disease–free survival (defined as freedom from invasive disease recurrence, second primary cancer, or death). Results Endocrine therapy was noninferior to chemoendocrine therapy in the analysis of invasive disease–free surv...

1,337 citations


Journal ArticleDOI
TL;DR: A new periodontitis classification scheme has been adopted, in which forms of the disease previously recognized as "chronic" or "aggressive" are now grouped under a single category ("periodontitis") and are further characterized based on a multi-dimensional staging and grading system as mentioned in this paper.
Abstract: A new periodontitis classification scheme has been adopted, in which forms of the disease previously recognized as "chronic" or "aggressive" are now grouped under a single category ("periodontitis") and are further characterized based on a multi-dimensional staging and grading system. Staging is largely dependent upon the severity of disease at presentation as well as on the complexity of disease management, while grading provides supplemental information about biological features of the disease including a history-based analysis of the rate of periodontitis progression; assessment of the risk for further progression; analysis of possible poor outcomes of treatment; and assessment of the risk that the disease or its treatment may negatively affect the general health of the patient. Necrotizing periodontal diseases, whose characteristic clinical phenotype includes typical features (papilla necrosis, bleeding, and pain) and are associated with host immune response impairments, remain a distinct periodontitis category. Endodontic-periodontal lesions, defined by a pathological communication between the pulpal and periodontal tissues at a given tooth, occur in either an acute or a chronic form, and are classified according to signs and symptoms that have direct impact on their prognosis and treatment. Periodontal abscesses are defined as acute lesions characterized by localized accumulation of pus within the gingival wall of the periodontal pocket/sulcus, rapid tissue destruction and are associated with risk for systemic dissemination.

1,301 citations


Journal ArticleDOI
09 Mar 2018-Science
TL;DR: It is found that adopting a high-fiber diet promoted the growth of SCFA-producing organisms in diabetic humans and had better improvement in hemoglobin A1c levels, partly via increased glucagon-like peptide-1 production.
Abstract: The gut microbiota benefits humans via short-chain fatty acid (SCFA) production from carbohydrate fermentation, and deficiency in SCFA production is associated with type 2 diabetes mellitus (T2DM). We conducted a randomized clinical study of specifically designed isoenergetic diets, together with fecal shotgun metagenomics, to show that a select group of SCFA-producing strains was promoted by dietary fibers and that most other potential producers were either diminished or unchanged in patients with T2DM. When the fiber-promoted SCFA producers were present in greater diversity and abundance, participants had better improvement in hemoglobin A1c levels, partly via increased glucagon-like peptide-1 production. Promotion of these positive responders diminished producers of metabolically detrimental compounds such as indole and hydrogen sulfide. Targeted restoration of these SCFA producers may present a novel ecological approach for managing T2DM.

1,298 citations


Journal ArticleDOI
01 Apr 2018
TL;DR: A new epigenetic biomarker of aging, DNAm PhenoAge, is developed that strongly outperforms previous measures in regards to predictions for a variety of aging outcomes, including all-cause mortality, cancers, healthspan, physical functioning, and Alzheimer's disease.
Abstract: Identifying reliable biomarkers of aging is a major goal in geroscience. While the first generation of epigenetic biomarkers of aging were developed using chronological age as a surrogate for biological age, we hypothesized that incorporation of composite clinical measures of phenotypic age that capture differences in lifespan and healthspan may identify novel CpGs and facilitate the development of a more powerful epigenetic biomarker of aging. Using an innovative two-step process, we develop a new epigenetic biomarker of aging, DNAm PhenoAge, that strongly outperforms previous measures in regards to predictions for a variety of aging outcomes, including all-cause mortality, cancers, healthspan, physical functioning, and Alzheimer's disease. While this biomarker was developed using data from whole blood, it correlates strongly with age in every tissue and cell tested. Based on an in-depth transcriptional analysis in sorted cells, we find that increased epigenetic, relative to chronological age, is associated with increased activation of pro-inflammatory and interferon pathways, and decreased activation of transcriptional/translational machinery, DNA damage response, and mitochondrial signatures. Overall, this single epigenetic biomarker of aging is able to capture risks for an array of diverse outcomes across multiple tissues and cells, and provide insight into important pathways in aging.

1,261 citations


Proceedings ArticleDOI
18 Jun 2018
TL;DR: The proposed Context Encoding Module significantly improves semantic segmentation results with only marginal extra computation cost over FCN, and can improve the feature representation of relatively shallow networks for the image classification on CIFAR-10 dataset.
Abstract: Recent work has made significant progress in improving spatial resolution for pixelwise labeling with Fully Convolutional Network (FCN) framework by employing Dilated/Atrous convolution, utilizing multi-scale features and refining boundaries. In this paper, we explore the impact of global contextual information in semantic segmentation by introducing the Context Encoding Module, which captures the semantic context of scenes and selectively highlights class-dependent featuremaps. The proposed Context Encoding Module significantly improves semantic segmentation results with only marginal extra computation cost over FCN. Our approach has achieved new state-of-the-art results 51.7% mIoU on PASCAL-Context, 85.9% mIoU on PASCAL VOC 2012. Our single model achieves a final score of 0.5567 on ADE20K test set, which surpasses the winning entry of COCO-Place Challenge 2017. In addition, we also explore how the Context Encoding Module can improve the feature representation of relatively shallow networks for the image classification on CIFAR-10 dataset. Our 14 layer network has achieved an error rate of 3.45%, which is comparable with state-of-the-art approaches with over 10A— more layers. The source code for the complete system are publicly available1.

1,235 citations


Proceedings ArticleDOI
18 Jun 2018
TL;DR: AttnGAN as mentioned in this paper proposes an attentional generative network to synthesize fine-grained details at different sub-regions of the image by paying attentions to the relevant words in the natural language description.
Abstract: In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation. With a novel attentional generative network, the AttnGAN can synthesize fine-grained details at different sub-regions of the image by paying attentions to the relevant words in the natural language description. In addition, a deep attentional multimodal similarity model is proposed to compute a fine-grained image-text matching loss for training the generator. The proposed AttnGAN significantly outperforms the previous state of the art, boosting the best reported inception score by 14.14% on the CUB dataset and 170.25% on the more challenging COCO dataset. A detailed analysis is also performed by visualizing the attention layers of the AttnGAN. It for the first time shows that the layered attentional GAN is able to automatically select the condition at the word level for generating different parts of the image.

1,217 citations


Journal ArticleDOI
TL;DR: The PseudoDojo framework for developing and testing full tables of pseudopotentials is presented, and a new table generated with the ONCVPSP approach is demonstrated, leading to new insights into the effects of both the core-valence partitioning and the non-linear core corrections on the stability, convergence, and transferability of norm-conserving pseudopotential.

Journal ArticleDOI
TL;DR: It is demonstrated that Fe3GeTe2 (FGT), an exfoliable vdW magnet, exhibits robust 2D ferromagnetism with strong perpendicular anisotropy when thinned down to a monolayer.
Abstract: Discoveries of intrinsic two-dimensional (2D) ferromagnetism in van der Waals (vdW) crystals provide an interesting arena for studying fundamental 2D magnetism and devices that employ localized spins1–4. However, an exfoliable vdW material that exhibits intrinsic 2D itinerant magnetism remains elusive. Here we demonstrate that Fe3GeTe2 (FGT), an exfoliable vdW magnet, exhibits robust 2D ferromagnetism with strong perpendicular anisotropy when thinned down to a monolayer. Layer-number-dependent studies reveal a crossover from 3D to 2D Ising ferromagnetism for thicknesses less than 4 nm (five layers), accompanied by a fast drop of the Curie temperature (TC) from 207 K to 130 K in the monolayer. For FGT flakes thicker than ~15 nm, a distinct magnetic behaviour emerges in an intermediate temperature range, which we show is due to the formation of labyrinthine domain patterns. Our work introduces an atomically thin ferromagnetic metal that could be useful for the study of controllable 2D itinerant ferromagnetism and for engineering spintronic vdW heterostructures5. Metallic ferromagnetism is reported in an exfoliated monolayer of the van der Waals material Fe3GeTe2.

Journal ArticleDOI
TL;DR: The characteristics and underlying mechanisms of physiological and pathological hypertrophy are summarized, and possible therapeutic strategies targeting these pathways to prevent or reverse pathological hyperTrophy are discussed.
Abstract: Cardiomyocytes exit the cell cycle and become terminally differentiated soon after birth. Therefore, in the adult heart, instead of an increase in cardiomyocyte number, individual cardiomyocytes increase in size, and the heart develops hypertrophy to reduce ventricular wall stress and maintain function and efficiency in response to an increased workload. There are two types of hypertrophy: physiological and pathological. Hypertrophy initially develops as an adaptive response to physiological and pathological stimuli, but pathological hypertrophy generally progresses to heart failure. Each form of hypertrophy is regulated by distinct cellular signalling pathways. In the past decade, a growing number of studies have suggested that previously unrecognized mechanisms, including cellular metabolism, proliferation, non-coding RNAs, immune responses, translational regulation, and epigenetic modifications, positively or negatively regulate cardiac hypertrophy. In this Review, we summarize the underlying molecular mechanisms of physiological and pathological hypertrophy, with a particular emphasis on the role of metabolic remodelling in both forms of cardiac hypertrophy, and we discuss how the current knowledge on cardiac hypertrophy can be applied to develop novel therapeutic strategies to prevent or reverse pathological hypertrophy.

Journal ArticleDOI
TL;DR: The physiology of monocytes and macrophages in acute wound healing and the different phenotypes described in the literature for both in vitro and in vivo models are discussed.
Abstract: Macrophages play key roles in all phases of adult wound healing, which are inflammation, proliferation, and remodeling. As wounds heal, the local macrophage population transitions from predominantly pro-inflammatory (M1-like phenotypes) to anti-inflammatory (M2-like phenotypes). Non-healing chronic wounds, such as pressure, arterial, venous, and diabetic ulcers indefinitely remain in inflammation-the first stage of wound healing. Thus, local macrophages retain pro-inflammatory characteristics. This review discusses the physiology of monocytes and macrophages in acute wound healing and the different phenotypes described in the literature for both in vitro and in vivo models. We also discuss aberrations that occur in macrophage populations in chronic wounds, and attempts to restore macrophage function by therapeutic approaches. These include endogenous M1 attenuation, exogenous M2 supplementation and endogenous macrophage modulation/M2 promotion via mesenchymal stem cells, growth factors, biomaterials, heme oxygenase-1 (HO-1) expression, and oxygen therapy. We recognize the challenges and controversies that exist in this field, such as standardization of macrophage phenotype nomenclature, definition of their distinct roles and understanding which phenotype is optimal in order to promote healing in chronic wounds.

Proceedings ArticleDOI
18 Jun 2018
TL;DR: Zhang et al. as discussed by the authors proposed a Densely Connected Pyramid Dehazing Network (DCPDN), which can jointly learn the transmission map, atmospheric light and dehazing all together.
Abstract: We propose a new end-to-end single image dehazing method, called Densely Connected Pyramid Dehazing Network (DCPDN), which can jointly learn the transmission map, atmospheric light and dehazing all together. The end-to-end learning is achieved by directly embedding the atmospheric scattering model into the network, thereby ensuring that the proposed method strictly follows the physics-driven scattering model for dehazing. Inspired by the dense network that can maximize the information flow along features from different levels, we propose a new edge-preserving densely connected encoder-decoder structure with multi-level pyramid pooling module for estimating the transmission map. This network is optimized using a newly introduced edge-preserving loss function. To further incorporate the mutual structural information between the estimated transmission map and the dehazed result, we propose a joint-discriminator based on generative adversarial network framework to decide whether the corresponding dehazed image and the estimated transmission map are real or fake. An ablation study is conducted to demonstrate the effectiveness of each module evaluated at both estimated transmission map and dehazed result. Extensive experiments demonstrate that the proposed method achieves significant improvements over the state-of-the-art methods. Code and dataset is made available at: https://github.com/hezhangsprinter/DCPDN

Journal ArticleDOI
TL;DR: A new periodontitis classification scheme has been adopted, in which forms of the disease previously recognized as "chronic" or "aggressive" are now grouped under a single category ("periodontitis") and are further characterized based on a multi-dimensional staging and grading system.
Abstract: A new periodontitis classification scheme has been adopted, in which forms of the disease previously recognized as \"chronic\" or \"aggressive\" are now grouped under a single category (\"periodontitis\") and are further characterized based on a multi-dimensional staging and grading system. Staging is largely dependent upon the severity of disease at presentation as well as on the complexity of disease management, while grading provides supplemental information about biological features of the disease including a history-based analysis of the rate of periodontitis progression; assessment of the risk for further progression; analysis of possible poor outcomes of treatment; and assessment of the risk that the disease or its treatment may negatively affect the general health of the patient. Necrotizing periodontal diseases, whose characteristic clinical phenotype includes typical features (papilla necrosis, bleeding, and pain) and are associated with host immune response impairments, remain a distinct periodontitis category. Endodontic-periodontal lesions, defined by a pathological communication between the pulpal and periodontal tissues at a given tooth, occur in either an acute or a chronic form, and are classified according to signs and symptoms that have direct impact on their prognosis and treatment. Periodontal abscesses are defined as acute lesions characterized by localized accumulation of pus within the gingival wall of the periodontal pocket/sulcus, rapid tissue destruction and are associated with risk for systemic dissemination.

Journal ArticleDOI
10 Jan 2018
TL;DR: In this article, the state of the art in the implementation of low-dimensional nanomaterials and their van der Waals heterostructures for hydrogen evolution and CO2 reduction by electrocatalysis and photocatalysis is analyzed.
Abstract: Low-dimensional materials and their hybrids have emerged as promising candidates for electrocatalytic and photocatalytic hydrogen evolution and CO2 conversion into useful molecules. Progress in synthetic methods for the production of catalysts coupled with a better understanding of the fundamental catalytic mechanisms has enabled the rational design of catalytic nanomaterials with improved performance and selectivity. In this Review, we analyse the state of the art in the implementation of low-dimensional nanomaterials and their van der Waals heterostructures for hydrogen evolution and CO2 reduction by electrocatalysis and photocatalysis. We explore the mechanisms involved in both reactions and the different strategies to further optimize the activity, efficiency and selectivity of low-dimensional catalysts. The electrochemical oxidation and reduction of water and carbon dioxide are associated with the release or storage of energy. This Review reports the latest developments in the design and use of low-dimensional materials and their van der Waals heterostructures for electrocatalytic and photocatalytic hydrogen evolution and CO2 conversion.

Journal ArticleDOI
TL;DR: It is demonstrated experimentally that intelligent control of an autonomous vehicle is able to dampen stop-and-go waves that can arise even in the absence of geometric or lane changing triggers, suggesting a paradigm shift in traffic management.
Abstract: Traffic waves are phenomena that emerge when the vehicular density exceeds a critical threshold. Considering the presence of increasingly automated vehicles in the traffic stream, a number of research activities have focused on the influence of automated vehicles on the bulk traffic flow. In the present article, we demonstrate experimentally that intelligent control of an autonomous vehicle is able to dampen stop-and-go waves that can arise even in the absence of geometric or lane changing triggers. Precisely, our experiments on a circular track with more than 20 vehicles show that traffic waves emerge consistently, and that they can be dampened by controlling the velocity of a single vehicle in the flow. We compare metrics for velocity, braking events, and fuel economy across experiments. These experimental findings suggest a paradigm shift in traffic management: flow control will be possible via a few mobile actuators (less than 5%) long before a majority of vehicles have autonomous capabilities.

Journal ArticleDOI
TL;DR: This work outlines some of the common pitfalls of mobility extraction from field-effect transistor (FET) measurements and proposes practical recommendations to avoid reporting erroneous mobilities in publications.
Abstract: Mobility is an important charge-transport parameter in organic, inorganic and hybrid semiconductors. We outline some of the common pitfalls of mobility extraction from field-effect transistor (FET) measurements and propose practical recommendations to avoid reporting erroneous mobilities in publications.

Proceedings ArticleDOI
18 Jun 2018
TL;DR: A novel density-aware multi-stream densely connected convolutional neural network-based algorithm, called DID-MDN, for joint rain density estimation and de-raining, which achieves significant improvements over the recent state-of-the-art methods.
Abstract: Single image rain streak removal is an extremely challenging problem due to the presence of non-uniform rain densities in images. We present a novel density-aware multi-stream densely connected convolutional neural network-based algorithm, called DID-MDN, for joint rain density estimation and de-raining. The proposed method enables the network itself to automatically determine the rain-density information and then efficiently remove the corresponding rain-streaks guided by the estimated rain-density label. To better characterize rain-streaks with different scales and shapes, a multi-stream densely connected de-raining network is proposed which efficiently leverages features from different scales. Furthermore, a new dataset containing images with rain-density labels is created and used to train the proposed density-aware network. Extensive experiments on synthetic and real datasets demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art methods. In addition, an ablation study is performed to demonstrate the improvements obtained by different modules in the proposed method. The code can be downloaded at https://github.com/hezhangsprinter/DID-MDN

Journal ArticleDOI
TL;DR: Spatial imaging of current-induced spin accumulation at the edges of Bi2Se3 and BiSbTeSe2 topological insulators as well as Pt by a scanning photovoltage microscope at room temperature points towards a better understanding of the interaction between spins and circularly polarized light.
Abstract: Charge-to-spin conversion in various materials is the key for the fundamental understanding of spin-orbitronics and efficient magnetization manipulation. Here we report the direct spatial imaging of current-induced spin accumulation at the channel edges of Bi2Se3 and BiSbTeSe2 topological insulators as well as Pt by a scanning photovoltage microscope at room temperature. The spin polarization is along the out-of-plane direction with opposite signs for the two channel edges. The accumulated spin direction reverses sign upon changing the current direction and the detected spin signal shows a linear dependence on the magnitude of currents, indicating that our observed phenomena are current-induced effects. The spin Hall angle of Bi2Se3, BiSbTeSe2, and Pt is determined to be 0.0085, 0.0616, and 0.0085, respectively. Our results open up the possibility of optically detecting the current-induced spin accumulations, and thus point towards a better understanding of the interaction between spins and circularly polarized light.

Journal ArticleDOI
TL;DR: This work focuses on a dual-functional multi-input-multi-output (MIMO) radar-communication system, where a single transmitter with multiple antennas communicates with downlink cellular users and detects radar targets simultaneously and proposes a branch-and-bound algorithm that obtains a globally optimal solution.
Abstract: We focus on a dual-functional multi-input-multi-output (MIMO) radar-communication (RadCom) system, where a single transmitter with multiple antennas communicates with downlink cellular users and detects radar targets simultaneously. Several design criteria are considered for minimizing the downlink multiuser interference. First, we consider both omnidirectional and directional beampattern design problems, where the closed-form globally optimal solutions are obtained. Based on the derived waveforms, we further consider weighted optimizations targeting a flexible tradeoff between radar and communications performance and introduce low-complexity algorithms. Moreover, to address the more practical constant modulus waveform design problem, we propose a branch-and-bound algorithm that obtains a globally optimal solution, and derive its worst-case complexity as function of the maximum iteration number. Finally, we assess the effectiveness of the proposed waveform design approaches via numerical results.

Journal ArticleDOI
Siobain Duffy1
TL;DR: The high mutation rate of RNA viruses is credited with their evolvability and virulence, but recent evidence that this is a byproduct of selection for faster genomic replication is discussed.
Abstract: The high mutation rate of RNA viruses is credited with their evolvability and virulence. This Primer, however, discusses recent evidence that this is, in part, a byproduct of selection for faster genomic replication.

Journal ArticleDOI
23 Oct 2018-ACS Nano
TL;DR: Author(s): Voiry, Damien; Chhowalla, Manish; Gogotsi, Yury; Kotov, Nicholas A; Li, Yan; Penner, Reginald M; Schaak, Raymond E; Weiss, Paul S.
Abstract: Author(s): Voiry, Damien; Chhowalla, Manish; Gogotsi, Yury; Kotov, Nicholas A; Li, Yan; Penner, Reginald M; Schaak, Raymond E; Weiss, Paul S

Journal ArticleDOI
TL;DR: This review will discuss the advantages of metal-organic frameworks in the sensing and capture of harmful gases and vapors, as well as principles and strategies guiding the design of these materials.
Abstract: Toxic and hazardous chemical species are ubiquitous, predominantly emitted by anthropogenic activities, and pose serious risks to human health and the environment. Thus, the sensing and subsequent capture of these chemicals, especially in the gas or vapor phase, are of extreme importance. To this end, metal–organic frameworks have attracted significant interest, as their high porosity and wide tunability make them ideal for both applications. These tailorable framework materials are particularly promising for the specific sensing and capture of targeted chemicals, as they can be designed to fit a diverse range of required conditions. This review will discuss the advantages of metal–organic frameworks in the sensing and capture of harmful gases and vapors, as well as principles and strategies guiding the design of these materials. Recent progress in the luminescent detection of aromatic and aliphatic volatile organic compounds, toxic gases, and chemical warfare agents will be summarized, and the adsorptive removal of fluorocarbons/chlorofluorocarbons, volatile radioactive species, toxic industrial gases and chemical warfare agents will be discussed.

Journal ArticleDOI
TL;DR: Recon3D is presented, a computational resource that includes three-dimensional metabolite and protein structure data and enables integrated analyses of metabolic functions in humans and is used to functionally characterize mutations associated with disease, and identify metabolic response signatures that are caused by exposure to certain drugs.
Abstract: Genome-scale network reconstructions have helped uncover the molecular basis of metabolism. Here we present Recon3D, a computational resource that includes three-dimensional (3D) metabolite and protein structure data and enables integrated analyses of metabolic functions in humans. We use Recon3D to functionally characterize mutations associated with disease, and identify metabolic response signatures that are caused by exposure to certain drugs. Recon3D represents the most comprehensive human metabolic network model to date, accounting for 3,288 open reading frames (representing 17% of functionally annotated human genes), 13,543 metabolic reactions involving 4,140 unique metabolites, and 12,890 protein structures. These data provide a unique resource for investigating molecular mechanisms of human metabolism. Recon3D is available at http://vmh.life.

Journal ArticleDOI
Albert M. Sirunyan, Armen Tumasyan, Wolfgang Adam1, Federico Ambrogi1  +2238 moreInstitutions (159)
TL;DR: In this paper, the discriminating variables and the algorithms used for heavy-flavour jet identification during the first years of operation of the CMS experiment in proton-proton collisions at a centre-of-mass energy of 13 TeV, are presented.
Abstract: Many measurements and searches for physics beyond the standard model at the LHC rely on the efficient identification of heavy-flavour jets, i.e. jets originating from bottom or charm quarks. In this paper, the discriminating variables and the algorithms used for heavy-flavour jet identification during the first years of operation of the CMS experiment in proton-proton collisions at a centre-of-mass energy of 13 TeV, are presented. Heavy-flavour jet identification algorithms have been improved compared to those used previously at centre-of-mass energies of 7 and 8 TeV. For jets with transverse momenta in the range expected in simulated events, these new developments result in an efficiency of 68% for the correct identification of a b jet for a probability of 1% of misidentifying a light-flavour jet. The improvement in relative efficiency at this misidentification probability is about 15%, compared to previous CMS algorithms. In addition, for the first time algorithms have been developed to identify jets containing two b hadrons in Lorentz-boosted event topologies, as well as to tag c jets. The large data sample recorded in 2016 at a centre-of-mass energy of 13 TeV has also allowed the development of new methods to measure the efficiency and misidentification probability of heavy-flavour jet identification algorithms. The b jet identification efficiency is measured with a precision of a few per cent at moderate jet transverse momenta (between 30 and 300 GeV) and about 5% at the highest jet transverse momenta (between 500 and 1000 GeV).

Journal ArticleDOI
TL;DR: The attack model for IoT systems is investigated, and the IoT security solutions based on machine-learning (ML) techniques including supervised learning, unsupervised learning, and reinforcement learning (RL) are reviewed.
Abstract: The Internet of things (IoT), which integrates a variety of devices into networks to provide advanced and intelligent services, has to protect user privacy and address attacks such as spoofing attacks, denial of service (DoS) attacks, jamming, and eavesdropping. We investigate the attack model for IoT systems and review the IoT security solutions based on machine-learning (ML) techniques including supervised learning, unsupervised learning, and reinforcement learning (RL). ML-based IoT authentication, access control, secure offloading, and malware detection schemes to protect data privacy are the focus of this article. We also discuss the challenges that need to be addressed to implement these ML-based security schemes in practical IoT systems.

Proceedings ArticleDOI
02 Feb 2018
TL;DR: A memory-augmented neural network (MANN) integrated with the insights of collaborative filtering for recommendation is designed, which store and update users» historical records explicitly, which enhances the expressiveness of the model.
Abstract: User preferences are usually dynamic in real-world recommender systems, and a user»s historical behavior records may not be equally important when predicting his/her future interests. Existing recommendation algorithms -- including both shallow and deep approaches -- usually embed a user»s historical records into a single latent vector/representation, which may have lost the per item- or feature-level correlations between a user»s historical records and future interests. In this paper, we aim to express, store, and manipulate users» historical records in a more explicit, dynamic, and effective manner. To do so, we introduce the memory mechanism to recommender systems. Specifically, we design a memory-augmented neural network (MANN) integrated with the insights of collaborative filtering for recommendation. By leveraging the external memory matrix in MANN, we store and update users» historical records explicitly, which enhances the expressiveness of the model. We further adapt our framework to both item- and feature-level versions, and design the corresponding memory reading/writing operations according to the nature of personalized recommendation scenarios. Compared with state-of-the-art methods that consider users» sequential behavior for recommendation, e.g., sequential recommenders with recurrent neural networks (RNN) or Markov chains, our method achieves significantly and consistently better performance on four real-world datasets. Moreover, experimental analyses show that our method is able to extract the intuitive patterns of how users» future actions are affected by previous behaviors.

Journal ArticleDOI
TL;DR: Two of the frequently used surrogates, radial basis functions, and Kriging are tested on a variety of test problems and guidelines for the choice of appropriate surrogate model are discussed.