Rich Model Research Articles

Mapping breeding bird species richness at management‐relevant resolutions across the United States

Human activities alter ecosystems everywhere, causing rapid biodiversity loss and biotic homogenization. These losses necessitate coordinated conservation actions guided by biodiversity and species distribution spatial data that cover large areas yet have fine-enough resolution to be management-relevant (i.e., ≤5km). However, most biodiversity products are too coarse for management or are only available for small areas. Furthermore, many maps generated for biodiversity assessment and conservation do not explicitly quantify the inherent tradeoff between resolution and accuracy when predicting biodiversity patterns. Our goals were to generate predictive models of overall breeding bird species richness and species richness of different guilds based on nine functional or life-history-based traits across the conterminous United States at three resolutions (0.5, 2.5, and 5km) and quantify the tradeoff between resolution and accuracy and, hence, relevance for management of the resulting biodiversity maps. We summarized 18 years of North American Breeding Bird Survey data (1992-2019) and modeled species richness using random forests, including 66 predictor variables (describing climate, vegetation, geomorphology, and anthropogenic conditions), 20 of which we newly derived. Among the three spatial resolutions, the percentage variance explained ranged from 27% to 60% (median=54%; mean=57%) for overall species richness and 12% to 87% (median=61%; mean=58%) for our different guilds. Overall species richness and guild-specific species richness were best explained at 5-km resolution using ~24 predictor variables based on percentage variance explained, symmetric mean absolute percentage error, and root mean square error values. However, our 2.5-km-resolution maps were almost as accurate and provided more spatially detailed information, which is why we recommend them for most management applications. Our results represent the first consistent, occurrence-based, and nationwide maps of breeding bird richness with a thorough accuracy assessment that are also spatially detailed enough to inform local management decisions. More broadly, our findings highlight the importance of explicitly considering tradeoffs between resolution and accuracy to create management-relevant biodiversity products for large areas.

Understanding the Imperfection of 3D point Cloud and Semantic Segmentation algorithms for 3D Models of Indoor Environment

Abstract. Point clouds data provides new potentials for automated construction of more geometrically accurate and semantically rich 3D models for indoor environments. Recent advances in deep learning methods on point cloud semantic segmentation demonstrated impressive accuracy in labeling points of 3D surfaces with object classes. However, it remains challenging to reconstruct the shape of semantic objects from semantically-labeled 3D points, due to imperfection of such data and the under-determination of object construction algorithms. We have little empirical knowledge about how data imperfections affect the reconstruction of 3D indoor room objects. This paper contributes to understanding the nature of such imperfection of 3D point cloud data and semantic segmentation algorithms by analyzing the reconstructability of indoor room objects from semantically-labeled point cloud. 181 rooms from Stanford Large-Scale 3D Indoor Spaces Dataset (S3DIS) were used in our experiment. After generating semantic labels on point-clouds using PointNet++ segmentic segmentation algorithm, we use human coders to judge the reconstructability of indoor objects, following a qualitative coding scheme. Human exploration of object shape imperfection was assisted by a visual analytic tool in making their judgement. We found that high point-level accuracy achieved through semantic segmentation of point cloud data does not guarantee high object-level accuracy. The extent of this problem varies widely among different spatial settings and configurations. We discuss the significance of these findings on the choice of 3D reconstruction methods.

Host-parasite relationships in invasive species: macroecological framework

Despite considerable research effort, many aspects of the host-parasite relationships and parasite spatial distribution in invasive hosts remain poorly understood due to complex and context-dependent phenomena related to both the bioinvasions and the parasitism. Using macroecological patterns and theory is a useful approach to analyzing parasitological observations, but in practice parasite ecology and classical macroecology are disconnected. We propose a new framework that can use the conventional parasitology sampling data much more effectively. The innovative concept combines the data inferred from populations, infra- and component communities of parasites and the application of a macroecological approach in the analysis of complex and frequently hidden relationships in host-parasite systems. This comparative analysis draws on parasite data across regions and host species at different organizational (population vs. community) and hierarchical (infra vs. component community) levels of parasites. Our framework based on assessing and analysis of parasitological and ecological indexes, including descriptors of parasite species richness (individual and total), infection parameters, parasite aggregation (Taylor´s power law) and macroecological models (abundance-variance and abundance-occupancy relationships), can produce mechanistic explanations of the Enemy Release Hypothesis and unravel host-parasite relationships of an invasive host and its parasites. Moreover, abundance-variance and abundance-occupancy relationships, core-satellite species hypothesis, patterns on the aggregation and the frequency distribution of prevalence, infrapopulation size and individual parasite species richness provide useful tools to distinguish co-introduced and acquired parasites in communities of the invasive host based on quantitative descriptors. We hope that our framework becomes widely applied as it can potentially contribute to enhance future practice and research in biodiversity conservation and control of invasive species.

Building 3D CityGML Model and Digital Management of a Vernacular Architecture

The aim of the paper is to discuss the Levels Of Details (LODs) of 3D city models in order to assess the constructive and semantic characteristics that distinguish each of them. The standard approach used in order to build the 3D model is the CityGML 3.0 Conceptual Model. A key role for the development of the CityGML Conceptual Model is to provide a common definition of the basic entities, attributes, and relations of a semantically rich 3D city model. This approach is important with respect to the cost-effective sustainable maintenance of 3D city models, allowing the reuse of the same data in different application fields. In this context, we have developed a method to build a 3D CityGML model using SketchUP software and some of the tools implemented in it. In addition, the constructed model was displayed and validated by using an open source software (FZK viewer). The building taken into consideration is a vernacular dwelling structure called “trullo” (plural “trulli”) and present in the Apulia region, Italy. “Trulli” are extraordinary examples of dry stone construction with conical or pyramid-shaped roof made with slabs, a technique dating back to prehistoric times and still in use today in Apulia. Before building the 3D model, it was necessary to perform the 3D survey of the building; this task was carried out by using Terrestrial Laser Scanner (TLS) that allowed a realistic and detailed reconstruction of the structures. From the point cloud, a special procedure was developed in order to build CityGML models in several LODs.

Emergence of associative learning in a neuromorphic inference network

Objective. In the theoretical framework of predictive coding and active inference, the brain can be viewed as instantiating a rich generative model of the world that predicts incoming sensory data while continuously updating its parameters via minimization of prediction errors. While this theory has been successfully applied to cognitive processes—by modelling the activity of functional neural networks at a mesoscopic scale—the validity of the approach when modelling neurons as an ensemble of inferring agents, in a biologically plausible architecture, remained to be explored. Approach. We modelled a simplified cerebellar circuit with individual neurons acting as Bayesian agents to simulate the classical delayed eyeblink conditioning protocol. Neurons and synapses adjusted their activity to minimize their prediction error, which was used as the network cost function. This cerebellar network was then implemented in hardware by replicating digital neuronal elements via a low-power microcontroller. Main results. Persistent changes of synaptic strength—that mirrored neurophysiological observations—emerged via local (neurocentric) prediction error minimization, leading to the expression of associative learning. The same paradigm was effectively emulated in low-power hardware showing remarkably efficient performance compared to conventional neuromorphic architectures. Significance. These findings show that: (a) an ensemble of free energy minimizing neurons—organized in a biological plausible architecture—can recapitulate functional self-organization observed in nature, such as associative plasticity, and (b) a neuromorphic network of inference units can learn unsupervised tasks without embedding predefined learning rules in the circuit, thus providing a potential avenue to a novel form of brain-inspired artificial intelligence.

How far damming induced wetland fragmentation and water richness change affect wetland ecosystem services?

In inland wetland, river and lakes, the ecosystem service values (ESV) vary from 4267 to 25682 US$/ha/year, but when the landscape gets fragmented and becomes hydrologically inconsistent, will they produce the same ESV? With this fundamental query, the present work evaluated the ESV in different fragmented wetland units and over different water richness zones in reference to hydrological alteration introduced by damming (1992) over Punarbhaba river (a transboundary of India and Bangladesh). The wetlands were mapped using the support vector machine (SVM) model for pre and post-dam conditions. Subsequently, satellite image-derived parameters, such as hydro-period, water depth, and water presence consistency, were prepared and used for spatial weighted and fuzzy logic-based water richness modelling and wetland fragmentation analysis for both pre (1989) and post-dam periods (2019). Results showed that wetland area was reduced from 205.24 km2 to 73.22 km2 in between pre-and post-dam periods, and consequently, the ESV was reduced from 10596 × 104 US$ to 3874 × 104 US$. Damming effect has decreased the high-water richness area from 71.83% to 7.65%, which caused a 62% of ESV reduction in the post-dam period. Due to increasing patch frequency and decreasing the core area in the post-dam period, the ESV value was reduced by 45%, which could be considered the fragmentation effect. As the study identified the areas with decreasing ESV values, this work has very good policy implications on a priority basis for wetland conservation.

Cointeracting dark matter and conformally coupled light scalars

The increasing observational pressure on the standard cosmological model motivates analyses going beyond the paradigm of the collisionless cold dark matter (DM). Since the only clear evidence for the existence of DM is based on gravitational interactions, it seems particularly fitting to study them in this sector where extensions to the standard model can be naturally introduced. A promising avenue can be obtained using modifications of the space-time metric coupled to DM and induced by the presence of a new ultralight scalar field $\ensuremath{\phi}$. The $\ensuremath{\phi}$ field can contribute to the DM density and can couple all the matter species universally, including additional heavy DM particles. We present a simple two-component DM model employing derivative conformal interactions between the two DM species. This can simultaneously (1) guarantee the necessary symmetries to stabilize the dark species, (2) predict a subdominant thermal relic density of the heavy DM component, and (3) alleviate small-scale structure tensions of the cold DM scenario due to the possible cointeractions in the dark sector. The scenario is highly predictive with future observational prospects ranging from the Large Hadron Collider to gravitational-wave searches and can be generalized to more rich and realistic dark sector models.

Explaining the effectiveness of fear extinction through latent-cause inference

Acquiring fear responses to predictors of aversive outcomes is crucial for survival. At the same time, it is important to be able to modify such associations when they are maladaptive, for instance in treating anxiety and trauma-related disorders. Standard extinction procedures can reduce fear temporarily, but with sufficient delay or with reminders of the aversive experience, fear often returns. The latent-cause inference framework explains the return of fear by presuming that animals learn a rich model of the environment, in which the standard extinction procedure triggers the inference of a new latent cause, preventing the unlearning of the original aversive associations. This computational framework had previously inspired an alternative extinction paradigm – gradual extinction – which indeed was shown to be more effective in reducing the return of fear. However, the original framework was not sufficient to explain the pattern of results seen in the experiments. Here, we propose a formal model to explain the effectiveness of gradual extinction in reducing spontaneous recovery and reinstatement effects, in contrast to the ineffectiveness of standard extinction and a gradual reverse control procedure. We demonstrate through quantitative simulation that our model can explain qualitative behavioral differences across different extinction procedures as seen in the empirical study. We verify the necessity of several key assumptions added to the latent-cause framework, which suggest potential general principles of animal learning and provide novel predictions for future experiments.

A triad of kicknet sampling, eDNA metabarcoding, and predictive modeling to assess richness of mayflies, stoneflies and caddisflies in rivers

Monitoring biodiversity is essential to understand the impacts of human activities and for effective management of ecosystems. Thereby, biodiversity can be assessed through direct collection of targeted organisms, through indirect evidence of their presence (e.g. signs, environmental DNA, camera trap, etc.), or through extrapolations from species distribution and species richness models. Differences in approaches used in biodiversity assessment, however, may come with individual challenges and hinder cross-study comparability. In the context of rapidly developing techniques, we compared three different approaches in order to better understand assessments of aquatic macroinvertebrate diversity. Specifically, we compared the community composition and species richness of three orders of aquatic macroinvertebrates (mayflies, stoneflies, and caddisflies, hereafter EPT) obtained via eDNA metabarcoding and via traditional in situ kicknet sampling to catchment-level based predictions of a species richness model. We used kicknet data from 24 sites in Switzerland and compared taxonomic lists to those obtained using eDNA amplified with two different primer sets. Richness detected by these methods was compared to the independent predictions made by a statistical species richness model, that is, a generalized linear model using landscape-level features to estimate EPT diversity. Despite the ability of eDNA to consistently detect some EPT species found by traditional sampling, we found important discrepancies in community composition between the kicknet and eDNA approaches, particularly at a local scale. We found the EPT-specific primer set fwhF2/EPTDr2n, detected a greater number of targeted EPT species compared to the more general primer set mlCOIintF/HCO2198. Moreover, we found that the species richness measured by eDNA from either primer set was poorly correlated to the richness measured by kicknet sampling (Pearson correlation = 0.27) and that the richness estimated by eDNA and kicknet were poorly correlated with the prediction of the species richness model (Pearson correlation = 0.30 and 0.44, respectively). The weak relationships between the traditional kicknet sampling and eDNA with this model indicates inherent limitations in upscaling species richness estimates, and possibly a limited ability of the model to meet real world expectations. It is also possible that the number of replicates was not sufficient to detect ambiguous correlations. Future challenges include improving the accuracy and sensitivity of each approach individually, yet also acknowledging their respective limitations, in order to best meet stakeholder demands and address the biodiversity crisis we are facing.

Long-Term Study of Owl Occupancy in Protected Areas of El Salvador

ABSTRACT Understanding patterns in the occupancy of Neotropical owls can help inform land managers and conservation efforts; however, Neotropical owls are understudied compared to their temperate kin. We conducted the first long-term (10-yr) study of owls in El Salvador to assess occurrence patterns and species richness using occupancy modeling. Our study occurred in El Imposible National Park, Montecristo National Park, and Nancuchiname Forest, and we chose two survey routes per protected area to represent the landscape diversity of the country. Surveys involved passive listening and broadcasting calls of several species at selected points along each route, and we sought to repeat each survey route twice per year. We completed 86 surveys between March and May each year from 2003 through 2013, except in 2006 when no surveys occurred. We detected nine species of owls, including the Stygian Owl (Asio stygius), which was previously undocumented in El Salvador, and we documented 1211 owl records overall. We developed Bayesian, hierarchical, single-species occupancy models for the Mottled Owl (Ciccaba virgata), Ferruginous Pygmy-Owl (Glaucidium brasilianum), and Spectacled Owl (Pulsatrix perspicillata), and a multi-species richness model for all El Salvador owls. We found that the effects of broadcasting calls were species-specific (i.e., broadcasting some species' calls increased detection probabilities while others decreased detection). Nancuchiname Forest was the most species-rich, while the cloud forest of Montecristo National Park had several species that we did not observe elsewhere. In general, we found relatively stable occupancy patterns across the study period, and we recommend some specific areas of further study to inform management decisions.

Chiral metals and entrapped insulators in a one-dimensional topological non-Hermitian system

In this work, we study many-body ``steady states'' that arise in the non-Hermitian generalization of the noninteracting Su-Schrieffer-Heeger model at a finite density of fermions. We find that the hitherto known phase diagrams for this system, derived from the single-particle gap closings, in fact correspond to distinct nonequilibrium phases, which either carry finite currents or are dynamical insulators where particles are entrapped. Each of these have distinct quasiparticle excitations and steady-state correlations and entanglement properties. Looking at finite-sized systems, we further modulate the boundary to uncover the topological features in such steady states, particularly the emergence of leaky boundary modes. Using a variety of analytical and numerical methods, we develop a theoretical understanding of the various phases and their transitions, and we uncover the rich interplay of nonequilibrium many-body physics, quantum entanglement, and topology in a simple looking yet rich model system.

Multi Images Steganography using Neural Network

Abstract: Currently, the most successful approach to steganography in empirical objects, such as digital media, is to embed the payload while minimizing a suitably defined distortion function. The design of the distortion is essentially the only task left to the stegnographer since efficient practical codes exist that embed near the payload-distortion bound. The ractitioner goal is to design the distortion to obtain a scheme with a high empirical statistical detectability. In this paper, we propose a universal distortion design called universal wavelet relative distortion (UNIWARD) that can be applied for embedding in an arbitrary domain. The embedding distortion is computed as a sum of relative changes of coefficients in a directional filter bank decomposition of the cover image. The directionality forces the embedding changes to such parts of the cover object that are difficult to model in multiple directions, such as textures or noisy regions, while avoiding smooth regions or clean edges. We demonstrate experimentally using rich models as well as targeted attacks that steganographic methods built using UNIWARD match or outperform the current state of the art in the spatial domain, JPEG domain, and sideinformed JPEG domain.

Embodiment in directive sequences: The case of triadic interactions in a Polish-English bilingual family

Aims: This study provides a multimodal conversation analytic account of directive sequences used in the presence of a child aged 1;8-2;4 growing up in an English-dominant environment and acquiring Polish as a heritage language. Design: The video recorded data are drawn from naturally occurring interactions, in which the child is present when one caregiver produces a directive turn (request, proposal, or suggestion) and another one carries it out in an embodied fashion. Analysis: The analysis focuses on the sequential unfolding of the triadic interactions, the two adults’ and child’s verbal turns, gaze direction, manual actions, and handling of objects. Findings: Although the child is not always verbally active in the directive sequences, she observes them and sometimes takes part, either through bodily actions or verbal utterances. The multimodal analysis also shows that even if the child’s verbal activity might indicate understanding a prior turn and responding to it, the child may not actually be orienting to the conversation. Observing adults carrying out heritage language directive sequences is only possible when the child is interacting with two speakers of her heritage language, or a speaker of that language and a person who has some passive knowledge of it. Seeing adults’ mutual social actions offers the child a social environment, in which she can get a rich linguistic model and also observe the benefits of using the heritage language in everyday interactional situations. Originality: This is the first study to offer a multimodal conversation analysis of directive sequences in which one adult produces the directive and another adult responds to it in the presence of a child acquiring a heritage language.

Are Marriage-Related Taxes and Social Security Benefits Holding Back Female Labour Supply?

Abstract In the US, both taxes and old-age social security benefits depend on one’s marital status and tend to reduce the labour supply of the secondary earner. To what extent are these provisions holding back the female labour supply? We estimate a rich dynamic life-cycle model of labour supply and savings for couples and singles using the Method of Simulated Moments for the 1945 and 1955 birth cohorts. Our model matches well the life-cycle profiles of labour market participation, hours, and savings for married and single people, and generates plausible elasticities of labour supply. It implies that eliminating these marriage-related provisions would drastically increase the participation of married women over their entire life cycle, reduce the participation of married men after age 60, and increase savings. If the resulting government surplus were used to lower income taxation, there would be large welfare gains for the vast majority of the population. These results hold for both cohorts, including the later one, which has participation similar to that of more recent generations.

CPRO: Competitive Poor and Rich Optimizer-Enabled Deep Learning Model and Holoentropy Weighted-Power K-Means Clustering for Brain Tumor Classification Using MRI

A brain tumor is a collection of irregular and needless cell development in the brain region, and it is considered a life-threatening disease. Therefore, early level segmentation and brain tumor detection with Magnetic Resonance Imaging (MRI) is more important to save the patient’s life. Moreover, MRI is more effective in identifying patients with brain tumors since the recognition of this modality is moderately larger than considering other imaging modalities. The classification of brain tumors is the most important, difficult task in medical imaging systems because of size, appearance and shape variations. In this paper, Competitive Poor and Rich Optimization (CPRO)-based Deep Quantum Neural Network (Deep QNN) is proposed for brain tumor classification. Additionally, the pre-processing process assists in eradicating noises and uses image intensity to eliminate the artifacts. The significant features are extracted from pre-processed image to perform a productive classification process. The Deep QNN classifier is employed for classifying the brain tumor regions. Besides, the Deep QNN classifier is trained by the developed CPRO approach, which is newly designed by integrating Poor and Rich Optimization (PRO) and Competitive Swarm Optimizer (CSO). The developed brain tumor detection model outperformed other existing models with accuracy, sensitivity and specificity of 94.44%, 97.60% and 93.78%.

Accelerating the Activation of NO x − on Ru Nanoparticles for Ammonia Production by Tuning Their Electron Deficiency

Accelerating the Activation of NO _x ⁻ on Ru Nanoparticles for Ammonia Production by Tuning Their Electron Deficiency

An Intelligence Architecture for Grounded Language Communication with Field Robots

For humans and robots to collaborate effectively as teammates in unstructured environments, robots must be able to construct semantically rich models of the environment, communicate efficiently with teammates, and perform sequences of tasks robustly with minimal human intervention, as direct human guidance may be infrequent and/or intermittent. Contemporary architectures for human-robot interaction often rely on engineered human-interface devices or structured languages that require extensive prior training and inherently limit the kinds of information that humans and robots can communicate. Natural language, particularly when situated with a visual representation of the robot’s environment, allows humans and robots to exchange information about abstract goals, specific actions, and/or properties of the environment quickly and effectively. In addition, it serves as a mechanism to resolve inconsistencies in the mental models of the environment across the human-robot team. This article details a novel intelligence architecture that exploits a centralized representation of the environment to perform complex tasks in unstructured environments. The centralized environment model is informed by a visual perception pipeline, declarative knowledge, deliberate interactive estimation, and a multimodal interface. The language pipeline also exploits proactive symbol grounding to resolve uncertainty in ambiguous statements through inverse semantics. A series of experiments on three different, unmanned ground vehicles demonstrates the utility of this architecture through its robust ability to perform language-guided spatial navigation, mobile manipulation, and bidirectional communication with human operators. Experimental results give examples of component-level behaviors and overall system performance that guide a discussion on observed performance and opportunities for future innovation.

Possible Futures for Network Psychometrics

This commentary reflects on the articles included in the Psychometrika Special Issue on Network Psychometrics in Action. The contributions to the special issue are related to several possible future paths for research in this area. These include the development of models to analyze and represent interventions, improvement in exploratory and inferential techniques in network psychometrics, the articulation of psychometric theories in addition to psychometric models, and extensions of network modeling to novel data sources. Finally, network psychometrics is part of a larger movement in psychology that revolves around the analysis of human beings as complex systems, and it is timely that psychometricians start extending their rich modeling tradition to improve and extend the analysis of systems in psychology.

Using two case studies to explore the applicability of VIATRA for the model-driven engineering of mechatronic production systems

The engineering of mechatronic production systems is complex and requires various disciplines (e.g., systems, mechanical, electrical and software engineers). Model-driven engineering (MDE) supports systems development and the exchange of information based on models and transformations. However, the integration and adoption of different modeling approaches are becoming challenges when it comes to cross-disciplinary work. VIATRA is a long-living enduring and mature modeling framework that offers rich model transformation features to develop MDE applications. This study investigates the extent to which VIATRA can be applied in the engineering of mechatronic production systems. For this purpose, two model transformation case studies are presented: “SysML – AutomationML” and “SysML4Mechatronics – AutomationML.” Both case studies are representative of structural modeling and interdisciplinary data exchange during the development of mechatronic production systems. These case studies are derived from other researchers in the community. A VIATRA software prototype implements these case studies as a batch-oriented transformation and serves as one basis for evaluating VIATRA. To report on our observations and findings, we built on an evaluation framework from the MDE community. This framework considers 14 different characteristics (e.g., maturity, size, execution time, modularity, learnability), according to the Goal-Question-Metric paradigm. To be able to evaluate our findings, we compared VIATRA to ATL. We applied all cases to a lab-size mechatronic production system. We found that, with VIATRA, the same functions for model transformation applications can be achieved as with ATL, which is popular for model transformations in both the MDE and the mechatronic production systems community. VIATRA combines the relational, imperative, and graph-based paradigms and enables the development and execution of model-to-model (M2M) and model-to-text (M2T) transformations. Furthermore, the VIATRA internal DSL is based on Xtend and Java, making VIATRA attractive and intuitive for users with less experience in modeling than in object-oriented programming. Thus, VIATRA leads to an interesting alternative for the model-driven engineering of mechatronic production systems. It has the potential to reduce the complexity during the development of model transformations. To conclude, this paper evaluates the applicability of VIATRA, its strengths and limitations. It provides lessons learned and insights that can stimulate further research in the MDE for mechatronic production systems.

CHEER: Rich Model Helps Poor Model via Knowledge Infusion.

There is a growing interest in applying deep learning (DL) to healthcare, driven by the availability of data with multiple feature channels in rich-data environments (e.g., intensive care units). However, in many other practical situations, we can only access data with much fewer feature channels in a poor-data environments (e.g., at home), which often results in predictive models with poor performance. How can we boost the performance of models learned from such poor-data environment by leveraging knowledge extracted from existing models trained using rich data in a related environment? To address this question, we develop a knowledge infusion framework named CHEER that can succinctly summarize such rich model into transferable representations, which can be incorporated into the poor model to improve its performance. The infused model is analyzed theoretically and evaluated empirically on several datasets. Our empirical results showed that CHEER outperformed baselines by 5.60% to 46.80% in terms of the macro-F1 score on multiple physiological datasets.