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To better align theories of paradigm shifting discoveries and empirics identifying them, we pro-pose a novel measure that incorporates a discovery impact, novelty, and tendency to break with the past into a single, coherent measure. Calibration using the National Inventor Hall of Fame data reveals that impact, novelty, and disruptiveness are strict complements meaning, for example, that greater impact cannot substitute for moderate novelty. We illustrate the workings of the measure using data on USPTO patents from 1982 to 2015.
We present the Ultracool dwarf Science with MachIne LEarning (USMILE), a program developing machine-learning tools for the discovery and characterization of ultracool dwarfs. We introduce USMILE Avocado, a spectral classification framework that uses broadband photometry from wide-field surveys -- Rubin Observatory LSST Data Preview 1, VISTA Hemisphere Survey, and CatWISE -- as input features. The framework has two gradient-boosted decision-tree models scalable to the massive data volumes of modern surveys: the classifier, which distinguishes ultracool dwarfs from stellar/extragalactic contaminants, and the regressor, which predicts spectral types. A key strength is its ability to natively handle missing photometric features, whereas earlier machine-learning approaches required complete multi-band detections or relied on imputation, thereby excluding genuine ultracool dwarfs or introducing bias. Trained on an augmented labeled dataset of >2 million sources built from known ultracool dwarfs, reddened early-type stars, and quasars, the models achieve strong performance: the classifier attains an ROC AUC of 0.976 and an F1 score of 0.92, while the regressor yields a mean-squared err
Just as the chemical elements from hydrogen (Z = 1) to oganesson (Z = 118) once were discovered, so were the numerous isotopes. The histories of how the isotopes were discovered are less well known, but in a few cases they are as interesting and instructive as those of the elements figuring in the periodic table. Following an overview of criteria usually associated with the concept of discovery in general, this paper examines in detail the historical developments that led to the discoveries of deuterium and tritium and also, as a by-product, the helium-3 isotope. It also includes a brief section on the neutron, which in the 1920s, when it was still a hypothetical particle, was sometimes discussed together with the mass-2 and mass-3 hydrogen isotopes. The paper concludes with a discussion of priority questions relating to suggestions of the two heavy isotopes as well as to their actual discoveries.
Philosophers have spilled much ink over the discovery of ideas in the classical 'context of discovery'. However, there has been little engagement with the question of what constitutes a discovery of 'things in the world'. A much-overlooked answer to this question is provided by T.S. Kuhn. In this paper, I show that discoveries awarded with a Nobel Prize in Physics in the past 53 years accord with a basic premise of Kuhn's account and his distinction between two types of natural kind discoveries. I also draw normative conclusions for credit attribution in science.
We have inspected all supernova discoveries reported during 2010 and 2011 (538 and 926 events, respectively). We examine the statistics of all discovered objects, as well as those of the subset of spectroscopically-confirmed events. In these two years we see the rise of wide-field non-targeted supernova surveys to prominence, with the largest numbers of events reported by the CRTS and PTF surveys (572 and 393 events in total respectively, contributing together 74% of all reported discoveries in 2011), followed by the integrated contribution of numerous amateurs (184 events). Among spectroscopically-confirmed events the PTF (393 events) leads, followed by CRTS (170 events), and amateur discoveries (144 events). Traditional galaxy-targeted surveys, such as LOSS and CHASE, maintain a strong contribution (86 and 61 events, respectively) with high spectroscopic completeness (~90% per cent). It is interesting to note that the community managed to provide substantial spectroscopic follow-up for relatively brighter amateur discoveries (<m>=16.5 mag), but significant less help for fainter (and much more numerous) events promptly released by the CRTS (<m>=18.6 mag). Inspecting di
Public elevation data can support landscape research, environmental interpretation, planning, education, and public engagement, but their practical reuse is often limited by fragmented delivery and specialist processing requirements. This paper presents RO-LiDAR GeoQuickView, an independent, voluntary, and non-commercial Web-GIS initiative for exploring and reusing publicly accessible elevation data in Romania. The platform integrates LiDAR-derived digital terrain models (DTMs) and complementary elevation models of different resolutions, publishes standardized hillshade visualizations for immediate browser access, supports participatory landscape documentation, and provides a spatial index for direct raster retrieval. Its most detailed currently integrated component is the 0.5 m LAKI III Zone A DTM coverage for Caras-Severin, Gorj, Mehedinti, and Dolj counties. LAKI III Zone B, comprising Suceava, Neamt, Bacau, and Vrancea counties, is the next scheduled high-resolution extension. It will be integrated after the public products become available and pass through the same harmonization and quality-control workflow. The platform also incorporates LAKI II and additional public altimetr
New technologies have led to vast troves of large and complex datasets across many scientific domains and industries. People routinely use machine learning techniques to not only process, visualize, and make predictions from this big data, but also to make data-driven discoveries. These discoveries are often made using Interpretable Machine Learning, or machine learning models and techniques that yield human understandable insights. In this paper, we discuss and review the field of interpretable machine learning, focusing especially on the techniques as they are often employed to generate new knowledge or make discoveries from large data sets. We outline the types of discoveries that can be made using Interpretable Machine Learning in both supervised and unsupervised settings. Additionally, we focus on the grand challenge of how to validate these discoveries in a data-driven manner, which promotes trust in machine learning systems and reproducibility in science. We discuss validation from both a practical perspective, reviewing approaches based on data-splitting and stability, as well as from a theoretical perspective, reviewing statistical results on model selection consistency an
The history of science reveals that major discoveries are not predictable. Naively, one might conclude therefore that it is not possible to artificially cultivate an environment that promotes discoveries. I suggest instead that open research without a programmatic agenda establishes a fertile ground for unexpected breakthroughs. Contrary to current practice, funding agencies should allocate a small fraction of their funds to support research in centers of excellence without programmatic reins tied to specific goals.
Ediacaran fossils are now largely known in different parts of the world. However, some countries are poorly documented on these remains of a still enigmatic life. Thus, rare fossils from the Neoproterozoic Histria Formation of central Dobrogea (Romania) have been reported. Two specimens with discoid imprints are described here in detail and assigned to the typical Ediacaran species Beltanelliformis brunsae Menner in Keller et al., 1974. This paleontological development confirms both the large geographical distribution of this species and the Ediacaran age of the Histria Formation. KEY WORDS Flat discoid imprints, Ediacaran biota, Precambrian, Dobrogea, Romania. R{É}SUM{É}
The VERITAS array of 12-m atmospheric-Cherenkov telescopes is used in an intensive observation program focused on the discovery of VHE (E > 100 GeV) gamma-ray-emitting blazars. Since VERITAS began full-scale operation in 2007, more than 1000 hours of observation, on ~90 targets, have been devoted to the VHE blazar discovery effort and to timely follow-up in multi-wavelength (MWL) observation campaigns. These data have resulted in the discovery of VHE emission from 10 blazars (6 HBLs and 4 IBLs). A summary of these VHE discoveries is presented.
This paper critically examines the models of scientific discovery propounded by Kuhn, McArthur, Hudson, and Schindler. As an alternative, we proffer the $\unicode{x201c}$change$\unicode{x2013}$driver model$\unicode{x201c}$. It conceives of discoveries as problems or solutions to problems that have epistemically advanced science. Here we take a problem to be generated by a datum that we want to account for and make sense of$\unicode{x2013}$by putting it in contact with our wider web of scientific knowledge and understanding. The model overcomes the shortcomings of its precursors, whilst preserving their insights. We demonstrate its intensional and extensional superiority, especially with respect to the link between scientific discoveries and the dynamics of science. Both as an illustration, and as an application to a recent scientific and political controversy, we apply the considered models of discovery to one of the most momentous discoveries of science: the expansion of the universe.
Presently about 3000 different nuclei are known with about another 3000-4000 predicted to exist. A review of the discovery of the nuclei, the present status and the possibilities for future discoveries are presented.
The 2015 update of the discovery of nuclide project is presented. Twenty new nuclides were observed for the first time in 2015. An overall review of all previous assignments was made in order to apply the discovery criteria consistently to all elements. In addition, a list of isotopes published so far only in conference proceedings or internal reports is included.
The intricate relationship between genetic variation and human diseases has been a focal point of medical research, evidenced by the identification of risk genes regarding specific diseases. The advent of advanced genome sequencing techniques has significantly improved the efficiency and cost-effectiveness of detecting these genetic markers, playing a crucial role in disease diagnosis and forming the basis for clinical decision-making and early risk assessment. To overcome the limitations of existing databases that record disease-gene associations from existing literature, which often lack real-time updates, we propose a novel framework employing Large Language Models (LLMs) for the discovery of diseases associated with specific genes. This framework aims to automate the labor-intensive process of sifting through medical literature for evidence linking genetic variations to diseases, thereby enhancing the efficiency of disease identification. Our approach involves using LLMs to conduct literature searches, summarize relevant findings, and pinpoint diseases related to specific genes. This paper details the development and application of our LLM-powered framework, demonstrating its p
Carbon emissions have become a specific alarming indicators and intricate challenges that lead an extended argue about climate change. The growing trend in the utilization of fossil fuels for the economic progress and simultaneously reducing the carbon quantity has turn into a substantial and global challenge. The aim of this paper is to examine the driving factors of CO$_2$ emissions from energy sector in Romania during the period 2008-2022 emissions using the log mean Divisia index (LMDI) method and takes into account five items: CO$_2$ emissions, primary energy resources, energy consumption, gross domestic product and population, the driving forces of CO$_2$ emissions, based on which it was calculated the contribution of carbon intensity, energy mixes, generating efficiency, economy, and population. The results indicate that generating efficiency effect -90968.57 is the largest inhibiting index while economic effect is the largest positive index 69084.04 having the role of increasing CO$_2$ emissions.
The 2013 update of the discovery of nuclide project is presented. Details of the 12 new nuclides observed for the first time in 2013 are described. In addition, the discovery of 266Db has been included and the previous assignments of 6 other nuclides were changed. Overview tables of where and how nuclides were discovered have also been updated and are discussed.
The paper surveys automated scientific discovery, from equation discovery and symbolic regression to autonomous discovery systems and agents. It discusses the individual approaches from a "big picture" perspective and in context, but also discusses open issues and recent topics like the various roles of deep neural networks in this area, aiding in the discovery of human-interpretable knowledge. Further, we will present closed-loop scientific discovery systems, starting with the pioneering work on the Adam system up to current efforts in fields from material science to astronomy. Finally, we will elaborate on autonomy from a machine learning perspective, but also in analogy to the autonomy levels in autonomous driving. The maximal level, level five, is defined to require no human intervention at all in the production of scientific knowledge. Achieving this is one step towards solving the Nobel Turing Grand Challenge to develop AI Scientists: AI systems capable of making Nobel-quality scientific discoveries highly autonomously at a level comparable, and possibly superior, to the best human scientists by 2050.
Recent discoveries suggest that our gut microbiome plays an important role in our health and wellbeing. However, the gut microbiome data are intricate; for example, the microbial diversity in the gut makes the data high-dimensional. While there are dedicated high-dimensional methods, such as the lasso estimator, they always come with the risk of false discoveries. Knockoffs are a recent approach to control the number of false discoveries. In this paper, we show that knockoffs can be aggregated to increase power while retaining sharp control over the false discoveries. We support our method both in theory and simulations, and we show that it can lead to new discoveries on microbiome data from the American Gut Project. In particular, our results indicate that several phyla that have been overlooked so far are associated with obesity.
Models specified by low-rank matrices are ubiquitous in contemporary applications. In many of these problem domains, the row/column space structure of a low-rank matrix carries information about some underlying phenomenon, and it is of interest in inferential settings to evaluate the extent to which the row/column spaces of an estimated low-rank matrix signify discoveries about the phenomenon. However, in contrast to variable selection, we lack a formal framework to assess true/false discoveries in low-rank estimation; in particular, the key source of difficulty is that the standard notion of a discovery is a discrete one that is ill-suited to the smooth structure underlying low-rank matrices. We address this challenge via a geometric reformulation of the concept of a discovery, which then enables a natural definition in the low-rank case. We describe and analyze a generalization of the Stability Selection method of Meinshausen and Bühlmann to control for false discoveries in low-rank estimation, and we demonstrate its utility compared to previous approaches via numerical experiments.
Neutrino mass and mixing are amongst the major discoveries of recent years. From the observation of flavor change in solar and atmospheric neutrino experiments to the measurements of neutrino mixing with terrestrial neutrinos, recent experiments have provided consistent and compelling evidence for the mixing of massive neutrinos. The discoveries at Super-Kamiokande, SNO, and KamLAND have solved the long-standing solar neutrino problem and demand that we make the first significant revision of the Standard Model in decades. Searches for neutrinoless double-beta decay probe the particle nature of neutrinos and continue to place limits on the effective mass of the neutrino. Possible signs of neutrinoless double-beta decay will stimulate neutrino mass searches in the next decade and beyond. I review the recent discoveries in neutrino physics and the current evidence for massive neutrinos.