Third-party arbitration and forgiving strategies increase cooperation when perception errors are common.

Humans cooperate in groups in which mutual monitoring is common, and this provides the possibility of third-party arbitration. Third-party arbitration stabilizes reciprocity in at least two ways: first, when it is accurate, it reduces the frequency of misunderstandings resulting from perception errors, and second, even when it is inaccurate, it provides a public signal that allows pairs to align their expectations about how to behave after errors occur. Here, we describe experiments that test for these two effects. We find that in an iterated, sequential Prisoner's Dilemma game with errors, players with the highest average payoffs are those who make use of third-party arbitration and who also employ forgiving strategies. The combination of these two behaviours reduces the detrimental effects of errors on reciprocity, resulting in more cooperation.

Thermoradiotherapy optimization strategies accounting for hyperthermia delivery uncertainties.

INTRODUCTION: The combined effect of hyperthermia and radiotherapy can be quantified by an enhanced equivalent radiation dose (EQDRT). Uncertainties in hyperthermia treatment planning and adjustments during treatment can impact achieved EQDRT. We developed and compared strategies for EQDRT optimization of radiotherapy plans, focusing on robustness against common adjustments. METHODS: Using Plan2Heat, we computed pre-planning hyperthermia plans and treatment adjustment scenarios for three cervical cancer patients. We imported these scenarios into RayStation 12A for optimization with four different strategies: (1) Conventional radiotherapy optimization prescribing 46 Gy to the planning target volume (PTV), (2) Nominal EQDRT optimization using the pre-planning scenario, targeting uniform 58 Gy in the gross tumor volume (GTV), keeping organs at risk (OAR) doses as in plan (1), (3) Robust EQDRT optimization, as (2) but adding adjusted scenarios for optimization, (4) Library of Plans (four plans), with strategy (2) criteria but optimizing on one adjusted scenario per plan. We calculated for each radiotherapy plan EQDRT distributions for pre-planning and adjusted scenarios, evaluating for each combination GTV coverage and homogeneity objectives. RESULTS: EQDRT95% increased from 49.9-50.9 Gy in strategy (1) to 56.1-57.4 Gy in strategy (2) with the pre-planning scenario, improving homogeneity in ∼10%. Strategy (2) demonstrated the best overall robustness, with 62% of all GTV objectives within tolerance. Strategy (3) had higher percentage of coverage objectives within tolerance than strategy (2) (68% vs 54%), but lower percentage for uniformity (44% vs 71%). Strategy (4) showed similar EQDRT95% and homogeneity for adjusted scenarios than strategy (2) for pre-planning scenario. D0.1% for OARs was increased by strategies (2-4) by up to ∼6 Gy. CONCLUSIONS: EQDRT optimization enhances EQDRT levels and uniformity compared to conventional optimization. Better overall robustness is achieved optimizing on the pre-planning hyperthermia plan. Robust optimization improves coverage but reduces homogeneity. A library of plans ensures coverage and uniformity when dealing with adjusted hyperthermia scenarios.

Robust, stable cooling cellulose composite: Coupling nano-SiO2 and cellulose acetate in natural cellulose.

Passive radiative cooling material of cellulose by coupling inorganic nanoparticles, have demonstrated competitive advantages in sustainably cooling buildings and constructions due to their voluminous availability, biodegradability, renewability, and natural origin. However, the weak stability of cellulose-inorganic nanoparticle materials when exposed to water or external forces remains a significant challenge that impedes their practical application. In this study, we proposed an easy-to-prepare, scalable, and robust cooling cellulose composite by coupling nano-SiO2 and cellulose acetate (CA) within cellulose fibers, using the mature pulping and paper process (filling of inorganic particles of nano-SiO2 and subsequent sizing of polymer of CA). More importantly, the CA molecules form the strong bonding with the cellulose molecules due to the high similarity of their molecular structure, which makes CA function as a "glue" to effectively fasten nano-SiO2 on the cellulose fibers. Correspondingly, our cellulose composite features desirable robustness and structural stability even undergoing mechanical beating and water-soaking treatments, demonstrating its excellent robustness and desirable adaptability to natural environments, such as wind and rain. As a result, despite undergoing water-soaking (for 40 days) or environmental exposure (for 90 days), the cooling cellulose composite still exhibits excellent solar reflectance (>95 %) and infrared thermal emissivity (>0.95 at 8-13 µm), enabling sub-ambient temperature (∼6.5 °C during daytime and ∼8 °C at nighttime) throughout the day. Our cooling cellulose composite demonstrates promising potential as an environmentally friendly, low-cost, and stable cooling material in our low-carbon society.

Gradient-Robust Hybrid DG Discretizations for the Compressible Stokes Equations.

This paper studies two hybrid discontinuous Galerkin (HDG) discretizations for the velocity-density formulation of the compressible Stokes equations with respect to several desired structural properties, namely provable convergence, the preservation of non-negativity and mass constraints for the density, and gradient-robustness. The later property dramatically enhances the accuracy in well-balanced situations, such as the hydrostatic balance where the pressure gradient balances the gravity force. One of the studied schemes employs an H ( div ) -conforming velocity ansatz space which ensures all mentioned properties, while a fully discontinuous method is shown to satisfy all properties but the gradient-robustness. Also higher-order schemes for both variants are presented and compared in three numerical benchmark problems. The final example shows the importance also for non-hydrostatic well-balanced states for the compressible Navier-Stokes equations.

Multiply robust estimation of marginal structural models in observational studies subject to covariate-driven observations.

Electronic health records and other sources of observational data are increasingly used for drawing causal inferences. The estimation of a causal effect using these data not meant for research purposes is subject to confounding and irregularly-spaced covariate-driven observation times affecting the inference. A doubly-weighted estimator accounting for these features has previously been proposed that relies on the correct specification of two nuisance models used for the weights. In this work, we propose a novel consistent multiply robust estimator and demonstrate analytically and in comprehensive simulation studies that it is more flexible and more efficient than the only alternative estimator proposed for the same setting. It is further applied to data from the Add Health study in the United States to estimate the causal effect of therapy counseling on alcohol consumption in American adolescents.

Antireflective Superhydrophobic and Robust Coating Based on Chitin Nanofibers and Methylsilanized Silica for Outdoor Applications.

Antireflective coatings with superhydrophobicity have many outdoor applications, such as solar photovoltaic panels and windshields. In this study, we fabricated an omnidirectional antireflective and superhydrophobic coating with good mechanical robustness and environmental durability via the spin coating technique. The coating consisted of a layer of phytic acid (PA)/polyacrylamide (PAM)/calcium ions (Ca2+) (referred to as Binder), an antireflective layer composed of chitin nanofibers (ChNFs), and a hydrophobic layer composed of methylsilanized silica (referred to as Mosil). The transmittance of a glass slide with the Binder/ChNFs/Mosil coating had a 5.2% gain at a wavelength of 550 nm, and the antireflective coating showed a water contact angle as high as 160° and a water sliding angle of 8°. The mechanical robustness and environmental durability of the coating, including resistance to peeling, dynamic impact, chemical erosion, ultraviolet (UV) irradiation, and high temperature, were evaluated. The coating retained excellent antireflective capacity and self-cleaning performance in the harsh conditions. The increase in voltage per unit area of a solar panel with a Binder/ChNFs/Mosil coating reached 0.4 mV/cm2 compared to the solar panel exposed to sunlight with an intensity of 54.3 × 103 lx. This work not only demonstrates that ChNFs can be used as raw materials to fabricate antireflective superhydrophobic coatings for outdoor applications but also provides a feasible and efficient approach to do so.

Graph Feature Refinement and Fusion in Transformer for Structural Damage Detection.

Structural damage detection is of significance for maintaining the structural health. Currently, data-driven deep learning approaches have emerged as a highly promising research field. However, little progress has been made in studying the relationship between the global and local information of structural response data. In this paper, we have presented an innovative Convolutional Enhancement and Graph Features Fusion in Transformer (CGsformer) network for structural damage detection. The proposed CGsformer network introduces an innovative approach for hierarchical learning from global to local information to extract acceleration response signal features for structural damage representation. The key advantage of this network is the integration of a graph convolutional network in the learning process, which enables the construction of a graph structure for global features. By incorporating node learning, the graph convolutional network filters out noise in the global features, thereby facilitating the extraction to more effective local features. In the verification based on the experimental data of four-story steel frame model experiment data and IASC-ASCE benchmark structure simulated data, the CGsformer network achieved damage identification accuracies of 92.44% and 96.71%, respectively. It surpassed the existing traditional damage detection methods based on deep learning. Notably, the model demonstrates good robustness under noisy conditions.

Design principles of multi-map variation in biological systems.

Complexity in biology is often described using a multi-map hierarchical architecture, where the genotype, representing the encoded information, is mapped to the functional level, known as the phenotype, which is then connected to a latent phenotype we refer to as fitness. This underlying architecture governs the processes driving evolution. Furthermore, natural selection, along with other neutral forces, can, in turn, modify these maps. At each level, variation is observed. Here, I propose the need to establish principles that can aid in understanding the transformation of variation within this multi-map architecture. Specifically, I will introduce three, related to the presence of modulators, constraints, and the modular channeling of variation. By comprehending these design principles in various biological systems, we can gain better insights into the mechanisms underlying these maps and how they ultimately contribute to evolutionary dynamics.

A geometric approach to robust medical image segmentation.

Robustness of deep learning segmentation models is crucial for their safe incorporation into clinical practice. However, these models can falter when faced with distributional changes. This challenge is evident in magnetic resonance imaging (MRI) scans due to the diverse acquisition protocols across various domains, leading to differences in image characteristics such as textural appearances. We posit that the restricted anatomical differences between subjects could be harnessed to refine the latent space into a set of shape components. The learned set then aims to encompass the relevant anatomical shape variation found within the patient population. We explore this by utilising multiple MRI sequences to learn texture invariant and shape equivariant features which are used to construct a shape dictionary using vector quantisation. We investigate shape equivariance to a number of different types of groups. We hypothesise and prove that the greater the group order, i.e., the denser the constraint, the better becomes the model robustness. We achieve shape equivariance either with a contrastive based approach or by imposing equivariant constraints on the convolutional kernels. The resulting shape equivariant dictionary is then sampled to compose the segmentation output. Our method achieves state-of-the-art performance for the task of single domain generalisation for prostate and cardiac MRI segmentation. Code is available at https://github.com/AinkaranSanthi/A_Geometric_Perspective_For_Robust_Segmentation.

Multi-institutional investigation into the robustness of intra-cranial multi-target stereotactic radiosurgery plans to patient setup errors.

PURPOSE: This study aimed to analyse correlations between planning factors including plan geometry and plan complexity with robustness to patient setup errors. METHODS: Multiple-target brain stereotactic radiosurgery (SRS) plans were obtained through the Trans-Tasman Radiation Oncology Group (TROG) international treatment planning challenge (2018). The challenge dataset consisted of five intra-cranial targets with a 20 Gy prescription. Setup error was simulated using an in-house tool. Dose to targets was assessed via dose covering 99 % (D99 %) of gross tumour volume (GTV) and 98 % of planning target volume (PTV). Dose to organs at risk was assessed using volume of normal brain receiving 12 Gy and maximum dose covering 0.03 cc of brainstem. Plan complexity was assessed via edge metric, modulation complexity score, mean multi-leaf collimator (MLC) gap, mean MLC speed and plan modulation. RESULTS: Even for small (0.5 mm/°) errors, GTV D99 % was reduced by up to 20 %. The strongest correlation was found between lower complexity plans (larger mean MLC gap and lower edge metric) and higher robustness to setup error. Lower complexity plans had 1 %-20 % fewer targets/scenarios with GTV D99 % falling below the specified tolerance threshold. These complexity metrics correlated with 100 % isodose volume sphericity and dose conformity, though similar conformity was achievable with a range of complexities. CONCLUSIONS: A higher level of importance should be directed towards plan complexity when considering plan robustness. It is recommended when planning multi-target SRS, larger MLC gaps and lower MLC aperture irregularity be considered during plan optimisation due to higher robustness should patient positioning errors occur.

The structure of bacteria-fungi bipartite networks along elevational gradients in contrasting climates.

Climate change is altering species distribution and modifying interactions in microbial communities. Understanding microbial community structure and their interactions is crucial to interpreting ecosystem responses to climate change. Here, we examined the assemblages of stream bacteria and fungi, and the associations between the two groups along elevational gradients in two regions with contrasting precipitation and temperature, that is the Galong and Qilian mountains of the Tibetan Plateau. In the wetter and warmer region, the species richness significantly increased and decreased with elevation for bacteria and fungi, respectively, while were nonsignificant in the drier and colder region. Their bipartite network structure was also different by showing significant increases in connectance and nestedness towards higher elevations only in the wetter and warmer region. In addition, these correlation network structure generally exhibited similar positive association with species richness in the wetter and warmer region and the drier and colder region. In the wetter and warmer region, climatic change along elevation was more important in determining connectance and nestedness, whereas microbial species richness exerted a stronger influence on network structure and robustness in the drier and colder region. These findings indicate substantial forthcoming changes in microbial diversity and network structure in warming climates, especially in wetter and warmer regions on Earth, advancing the understanding of microbial bipartite interactions' response to climate change.

A novel bounded loss framework for support vector machines.

This paper introduces a novel bounded loss framework for SVM and SVR. Specifically, using the Pinball loss as an illustration, we devise a novel bounded exponential quantile loss (Leq-loss) for both support vector machine classification and regression tasks. For Leq-loss, it not only enhances the robustness of SVM and SVR against outliers but also improves the robustness of SVM to resampling from a different perspective. Furthermore, EQSVM and EQSVR were constructed based on Leq-loss, and the influence functions and breakdown point lower bounds of their estimators are derived. It is proved that the influence functions are bounded, and the breakdown point lower bounds can reach the highest asymptotic breakdown point of 1/2. Additionally, we demonstrated the robustness of EQSVM to resampling and derived its generalization error bound based on Rademacher complexity. Due to the Leq-loss being non-convex, we can use the concave-convex procedure (CCCP) technique to transform the problem into a series of convex optimization problems and use the ClipDCD algorithm to solve these convex optimization problems. Numerous experiments have been conducted to confirm the effectiveness of the proposed EQSVM and EQSVR.

Sustainable Plastics with High Performance and Convenient Processibility.

Designing and making sustainable plastics is especially urgent to reduce their ecological and environmental impacts. However, it remains challenging to construct plastics with simultaneous high sustainability and outstanding comprehensive performance. Here, a composite strategy of in situ polymerizing a petroleum-based monomer with the presence of an industrialized bio-derived polymer in a quasi-solvent-free system is introduced, affording the plastic with excellent mechanical robustness, impressive thermal and solvent stability, as well as low energy, consumes during production, processing, and recycling. Particularly, the plastic can be easily processed into diverse shapes through 3D printing, injection molding, etc. during polymerization and further reprocessed into other complex structures via eco-friendly hydrosetting. In addition, the plastic is mechanically robust with Young's modulus of up to 3.7 GPa and tensile breaking strength of up to 150.2 MPa, superior to many commercially available plastics and other sustainable plastics. It is revealed that hierarchical hydrogen bonds in plastic predominate the well-balanced sustainability and performance. This work provides a new path for fabricating high-performance sustainable plastic toward practical applications, contributing to the circular economy.

Systematic solution for multi-model control approaches in nonlinear dynamic systems based on the s-gap metric.

This paper proposes a systematic approach for optimizing the distribution of local models in multi-model control systems (MMCS) to enhance overall robustness. While existing literature discusses this method for linear parameter varying (LPV) and uncertain linear time-invariant (LTI) systems, significant limitations persist in addressing nonlinear dynamic systems. Robust control tools like the gap metric and generalized stability margin (GSM) have limited effectiveness in analyzing the robustness of nonlinear feedback systems. To address these challenges, novel concepts of the gap metric and GSM are introduced to determine central operating points (COPs) within local operating areas (LOAs) across the total operating area (TOA). These COPs guide the extraction of affine disturbance local models (ADLMs). Additionally, an optimization problem based on the s-gap metric and GSM is presented to optimize COPs placement and LOAs boundaries. Challenges such as non-monotonic behavior of the cost function and complexity arising from the s-gap metric formulation necessitate novel solution methods. To address these, constraints are applied to the cost function, and a novel discrete optimization approach is introduced. Finally, theoretical findings are applied to the Duffing system, pH neutralization process, and continuous stirred tank reactor (CSTR) plant to evaluate the proposed method's effectiveness. This comprehensive validation across different systems underscores the versatility and practical utility of the proposed approach.

Challenges and limitations in applying radiomics to PET imaging: Possible opportunities and avenues for research.

Radiomics, the high-throughput extraction of quantitative imaging features from medical images, holds immense potential for advancing precision medicine in oncology and beyond. While radiomics applied to positron emission tomography (PET) imaging offers unique insights into tumor biology and treatment response, it is imperative to elucidate the challenges and constraints inherent in this domain to facilitate their translation into clinical practice. This review examines the challenges and limitations of applying radiomics to PET imaging, synthesizing findings from the last five years (2019-2023) and highlights the significance of addressing these challenges to realize the full clinical potential of radiomics in oncology and molecular imaging. A comprehensive search was conducted across multiple electronic databases, including PubMed, Scopus, and Web of Science, using keywords relevant to radiomics issues in PET imaging. Only studies published in peer-reviewed journals were eligible for inclusion in this review. Although many studies have highlighted the potential of radiomics in predicting treatment response, assessing tumor heterogeneity, enabling risk stratification, and personalized therapy selection, various challenges regarding the practical implementation of the proposed models still need to be addressed. This review illustrates the challenges and limitations of radiomics in PET imaging across various cancer types, encompassing both phantom and clinical investigations. The analyzed studies highlight the importance of reproducible segmentation methods, standardized pre-processing and post-processing methodologies, and the need to create large multicenter studies registered in a centralized database to promote the continuous validation and clinical integration of radiomics into PET imaging.

The multiverse of universes: A tutorial to plan, execute and interpret multiverses analyses using the R package multiverse.

Even when guided by strong theories and sound methods, researchers must often choose a singular course of action from multiple viable alternatives. Regardless of the choice, it, along with all other choices made during the research process, individually and collectively affects study results, often in unpredictable ways. The inability to disentangle how much of an observed effect is attributable to the phenomenon of interest, and how much is attributable to what have come to be known as researcher degrees of freedom (RDF), slows theoretical progress and stymies practical implementation. However, if one could examine the results from a particular set of RDF (known as a universe) against a systematically and comprehensively determined background of alternative viable universes (known as a multiverse), then the effects of RDF can be directly examined to provide greater context and clarity to future researchers, and greater confidence in the recommendations to practitioners. This tutorial demonstrates a means to map result variability directly and efficiently, and empirically investigate RDF impact on conclusions via multiverse analysis. Using the R package multiverse, we outline best practices in planning, executing and interpreting of multiverse analyses.

Technical Notes: Robustness of three-dimensional treatment and imaging isocenter testing using a new gel dosimeter and kilovoltage CBCT.

BACKGROUND: Coincidence of the treatment and imaging isocenter coordinates is required to safely perform small-margin treatments, such as stereotactic radiosurgery of multiple brain metastases. A comprehensive and direct methodology for verifying concordance of kilovoltage cone-beam computed tomography (kV-CBCT) and treatment coordinates using an x-ray CT-based polymer gel dosimeter (dGEL) and onboard kV-CBCT was previously reported. Using this methodology, we tested the ability of a new commercially available x-ray CT-based polymer dGEL with a rapid response to provide efficient quality assurance (QA). PURPOSE: The aim of this study was to evaluate the robustness of the three-dimensional geometric QA methodology using dGEL. METHODS: The dGEL were commercially manufactured. The prescribed dose for each field was determined by visually identifying the 5, 10, and 20 Gy isodose lines. A linear accelerator was used to irradiate the gels with seven non-coplanar beams. An in-house analysis program was used to identify the beam axes and treatment isocenter in kV-CBCT coordinates by processing the pre- and post-irradiation CBCT images. The impact of the radiation dose on the test reproducibility was examined, and the detectability of an intentional geometric error was assessed. RESULTS: The treatment isocenter was within 0.4 mm of the imaging isocenter for all radiation doses. The residual error of the test with the intentional error was within 0.2 mm. The analysis and image quality variations for a single dGEL introduced displacement errors less than 0.3 mm. CONCLUSIONS: The test assessed the coincidence of treatment and kV-CBCT isocenter coordinates and detected errors with high robustness. Even for a 10 Gy dose, the test yielded results comparable with those obtained using higher radiation doses owing to the rapid response of the dGEL dosimeter.

A transient radial cortical microtubule array primes cell division in Arabidopsis.

Although the formation of new walls during plant cell division tends to follow maximal tensile stress direction, analyses of individual cells over time reveal a much more variable behavior. The origin of such variability as well as the exact role of interphasic microtubule behavior before cell division have remained mysterious so far. To approach this question, we took advantage of the Arabidopsis stem, where the tensile stress pattern is both highly anisotropic and stable. Although cortical microtubules (CMTs) generally align with maximal tensile stress, we detected a specific time window, ca. 3 h before cell division, where cells form a radial pattern of CMTs. This microtubule array organization preceded preprophase band (PPB) formation, a transient CMT array predicting the position of the future division plane. It was observed under different growth conditions and was not related to cell geometry or polar auxin transport. Interestingly, this cortical radial pattern correlated with the well-documented increase of cytoplasmic microtubule accumulation before cell division. This radial organization was prolonged in cells of the trm678 mutant, where CMTs are unable to form a PPB. Whereas division plane orientation in trm678 is noisier, we found that cell division symmetry was in contrast less variable between daughter cells. We propose that this "radial step" reflects a trade-off in robustness for two essential cell division attributes: symmetry and orientation. This involves a "reset" stage in G2, where an increased cytoplasmic microtubule accumulation transiently disrupts CMT alignment with tissue stress.

Self-organization underlies developmental robustness in plants.

Development is a self-organized process that builds on cells and their interactions. Cells are heterogeneous in gene expression, growth, and division; yet how development is robust despite such heterogeneity is a fascinating question. Here, we review recent progress on this topic, highlighting how developmental robustness is achieved through self-organization. We will first discuss sources of heterogeneity, including stochastic gene expression, heterogeneity in growth rate and direction, and heterogeneity in division rate and precision. We then discuss cellular mechanisms that buffer against such noise, including Paf1C- and miRNA-mediated denoising, spatiotemporal growth averaging and compensation, mechanisms to improve cell division precision, and coordination of growth rate and developmental timing between different parts of an organ. We also discuss cases where such heterogeneity is not buffered but utilized for development. Finally, we highlight potential directions for future studies of noise and developmental robustness.

Learning feature relationships in CNN model via relational embedding convolution layer.

Establishing the relationships among hierarchical visual attributes of objects in the visual world is crucial for human cognition. The classic convolution neural network (CNN) can successfully extract hierarchical features but ignore the relationships among features, resulting in shortcomings compared to humans in areas like interpretability and domain generalization. Recently, algorithms have introduced feature relationships by external prior knowledge and special auxiliary modules, which have been proven to bring multiple improvements in many computer vision tasks. However, prior knowledge is often difficult to obtain, and auxiliary modules bring additional consumption of computing and storage resources, which limits the flexibility and practicality of the algorithm. In this paper, we aim to drive the CNN model to learn the relationships among hierarchical deep features without prior knowledge and consumption increasing, while enhancing the fundamental performance of some aspects. Firstly, the task of learning the relationships among hierarchical features in CNN is defined and three key problems related to this task are pointed out, including the quantitative metric of connection intensity, the threshold of useless connections, and the updating strategy of relation graph. Secondly, Relational Embedding Convolution (RE-Conv) layer is proposed for the representation of feature relationships in convolution layer, followed by a scheme called use & disuse strategy which aims to address the three problems of feature relation learning. Finally, the improvements brought by the proposed feature relation learning scheme have been demonstrated through numerous experiments, including interpretability, domain generalization, noise robustness, and inference efficiency. In particular, the proposed scheme outperforms many state-of-the-art methods in the domain generalization community and can be seamlessly integrated with existing methods for further improvement. Meanwhile, it maintains comparable precision to the original CNN model while reducing floating point operations (FLOPs) by approximately 50%.