Ronin Institute
  • Montclair, United States
Recent publications
Background Several health institutions developed strategies to improve health content on Wikimedia platforms given their unparalleled reach. The objective of this study was to compare an online volunteer-based Wikimedia outreach campaign with university course Wikipedia assignments (both focused on improving hearing health content in Wikimedia’s public digital knowledge archives), in terms of the reach of the contributions and the extent of the participants’ input. A secondary objective was to examine the feasibility and the implementation of the different strategies. Methods The research team partnered for the (1) coordination of improvements in hearing and healthcare content through educational programs using Wikimedia platforms, (2) participation in the global campaign Wiki4WorldHearingDay2023 and (3) evaluation of the proposed strategies. Metrics used in the comparison of the two strategies included the number of articles edited, number of views of the edited articles (as reach) and the extent of edits, captured as the number of words. The feasibility evaluation included assessing recruitment success and the implementation of the proposed plan among faculty, students from various university programs, and volunteers representing different constituencies. Results The effort increased the availability of quality plain language information on hearing conditions and hearing care. Both strategies demonstrated to be feasible by their success in recruiting participants who contributed to the effort and by measurable outputs as edits. The contribution of content to Wikimedia platforms as part of education activities provided a more robust result. Wiki4WorldHearingDay2023 145 participants (78 from educational programs) contributed 167,000 words, 258 + references and 140 images to 322 Wikipedia articles (283 existing and 39 new ones), which were viewed 16.5 million times. Contributions occurred in six languages. Edits in Portuguese, mainly by those involved in educational programs, led the number of articles (226 or 70.2%) that were expanded or created during the 5-month tracking period. Conclusions The elements that contributed to the success of the studied strategies include an impact topic, coordination with educational programs, international multidisciplinary collaborations, the dissemination of the initiative in several platforms, connection with a robust local Wikimedia affiliate, and the use of a technical infrastructure that provides metrics and coordination mechanisms. Graphical abstract
Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape, necessitating the development of more sophisticated detection methods. Traditional machine learning approaches to phishing detection have relied heavily on feature engineering and have often fallen short in adapting to the dynamically changing patterns of phishing Uniform Resource Locator (URLs). Addressing these challenge, we introduce a framework that integrates the sequential data processing strengths of a Recurrent Neural Network (RNN) with the hyperparameter optimization prowess of the Whale Optimization Algorithm (WOA). Our model capitalizes on an extensive Kaggle dataset, featuring over 11,000 URLs, each delineated by 30 attributes. The WOA’s hyperparameter optimization enhances the RNN’s performance, evidenced by a meticulous validation process. The results, encapsulated in precision, recall, and F1-score metrics, surpass baseline models, achieving an overall accuracy of 92%. This study not only demonstrates the RNN’s proficiency in learning complex patterns but also underscores the WOA’s effectiveness in refining machine learning models for the critical task of phishing detection.
Secondary contact between closely related taxa represents a “moment of truth” for speciation—an opportunity to test the efficacy of reproductive isolation that evolved in allopatry and to identify the genetic, behavioral, and/or ecological barriers that separate species in sympatry. Sex chromosomes are known to rapidly accumulate differences between species, an effect that may be exacerbated for neo-sex chromosomes that are transitioning from autosomal to sex-specific inheritance. Here we report that, in the Solomon Islands, two closely related bird species in the honeyeater family—Myzomela cardinalis and Myzomela tristrami—carry neo-sex chromosomes and have come into recent secondary contact after ~1.1 my of geographic isolation. Hybrids of the two species were first observed in sympatry ~100 years ago. To determine the genetic consequences of hybridization, we use population genomic analyses of individuals sampled in allopatry and in sympatry to characterize gene flow in the contact zone. Using genome-wide estimates of diversity, differentiation, and divergence, we find that the degree and direction of introgression varies dramatically across the genome. For sympatric birds, autosomal introgression is bidirectional, with phenotypic hybrids and phenotypic parentals of both species showing admixed ancestry. In other regions of the genome, however, the story is different. While introgression on the Z/neo-Z-linked sequence is limited, introgression of W/neo-W regions and mitochondrial sequence (mtDNA) is highly asymmetric, moving only from the invading M. cardinalis to the resident M. tristrami. The recent hybridization between these species has thus enabled gene flow in some genomic regions but the interaction of admixture, asymmetric mate choice, and/or natural selection has led to the variation in the amount and direction of gene flow at sex-linked regions of the genome.
There is such a vast proliferation of scientific theories of consciousness that it is worrying some scholars. There are even competitions to test different theories, and the results are inconclusive. Consciousness research, far from converging toward a unifying framework, is becoming more discordant than ever, especially with respect to theoretical elements that do not have a clear neurobiological basis. Rather than dueling theories, an integration across theories is needed to facilitate a comprehensive view on consciousness and on how normal nervous system dynamics can develop into pathological states. In dealing with what is considered an extremely complex matter, we try to adopt a perspective from which the subject appears in relative simplicity. Grounded in experimental and theoretical observations, we advance an encompassing biophysical theory, MaxCon, which incorporates aspects of several of the main existing neuroscientific consciousness theories, finding convergence points in an attempt to simplify and to understand how cellular collective activity is organized to fulfill the dynamic requirements of the diverse theories our proposal comprises. Moreover, a computable index indicating consciousness level is presented. Derived from the level of description of the interactions among cell networks, our proposal highlights the association of consciousness with maximization of the number of configurations of neural network connections-constrained by neuroanatomy, biophysics and the environment-that is common to all consciousness theories.
This study introduces a long-short-term memory (LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes, focusing on the critical application of elderly fall detection. It balances the dataset using the Synthetic Minority Over-sampling Technique (SMOTE), effectively neutralizing bias to address the challenge of unbalanced datasets prevalent in time-series classification tasks. The proposed LSTM model is trained on the enriched dataset, capturing the temporal dependencies essential for anomaly recognition. The model demonstrated a significant improvement in anomaly detection, with an accuracy of 84%. The results, detailed in the comprehensive classification and confusion matrices, showed the model’s proficiency in distinguishing between normal activities and falls. This study contributes to the advancement of smart home safety, presenting a robust framework for real-time anomaly monitoring.
There is growing interest in enhancing soil carbon sequestration (SCS) as a climate mitigation strategy, including neutralizing atmospheric emissions from fossil‐fuel combustion through the development of soil carbon offset markets. Several studies have focused on refining estimates of the magnitude of potential SCS or on developing methods for soil carbon quantification in markets. We call on scientists and policy makers to resist assimilating soils into carbon offset markets due to not only fundamental flaws in the logic of these markets to reach climate neutrality but also environmental justice concerns. Here, we first highlight how carbon offset markets rely on an inappropriate substitution of inert fossil carbon with dynamic stocks of soil carbon. We then note the failure of these markets to account for intersecting anthropogenic perturbations to the carbon cycle, including the soil carbon debt and ongoing agricultural emissions. Next, we invite scientists to consider soil functions beyond productivity and profitability. Finally, we describe and support historical opposition to offset markets by environmental justice advocates. We encourage scientists to consider how their research and communications can promote diverse soil functions and just climate‐change mitigation.
Organic matter accumulation in soil is understood as the result of the dynamics between mineral‐associated (more decomposed, microbial derived) organic matter and free particulate (less decomposed, plant derived) organic matter. However, from regional to global scales, patterns and drivers behind main soil organic carbon (SOC) fractions are not well understood and remain poorly linked to the pedogenetic variation across soil types. Here, we separated SOC associated with silt‐ and clay‐sized particles (S + C), stable aggregates (>63 μm, SA) and particulate organic matter (POM) from a diverse range of grassland topsoils sampled along a geoclimatic gradient. The relative contribution of the two mineral‐associated fractions (S + C & SA) to SOC differed significantly across the gradient, while POM was never the dominant SOC fraction. Stable aggregates (>63 μm) emerged as the major SOC fraction in carbon‐rich soils. The degree of decomposition of carbon in stable aggregates (>63 μm) was consistently between that of the S + C and POM fractions and did not change along the investigated gradient. In contrast, carbon associated with the S + C fraction was less microbially decomposed in carbon‐rich soils than in carbon‐poor soils. The amount of SOC in the S + C fraction was positively correlated to pedogenic oxide contents and texture, whereas the amount of SOC associated with stable aggregates (>63 μm) was positively correlated to pedogenic oxide contents and negatively to temperature. We present a conceptual summary of our findings, which integrates the role of stable aggregates (>63 μm) with other major SOC fractions and illustrates their changing importance across (soil‐)environmental gradients.
The way organismic agents come to know the world, and the way algorithms solve problems, are fundamentally different. The most sensible course of action for an organism does not simply follow from logical rules of inference. Before it can even use such rules, the organism must tackle the problem of relevance. It must turn ill-defined problems into well-defined ones, turn semantics into syntax. This ability to realize relevance is present in all organisms, from bacteria to humans. It lies at the root of organismic agency, cognition, and consciousness, arising from the particular autopoietic, anticipatory, and adaptive organization of living beings. In this article, we show that the process of relevance realization is beyond formalization. It cannot be captured completely by algorithmic approaches. This implies that organismic agency (and hence cognition as well as consciousness) are at heart not computational in nature. Instead, we show how the process of relevance is realized by an adaptive and emergent triadic dialectic (a trialectic), which manifests as a metabolic and ecological-evolutionary co-constructive dynamic. This results in a meliorative process that enables an agent to continuously keep a grip on its arena, its reality. To be alive means to make sense of one’s world. This kind of embodied ecological rationality is a fundamental aspect of life, and a key characteristic that sets it apart from non-living matter.
Structural biology experiments benefit significantly from state‐of‐the‐art synchrotron data collection. One can acquire macromolecular crystallography (MX) diffraction data on large‐area photon‐counting pixel‐array detectors at framing rates exceeding 1000 frames per second, using 200 Gbps network connectivity, or higher when available. In extreme cases this represents a raw data throughput of about 25 GB s⁻¹, which is nearly impossible to deliver at reasonable cost without compression. Our field has used lossless compression for decades to make such data collection manageable. Many MX beamlines are now fitted with DECTRIS Eiger detectors, all of which are delivered with optimized compression algorithms by default, and they perform well with current framing rates and typical diffraction data. However, better lossless compression algorithms have been developed and are now available to the research community. Here one of the latest and most promising lossless compression algorithms is investigated on a variety of diffraction data like those routinely acquired at state‐of‐the‐art MX beamlines.
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Montclair, United States
Head of institution
Jon F. Wilkins