Complementary Sets Research Articles

Oxoammonium-Catalyzed Oxidation of N-Substituted Amines.

We report the development of oxoammonium-catalyzed oxidation of N-substituted amines via a hydride transfer mechanism. Steric and electronic tuning of catalyst led to complementary sets of conditions that can oxidize a broad scope of carbamates, sulfonamides, ureas, and amides into the corresponding imides. The reaction was further demonstrated on a 100-g scale using a continuous flow setup.

A genome engineered tool set for Drosophila TGF-β/BMP signaling studies.

Ligands of the TGF-β/BMP superfamily are critically involved in the regulation of growth, patterning and organogenesis and can act as long-range morphogens. Essential for understanding TGF-β/BMP signaling dynamics and regulation are tools that allow monitoring and manipulating pathway components expressed at physiological levels and endogenous spatiotemporal patterns. We used genome engineering to generate a comprehensive library of endogenously epitope- or fluorescently-tagged versions of receptors, co-receptors, transcription factors and key feedback regulators of the Drosophila BMP and Activin signaling pathways. We demonstrate that the generated alleles are biologically active and can be utilized for assessing tissue and subcellular distribution of the corresponding proteins. Further, we show that the genomic platforms can be used for in locus structure-function and cis-regulatory analyses. Finally, we present a complementary set of protein binder-based tools, which allow visualization as well as manipulation of the stability and subcellular localization of epitope-tagged proteins, providing new tools for the analysis of BMP signaling and beyond.

Assessing impacts of observations on ocean circulation models with examples from coastal, shelf, and marginal seas

Ocean observing systems in coastal, shelf and marginal seas collect diverse oceanographic information supporting a wide range of socioeconomic needs, but observations are necessarily sparse in space and/or time due to practical limitations. Ocean analysis and forecast systems capitalize on such observations, producing data-constrained, four-dimensional oceanographic fields. Here we review efforts to quantify the impact of ocean observations, observing platforms, and networks of platforms on model products of the physical ocean state in coastal regions. Quantitative assessment must consider a variety of issues including observation operators that sample models, error of representativeness, and correlated uncertainty in observations. Observing System Experiments, Observing System Simulation Experiments, representer functions and array modes, observation impacts, and algorithms based on artificial intelligence all offer methods to evaluate data-based model performance improvements according to metrics that characterize oceanographic features of local interest. Applications from globally distributed coastal ocean modeling systems document broad adoption of quantitative methods, generally meaningful reductions in model-data discrepancies from observation assimilation, and support for assimilation of complementary data sets, including subsurface in situ observation platforms, across diverse coastal environments.

Key attributes for effective knowledge brokering at the interface of environmental science and management

AbstractSpanning the boundaries between research and decision-making is critical for supporting environmental management. One way to do so is through knowledge brokers who, among other things, work to build social networks among decision-makers and researchers, facilitating their interaction and exchange of knowledge. While knowledge brokering has received increasing attention in the literature, critical gaps in our knowledge limit their effective recruitment and implementation, which demands for a better understanding of the personal attributes needed for their success. Such an understanding is also necessary to support the evaluation of knowledge brokering activities. Therefore, in this study, we investigate the question ‘what are the most important attributes for being a knowledge broker at the interface of environmental research and management’, and how is knowledge brokering evaluated. We do so through the study of Australia’s National Environmental Science Program (NESP) which employs a range of professional knowledge brokers, 14 of whom participated in this study. Specifically, we performed a quantitative ranking exercise (Q-methodology) and an online survey. Results show that the most important attributes to knowledge brokering were interpersonal social competencies of a unifying nature. As such, Being good at seeing opportunities for collaboration, Being able to tailor communication to different audiences, and Being able to ask questions were the most highly ranked attributes. In the dimension of evaluation, survey results show that NESP knowledge brokers are mainly evaluated through metrics relating to immediate delivery and engagement (mostly quantitative measures) and medium-term impact on end-users (qualitative measures). However, participants envisioned better options to capture the impact/value of knowledge brokering by evaluating long-term impacts and efforts throughout the full brokering process to better capture value addition in line with their goals, strategies, and required attributes. The new knowledge generated through our findings offers an applied contribution to the practice of knowledge brokering, suggesting that knowledge brokering is a distinct and complex career whereby different knowledge brokers with complementary skill sets appear relevant to perform all tasks along a knowledge brokering timeline. As such, our results also emphasize the need for bespoke approaches to evaluation that are tailored towards the diverse goals, strategies, and personal contributions of knowledge brokers to support their career progression and maximise outcomes.

Complementary Nil Equitable Domination in Fuzzy Graphs

Objectives: To demonstrate an inventive showcase prominent as complementary nil equitable dominating set, complementary nil equitable domination value in fuzzy graphs (FGs), and pertinent innovations. Methods: Strong arc in FGs ideas are used to perceive complementary nil equitable domination value. Findings: Modern concepts of complementary nil equitable dominance in FGs will lead to a number of applications, including the creation of cell phone towers in remote places, traffic light signals, and Google Maps. Novelty: Employing the ideas of equitable and strong arc in FGs, the complementary nil equitable dominating set and complementary nil equitable domination value in FGs are obtained. Some points are observed in this concept and some theorem related to the complementary nil equitable dominant set, its numbers are proved. AMS Subject Classification: 05C72. Keywords: Fuzzy graph, Equitable dominating set, Equitable domination number, Complementary nil equitable dominating set, Complementary nil equitable domination number

Morality and political economy from the vantage point of economics.

Political disagreement is increasingly moral, rather than economic, in nature, raising the question how the fields of political economy and moral psychology relate to each other. While these disciplines were initially deeply intertwined, cross-disciplinary exchange became rare throughout the 20th century. More recently, the tide has shifted again-social scientists of different backgrounds recognized that morality and politico-economic outcomes influence each other in rich bidirectional ways. Because psychologists and economists possess distinct and complementary skill sets, part of this movement consists of productive "economic imperialism"-economists leveraging their empirical toolkit to test and substantiate theories from moral psychology at scale or in the wild. To illustrate this, I present two case studies of recent economics research on prominent ideas in moral psychology. First is the theory that morality is ultimately functional-that it evolved as a form of "psychological and biological police" to enforce cooperation, such as in economic production and exchange. Second is that the structure of morality shapes political views and polarization, including on economic issues such as taxation and redistribution. I conclude from these case studies that economists have much to gain from integrating more ideas from moral psychology, and that moral psychologists will be able to make an even more compelling case if they engage with research in economics.

Organizations in the Space of Reasons

AbstractBounded rationality presents a challenge to the notion that virtue is a capacity for knowledge, suggesting that judgments concerning the salience of specific facts are, in some cases, an indication of one’s incapacity to appreciate the full range of normatively salient facts. This problem can be mitigated by linking an account of the virtues with a theory of organizations. From this perspective, virtue is inherently shaped by the norms structuring one’s role(s) and is linked to the complementary set of roles, that is, the organization, in which one is participating through the virtues of loyalty and obedience. Within this perspective, human cognitive limitations are made to be strengths, allowing one to focus on a narrow set of reasons for action linked with one’s role to better achieve salient aims as a member of the organization than one could as an isolated individual.

Advancing Photorealism Enhancement Using Convolutional Neural Networks (CNNs)

Photorealism has been the defining goal of computer graphics for half a century. A look at even the most sophisticated real-time games will quickly reveal that photorealism has not been achieved. An ineffable difference in the appearance of simulation and reality remains. In recent years, a complementary set of techniques has been developed in computer vision and machine learning. These techniques, based on deep learning, convolutional networks, and adversarial training, bypass physical modeling of geometric layout, material appearance, and light transport. Instead, images are synthesized by convolutional networks trained on large datasets. These techniques have been used to synthesize representative images from a given domain to convert semantic label maps to photographic images and to attempt to bridge the appearance gap between synthetic and real images. The images are enhanced by a convolutional network that leverages intermediate representations produced by conventional rendering pipelines. The network is trained via a novel adversarial objective, which provides strong supervision at multiple perceptual levels.

Underwater Target Detection Using Side-Scan Sonar Images Based on Upsampling and Downsampling

Side-scan sonar (SSS) images present unique challenges to computer vision due to their lower resolution, smaller targets, and fewer features. Although the mainstream backbone networks have shown promising results on traditional vision tasks, they utilize traditional convolution to reduce the dimensionality of feature maps, which may cause information loss for small targets and decrease performance in SSS images. To address this problem, based on the yolov8 network, we proposed a new underwater target detection model based on upsampling and downsampling. Firstly, we introduced a new general downsampling module called shallow robust feature downsampling (SRFD) and a receptive field convolution (RFCAConv) in the backbone network. Thereby multiple feature maps extracted by different downsampling techniques can be fused to create a more robust feature map with a complementary set of features. Additionally, an ultra-lightweight and efficient dynamic upsampling module (Dysample) is introduced to improve the accuracy of the feature pyramid network (FPN) in fusing different levels of features. On the underwater shipwreck dataset, our improved model’s mAP50 increased by 4.4% compared to the baseline model.

The role of cardiac acoustic biomarkers in monitoring patients with heart failure: A systematic literature review.

Heart failure (HF) creates a considerable clinical, humanistic and economic burden on patients and caregivers as well as on healthcare systems. To attenuate the significant burden of HF, there is a need for enhanced management of patients with HF. The use of digital tools for remote non-invasive monitoring of heart parameters is gaining traction, and cardiac acoustic biomarkers (CABs) have been proposed as a complementary set of measures to assess heart function alongside traditional methods such as electrocardiogram and echocardiography. We conducted a systematic literature review to evaluate associations between CABs and HF outcomes. Embase and MEDLINE databases were searched for recent studies published between 2013 and 2023 that evaluated CABs in patients with HF. Additional grey literature (i.e., conference, congress and pre-print publications from January 2021 to May 2023) searches were included. Two reviewers independently examined all articles; a third resolved conflicts. Data were extracted from articles meeting inclusion criteria. Extracted studies underwent quality and bias assessments using the Joanna Briggs Institute (JBI) critical appraisal tools. In total, 3074 records were screened, 73 full-text articles were assessed for eligibility and 27 publications were included. Third heart sound (S3) and electromechanical activation time (EMAT) were the CABs most often reported in the literature for monitoring HF. Fifteen publications discussed changes in S3 characteristics and its role in HF detection or outcomes: six studies highlighted S3 assessment among various groups of patients with HF; four studies evaluated the strength or amplitude of S3 with clinical outcomes; five studies assessed the relationship between S3 presence and clinical outcomes; and one study assessed both S3 presence and amplitude in relation to HF clinical outcomes. Eleven publications reported on EMAT and its derivatives: five studies on the relationship between EMAT and HF and six studies on the association of EMAT and HF clinical outcomes. Studies reporting the first and fourth heart sound, left ventricular ejection time and systolic dysfunction index were limited. Published literature supported S3 and EMAT as robust CAB measures in HF that may have value in remote clinical monitoring and management of patients with HF. Additional studies designed to test the predictive power of these CABs, and others less well-characterized, are needed. This work was funded by Astellas Pharma Inc.

Challenges and Opportunities of Sentinel-1 InSAR for Transport Infrastructure Monitoring

Monitoring displacement at transport infrastructure using Sentinel‑1 Interferometric Synthetic Aperture Radar (InSAR) faces challenges due to the sensor’s medium spatial resolution, which limits the pixel coverage over the infrastructure. Therefore, carefully selecting coherent pixels is crucial to achieve a high density of reliable measurement points and to minimize noisy observations. This study evaluates the effectiveness of various pixel selection methods for displacement monitoring within transport infrastructures. We employ a two-step InSAR time series processing approach. First, high-quality first-order pixels are selected using temporal phase coherence (TPC) to estimate and correct atmospheric contributions. Then, a combination of different pixel selection methods is applied to identify coherent second-order pixels for displacement analysis. These methods include amplitude dispersion index (ADI), TPC, phase linking coherence (PLC), and top eigenvalue percentage (TEP), targeting both point-like scatterer (PS) and distributed scatterer (DS) pixels. Experiments are conducted in two case studies: one in Germany, characterized by dense vegetation, and one in Spain, with sparse vegetation. In Germany, the density of measurement points was approximately 30 points/km², with the longest segment of the infrastructure without any coherent pixels being 2.8 km. In Spain, the density of measurement points exceeded 500 points/km², with the longest section without coherent pixels being 700 meters. The results indicate that despite the challenges posed by medium-resolution data, the sensor is capable of providing adequate measurement points when suitable pixel selection methods are employed. However, careful consideration is necessary to exclude noisy pixels from the analysis. The findings highlight the importance of choosing a proper method tailored to infrastructure characteristics. Specifically, combining TPC and PLC methods offers a complementary set of pixels suitable for displacement measurements, whereas ADI and TEP are less effective in this context. This study demonstrates the potential of Sentinel‑1 InSAR for capturing both regional-scale and localized displacements at transport infrastructure.

ES-GNN: Generalizing Graph Neural Networks Beyond Homophily With Edge Splitting.

While Graph Neural Networks (GNNs) have achieved enormous success in multiple graph analytical tasks, modern variants mostly rely on the strong inductive bias of homophily. However, real-world networks typically exhibit both homophilic and heterophilic linking patterns, wherein adjacent nodes may share dissimilar attributes and distinct labels. Therefore, GNNs smoothing node proximity holistically may aggregate both task-relevant and irrelevant (even harmful) information, limiting their ability to generalize to heterophilic graphs and potentially causing non-robustness. In this work, we propose a novel Edge Splitting GNN (ES-GNN) framework to adaptively distinguish between graph edges either relevant or irrelevant to learning tasks. This essentially transfers the original graph into two subgraphs with the same node set but complementary edge sets dynamically. Given that, information propagation separately on these subgraphs and edge splitting are alternatively conducted, thus disentangling the task-relevant and irrelevant features. Theoretically, we show that our ES-GNN can be regarded as a solution to a disentangled graph denoising problem, which further illustrates our motivations and interprets the improved generalization beyond homophily. Extensive experiments over 11 benchmark and 1 synthetic datasets not only demonstrate the effective performance of ES-GNN but also highlight its robustness to adversarial graphs and mitigation of the over-smoothing problem.

Multiple climatic drivers increase pace and consequences of ecosystem change in the Arctic Coastal Ocean

AbstractThe impacts of climate change on Arctic marine systems are noticeable within the scientific “lifetime” of most researchers and the iconic image of a polar bear struggling to stay on top of a melting ice floe captures many of the dominant themes of Arctic marine ecosystem change. But has our focus on open‐ocean systems and parameters that are more easily modeled and sensed remotely neglected an element that is responding more dramatically and with broader implications for Arctic ecosystems? We argue that a complementary set of changes to the open ocean is occurring along Arctic coasts, amplified by the interaction with changes on land and in the sea. We observe an increased number of ecosystem drivers with larger implications for the ecological and human communities they touch than are quantifiable in the open Arctic Ocean. Substantial knowledge gaps exist that must be filled to support adaptation and sustainability of socioecological systems along Arctic coasts.

Anomaly detection in collider physics via factorized observables

To maximize the discovery potential of high-energy colliders, experimental searches should be sensitive to unforeseen new physics scenarios. This goal has motivated the use of machine learning for unsupervised anomaly detection. In this paper, we introduce a new anomaly detection strategy called : factorized observables for regressing conditional expectations. Our approach is based on the inductive bias of factorization, which is the idea that the physics governing different energy scales can be treated as approximately independent. Assuming factorization holds separately for signal and background processes, the appearance of nontrivial correlations between low- and high-energy observables is a robust indicator of new physics. Under the most restrictive form of factorization, a machine-learned model trained to identify such correlations will in fact converge to the optimal new physics classifier. We test on a benchmark anomaly detection task for the Large Hadron Collider involving collimated sprays of particles called jets. By teasing out correlations between the kinematics and substructure of jets, our method can reliably extract percent-level signal fractions. This strategy for uncovering new physics adds to the growing toolbox of anomaly detection methods for collider physics with a complementary set of assumptions. Published by the American Physical Society 2024

Basis-Set Limit CCSD(T) Energies for Large Molecules with Local Natural Orbitals and Reduced-Scaling Basis-Set Corrections.

The calculation of density-based basis-set correction (DBBSC), which remedies the basis-set incompleteness (BSI) error of the correlation energy, is combined with local approximations. Aiming at large-scale applications, the procedure is implemented in our efficient local natural orbital-based coupled-cluster singles and doubles with perturbative triples [LNO-CCSD(T)] scheme. To this end, the range-separation function, which characterizes the one-electron BSI in space, is decomposed into the sum of contributions from individual localized molecular orbitals (LMOs). A compact domain is constructed around each LMO, and the corresponding contributions are evaluated only within these restricted domains. Furthermore, for the calculation of the complementary auxiliary basis set (CABS) correction, which significantly improves the Hartree-Fock (HF) energy, the local density fitting approximation is utilized. The errors arising from the local approximations are examined in detail, efficient prescreening techniques are introduced to compress the numerical quadrature used for DBBSC, and conservative default thresholds are selected for the truncation parameters. The efficiency of the DBBSC-LNO-CCSD(T) method is demonstrated through representative examples of up to 1000 atoms. Based on the numerical results, we conclude that the corrections drastically reduce the BSI error using double-ζ basis sets, often to below 1 kcal/mol compared to the reliable LNO-CCSD(T) complete basis set references, while significant improvements are also achieved with triple-ζ basis sets. Considering that the calculation of the DBBSC and CABS corrections only moderately increases the wall-clock time required for the post-HF steps in practical applications, the proposed DBBSC-LNO-CCSD(T) method offers a highly efficient and robust tool for large-scale calculations.

Delay-Doppler Map Shaping through Oversampled Complementary Sets for High-Speed Target Detection

Advanced waveform design schemes have been widely employed for radar and sonar remote sensing analysis such as target detection and separation, where significant range sidelobe is a main factor that limits the improvement of analysis performance. As an extensional type of Golay complementary waveforms, complementary sets are a waveform design scenario of concern that shows more diversity in the design of transmission order, and results in a different distribution of range sidelobes. This work proposes an oversampled generalized Prouhet–Thue–Morse (OGPTM) method for the transmitted signal design of complementary sets, with comprehensive analysis to the influence on the sidelobe distribution. Based on this idea and our previous work, we further put forward a pointwise multiplication processor (PMuP) to integrate two delay-Doppler maps of oversampled complementary sets, which achieve much better sidelobe suppression performance on high-speed target detection with range migration.

How Phenology Shapes Crop-Specific Sentinel-1 PolSAR Features and InSAR Coherence across Multiple Years and Orbits

Spatial information about plant health and productivity are essential when assessing the progress towards Sustainable Development Goals such as life on land and zero hunger. Plant health and productivity are strongly linked to a plant’s phenological progress. Remote sensing, and since the launch of Sentinel-1 (S1), specifically, radar-based frameworks have been studied for the purpose of monitoring phenological development. This study produces insights into how crop phenology shapes S1 signatures of PolSAR features and InSAR coherence of wheat, canola, sugar beet. and potato across multiple years and orbits. Hereby, differently smoothed time series and a base line of growing degree days are stacked to estimate the patterns of occurrence of extreme values and break points. These patterns are then linked to in situ observations of phenological developments. The comparison of patterns across multiple orbits and years reveals that a single optimized fit hampers the tracking capacities of an entire season monitoring framework, as does the sole reliance on extreme values. VV and VH backscatter intensities outperform all other features, but certain combinations of phenological stage and crop type are better covered by a complementary set of PolSAR features and coherence. With regard to PolSAR features, alpha and entropy can be replaced by the cross-polarization ratio for tracking certain stages. Moreover, a range of moderate incidence angles is better suited for monitoring crop phenology. Also, wheat and canola are favored by a late afternoon overpass. In sum, this study provides insights into phenological developments at the landscape level that can be of further use when investigating spatial and temporal variations within the landscape.

Iron(II)-Catalyzed 1,2-Diamination of Styrenes Installing a Terminal NH2 Group Alongside Unprotected Amines.

1,2-Diamination of alkenes represents an attractive way to generate differentiated vicinal diamines, which are prevalent motifs in biologically active compounds and catalysts. However, existing methods are usually limited in scope and produce diamines where one or both nitrogens are protected, adding synthetic steps for deprotection and further N-functionalization to reach a desired target. Furthermore, the range of amino groups that can be introduced at the internal position is fairly limited. Here we describe a 1,2-diamination of styrenes that directly installs a free amino group at the terminal position and a wide variety of unprotected nitrogen nucleophiles (primary or secondary alkyl or aromatic amines, sulfoximines, N-heterocycles, and ammonia surrogate) at the internal position. Two complementary sets of conditions encompass electronically activated and deactivated styrenes with diverse substitution patterns and functional groups. Moreover, this strategy can be extended to the 1,2-aminothiolation of styrenes.

Iron(II)‐Catalyzed 1,2‐Diamination of Styrenes Installing a Terminal NH2 Group Alongside Unprotected Amines

1,2‐Diamination of alkenes represents an attractive way to generate differentiated vicinal diamines, which are prevalent motifs in biologically active compounds and catalysts. However, existing methods are usually limited in scope and produce diamines where one or both nitrogens are protected, adding synthetic steps for deprotection and further N‐functionalization to reach a desired target. Furthermore, the range of amino groups that can be introduced at the internal position is fairly limited. Here we describe a 1,2‐diamination of styrenes that directly installs a free amino group at the terminal position and a wide variety of unprotected nitrogen nucleophiles (primary or secondary alkyl or aromatic amines, sulfoximines, N‐heterocycles, and ammonia surrogate) at the internal position. Two complementary sets of conditions encompass electronically activated and deactivated styrenes with diverse substitution patterns and functional groups. Moreover, this strategy can be extended to the 1,2‐aminothiolation of styrenes.

Genomic insights into the shared and distinct genetic architecture of cognitive function and schizophrenia

Cognitive impairment is a major determinant of functional outcomes in schizophrenia, however, understanding of the biological mechanisms underpinning cognitive dysfunction in the disorder remains incomplete. Here, we apply Genomic Structural Equation Modelling to identify latent cognitive factors capturing genetic liabilities to 12 cognitive traits measured in the UK Biobank. We identified three broad factors that underly the genetic correlations between the cognitive tests. We explore the overlap between latent cognitive factors, schizophrenia, and schizophrenia symptom dimensions using a complementary set of statistical approaches, applied to data from the latest schizophrenia genome-wide association study (Ncase = 53,386, Ncontrol = 77,258) and the Thematically Organised Psychosis study (Ncase = 306, Ncontrol = 1060). Global genetic correlations showed a significant moderate negative genetic correlation between each cognitive factor and schizophrenia. Local genetic correlations implicated unique genomic regions underlying the overlap between schizophrenia and each cognitive factor. We found substantial polygenic overlap between each cognitive factor and schizophrenia and biological annotation of the shared loci implicated gene-sets related to neurodevelopment and neuronal function. Lastly, we show that the common genetic determinants of the latent cognitive factors are not predictive of schizophrenia symptoms in the Norwegian Thematically Organized Psychosis cohort. Overall, these findings inform our understanding of cognitive function in schizophrenia by demonstrating important differences in the shared genetic architecture of schizophrenia and cognitive abilities.