搜索结果：Acta radiologica open

BACKGROUND: Although radiological pneumonia is used as an outcome measure in epidemiological studies, there is considerable variability in the interpretation of chest radiographs. A standardized method for identifying radiological pneumonia would facilitate comparison of the results of vaccine trials and epidemiological studies of pneumonia. METHODS: A WHO working group developed definitions for radiological pneumonia. Inter-observer variability in categorizing a set of 222 chest radiographic images was measured by comparing the readings made by 20 radiologists and clinicians with a reference reading. Intra-observer variability was measured by comparing the initial readings of a randomly chosen subset of 100 radiographs with repeat readings made 8-30 days later. FINDINGS: Of the 222 images, 208 were considered interpretable. The reference reading categorized 43% of these images as showing alveolar consolidation or pleural effusion (primary end-point pneumonia); the proportion thus categorized by each of the 20 readers ranged from 8% to 61%. Using the reference reading as the gold standard, 14 of the 20 readers had sensitivity and specificity of > 0.70 in identifying primary end-point pneumonia; 13 out of 20 readers had a kappa index of > 0.6 compared with the reference reading. For the 92 radiographs deemed to be interpretable among the 100 images used for intra-observer variability, 19 out of 20 readers had a kappa index of > 0.6. CONCLUSION: Using standardized definitions and training, it is possible to achieve agreement in identifying radiological pneumonia, thus facilitating the comparison of results of epidemiological studies that use radiological pneumonia as an outcome.

With this issue of Acta Radiologica, I am happy to announce and present to all of our readers and partners, the new partnership between Acta Radiologica and Royal Society of Medicine Press (RSMP). Both Acta Radiologica and RSMP have long historic traditions going back in time. The origin of the Royal Society of Medicine goes back to the 18th century when medical societies were beginning to be founded in Europe with the object of bringing together physicians and surgeons in order to promote further scientific, professional and social communication. The first general medical society of note in England was the Medical Society of London, founded in 1773. On May 22, 1805 a new medical society, The Medical and Chirurgical Society of London, was founded. This society was destined to be the progenitor of the present Royal Society of Medicine. This society was founded ‘for the purpose of conversation on professional subjects, for the reception of communications and for the formation of a library’. For the first 100 years or so the Society was located at different places in London. However, the RSM acquired the site on the corner of Wimpole Street and Henrietta Place in 1910, just behind Oxford Street. King George V and Queen Mary opened the final home of the Royal Society of Medicine at 1 Wimpole Street in May 1912. Over the years the RSM has had several famous Fellows and Presidents. Honorary Fellows have included Darwin, Pasteur, Jenner, and Freud. Several of the past presidents also have diseases named after them: Bright (1837), Addison (1849), Hodgson (1851), Paget (1875), and Pavy (1900). The RSM has one of the largest medical libraries in Europe now exceeding half a million volumes going back to the 15th century. I spent half a day walking in this library, hours that are highly recommended for anyone interested in medical history. Acta Radiologica was founded in 1921 by the famous Swedish radiologist Gosta Forssell who was the founding and first chief editor of the journal from 1921 to his death in 1950. The journal was established as a partnership of all Nordic radiological societies, but in fact the Administration was owned privately by Gosta Forssell until the Foundation Acta Radiologica was established in 1939. Since then, the journal has continuously published radiological research – even during World War II when distribution of the journal was rather limited. The journal was devoted to both diagnostic and therapeutic research. In 1963 the journal was divided into two series, one on diagnosis and the other one on radiation therapy, physics andbiology. The resultwas thatActaRadiologicahada separate issue for radiation physics and biology in addition to the diagnosis issue. In 1987 a division of the journals was performed resulting in the journal Acta Oncologica in addition to Acta Radiologica. This resulted in two separate journals with separate economy and ownership. During all these years radiology has had an enormous, almost unbelievable expansionwith a row of newmodalities, which have completely transformed the specialty during the last 30 years. Ultrasound, computed tomography, magnetic resonance imaging and now PET-CT have opened up new horizons leading from not only static imaging to 3D imaging, but also into a new era of functional imaging and tests. Dynamic imaging today plays an extremely important role in the clinical use of radiology. Interventional radiology and minimally-invasive procedures have to a large extent replaced complicated surgical procedures with reducedmorbidity, mortality and costs, and often with an increased and better clinical outcome for the patients as a result. Radiology has also taken a major step into translational biomedical research and is an important part in the fast-expanding area of molecular imaging. This process is extremely demanding from both a scientific and economical point of view and has brought up new challenges to the radiological community. Radiology is already one of the most globally-oriented specialties and this fact will steadily increase in the years to come. It is the responsibility of every scientific journal to meet these demands and to take steps leading in this direction. Acta Radiologica has undergone a major change during the last few years by increasing the number of issues from six to ten and almost doubling the number of published articles. The Editorial Board has at the same time been re-organized and extended to meet these new demands. The time has now come for further internationalization and expansion. Our long-term strategy has been to expand to new markets, mainly outside the Nordic countries and Central Europe in order to increase the number of readers and subscribers, and most importantly to attract scientists and researchers to submit high-quality manuscripts to the journal. Acta Radiologica has therefore started the process of inviting distinctive representatives from new countries and markets as members of our Editorial Board as fullmember associate editors. I am therefore glad in the year to come to be able to present new members of our Editorial Board outside the Nordic countries, which hopefully will lead to further globalization of the journal. To be able to fulfill this strategy it is important to be in close collaboration with a publisher that we believe will be able to help us to perform and implement this strategy.

Let $B_1$ be the unit disk in ${\mathbb R}^2$. We consider the harmonic map equation $$ -Δu=| abla u|^2u,$$ subject to the Dirichlet boundary condition $ u(e^{iθ})=(R\cosθ,R\sinθ,\sqrt{1-R^2}):=g_R$, where $0<R<1$ and $u: B_1\to {\mathbb S}^2$ is understood in the weak harmonic-map sense. In 1983, Brezis and Coron proved the existence of two explicit solutions of this nonlinear Dirichlet problem and showed that they are the unique minimizers in their respective relative homotopy classes. In this paper, we resolve a long-standing open question originally posed in their work, later posed as Open Problem 3.1 in Brezis Favorite Open Problems List. Specifically, we prove that these two explicit maps are the only weak harmonic maps with boundary trace $g_{R}$, thereby providing a definitive affirmative answer to Brezis open problem. The proof is based on a boundary rigidity argument. An auxiliary potential $X$ associated with $u$, the Pohozaev identity for the Hopf differential, and the planar isoperimetric inequality imply $$|u_r|\equiv R, \qquad u_r\cdot u_θ\equiv0 \qquad\text{on }\partial B_1. $$ Thus the Hopf differential vanishes on the boundary and hence, by holomorphicity, o

Autonomous medical robots hold promise to improve patient outcomes, reduce provider workload, democratize access to care, and enable superhuman precision. However, autonomous medical robotics has been limited by a fundamental data problem: existing medical robotic datasets are small, single-embodiment, and rarely shared openly, restricting the development of foundation models that the field needs to advance. We introduce Open-H-Embodiment, the largest open dataset of medical robotic video with synchronized kinematics to date, spanning more than 50 institutions and multiple robotic platforms including the CMR Versius, Intuitive Surgical's da Vinci, da Vinci Research Kit (dVRK), Rob Surgical BiTrack, Virtual Incision's MIRA, Moon Surgical Maestro, and a variety of custom systems, spanning surgical manipulation, robotic ultrasound, and endoscopy procedures. We demonstrate the research enabled by this dataset through two foundation models. GR00T-H is the first open foundation vision-language-action model for medical robotics, which is the only evaluated model to achieve full end-to-end task completion on a structured suturing benchmark (25% of trials vs. 0% for all others) and achieves

We introduce open-sci-ref, a family of dense transformer models trained as research baselines across multiple model (0.13B to 1.7B parameters) and token scales (up to 1T) on 8 recent open reference datasets. Evaluating the models on various standardized benchmarks, our training runs set establishes reference points that enable researchers to assess the sanity and quality of alternative training approaches across scales and datasets. Intermediate checkpoints allow comparison and studying of the training dynamics. The established reference baselines allow training procedures to be compared through their scaling trends, aligning them on a common compute axis. Comparison of open reference datasets reveals that training on NemoTron-CC HQ consistently outperforms other reference datasets, followed by DCLM-baseline and FineWeb-Edu. In addition to intermediate training checkpoints, the release includes logs, code, and downstream evaluations to simplify reproduction, standardize comparison, and facilitate future research.

Open effective field theories provide a systematic framework for describing systems coupled to an environment, where dissipation, noise, and modified conservation laws naturally arise. Working within the Schwinger-Keldysh formalism, we examine open extensions of three well-studied theories: the superfluid, Maxwell theory, and Einstein gravity. In gauge and gravitational theories, open terms that break advanced symmetries while preserving physical ones are not automatically consistent; they are allowed only if they lead to deformed identities among the equations of motion. We explicitly construct such a term in open gravity and show that it leads to a consistent deformation of the diffeomorphism identities.

Machine learning (ML) offers a powerful path toward discovering sustainable polymer materials, but progress has been limited by the lack of large, high-quality, and openly accessible polymer datasets. The Open Polymer Challenge (OPC) addresses this gap by releasing the first community-developed benchmark for polymer informatics, featuring a dataset with 10K polymers and 5 properties: thermal conductivity, radius of gyration, density, fractional free volume, and glass transition temperature. The challenge centers on multi-task polymer property prediction, a core step in virtual screening pipelines for materials discovery. Participants developed models under realistic constraints that include small data, label imbalance, and heterogeneous simulation sources, using techniques such as feature-based augmentation, transfer learning, self-supervised pretraining, and targeted ensemble strategies. The competition also revealed important lessons about data preparation, distribution shifts, and cross-group simulation consistency, informing best practices for future large-scale polymer datasets. The resulting models, analysis, and released data create a new foundation for molecular AI in polym

In this work, I collect and discuss a series of open questions in one-dimensional geometric optimization in Euclidean spaces. The focus is on two classes of problems: maximal distance minimizers and Steiner trees. Maximal distance minimizers concern finding a connected set of minimal length whose closed $r$-neighborhood covers a given compact set, whereas Steiner trees aim to find a minimal-length set connecting a prescribed set of points. For both problems, I briefly summarize known results and highlight the remaining open questions. While some questions can be approached with elementary methods, others remain highly challenging.

Deploying robots in open-ended unstructured environments such as homes has been a long-standing research problem. However, robots are often studied only in closed-off lab settings, and prior mobile manipulation work is restricted to pick-move-place, which is arguably just the tip of the iceberg in this area. In this paper, we introduce Open-World Mobile Manipulation System, a full-stack approach to tackle realistic articulated object operation, e.g. real-world doors, cabinets, drawers, and refrigerators in open-ended unstructured environments. The robot utilizes an adaptive learning framework to initially learns from a small set of data through behavior cloning, followed by learning from online practice on novel objects that fall outside the training distribution. We also develop a low-cost mobile manipulation hardware platform capable of safe and autonomous online adaptation in unstructured environments with a cost of around 20,000 USD. In our experiments we utilize 20 articulate objects across 4 buildings in the CMU campus. With less than an hour of online learning for each object, the system is able to increase success rate from 50% of BC pre-training to 95% using online adaptat

Fully open multimodal large language models (MLLMs) currently lag behind proprietary counterparts, primarily due to a significant gap in data quality for supervised fine-tuning (SFT). Existing open-source datasets are often plagued by widespread noise and a critical deficit in complex reasoning data, such as Chain-of-Thought (CoT), which hinders the development of advanced model capabilities. Addressing these challenges, our work makes three primary contributions. First, we introduce Honey-Data-15M, a new SFT dataset comprising approximately 15 million QA pairs, processed through multiple cleaning techniques and enhanced with a novel dual-level (short and long) CoT enrichment strategy. Second, we introduce HoneyPipe, the data curation pipeline, and its underlying framework DataStudio, providing the community with a transparent and adaptable methodology for data curation that moves beyond static dataset releases. Finally, to validate our dataset and pipeline, we train Bee-8B, an 8B model on Honey-Data-15M. Experiments show that Bee-8B establishes a new state-of-the-art (SOTA) for fully open MLLMs, achieving performance that is competitive with, and in some cases surpasses, recent se

In the field of visual scene understanding, deep neural networks have made impressive advancements in various core tasks like segmentation, tracking, and detection. However, most approaches operate on the close-set assumption, meaning that the model can only identify pre-defined categories that are present in the training set. Recently, open vocabulary settings were proposed due to the rapid progress of vision language pre-training. These new approaches seek to locate and recognize categories beyond the annotated label space. The open vocabulary approach is more general, practical, and effective compared to weakly supervised and zero-shot settings. This paper provides a thorough review of open vocabulary learning, summarizing and analyzing recent developments in the field. In particular, we begin by comparing it to related concepts such as zero-shot learning, open-set recognition, and out-of-distribution detection. Then, we review several closely related tasks in the case of segmentation and detection, including long-tail problems, few-shot, and zero-shot settings. For the method survey, we first present the basic knowledge of detection and segmentation in close-set as the prelimin

This paper examines the state of Open Data in Latvia at the middle of 2014. The study is divided into two parts: (i) a survey of open data situation and (ii) an overview of available open data sets. The first part examines the general open data climate in Latvia according to the guidelines of the OKFN Open Data Index making the results comparable to those of other participants of this index. The second part examines datasets made available on the Latvia Open Data community catalogue, the only open data catalogue available in Latvia at the moment. We conclude that Latvia public sector open data mostly fulfil the basic criteria (e.g., data is available) of the Open Data Index but fail on more advanced criteria: the majority of data considered in the study are not published in machine-readable form, are not available for bulk download and none of the data sources have open license statements.

This paper reviews research literature on Diamond Open Access (DOA) journals - sometimes also called Platinum Open Access - that was produced after this journal segment started to become a priority in European research policy around 2020. It contextualizes the current science policy debate, critically examines different understandings of DOA, and reviews studies on the role of such journals in scholarly communication. Most existing research consists of quantitative studies focusing on aspects such as the number of DOA journals, their publication output, the diversity of the landscape in terms of subject areas, languages, publishing entities, indexing in major databases, awareness and perception among scholars, cost analyses, as well as insights into the internal operations of DOA journals. The review shows that research on DOA journals is partly influenced by the science policy discourse in at least two ways: first, through the normativity inherent in that discourse, and second, through the temporality of policy-driven research of practical relevance, which leaves important aspects of the phenomenon understudied. Moreover, research on the DOA journal landscape has implications beyo

Recent works have proven that intricate cooperative behaviors can emerge in agents trained using meta reinforcement learning on open ended task distributions using self-play. While the results are impressive, we argue that self-play and other centralized training techniques do not accurately reflect how general collective exploration strategies emerge in the natural world: through decentralized training and over an open-ended distribution of tasks. In this work we therefore investigate the emergence of collective exploration strategies, where several agents meta-learn independent recurrent policies on an open ended distribution of tasks. To this end we introduce a novel environment with an open ended procedurally generated task space which dynamically combines multiple subtasks sampled from five diverse task types to form a vast distribution of task trees. We show that decentralized agents trained in our environment exhibit strong generalization abilities when confronted with novel objects at test time. Additionally, despite never being forced to cooperate during training the agents learn collective exploration strategies which allow them to solve novel tasks never encountered duri

The fourth industrial revolution promotes the integration of Information Technology (IT) and strategic resources. New IT demands and uses have been leading to changes in business processes and corporate governance. Lately, the financial industry has adopted a new integrated banking model known as Open Banking (OB) and the advent of cryptocurrencies has led to the Digital Economy (DE) materialization. Considering these facts, this paper expects to point out through literature review some IT enabling factors that allow the conception of a new industry design (or governance) specifically in the financial industry illustrated by the cases of the Open Banking and Digital Economy. This paper is structured mostly on literature review, accompanied by results, discussions, and finally, conclusions are presented. It was found five potential enabling factors. Keywords: Digital Economy, Information Technology (IT), Open Banking.

Open generative models are vitally important for the community, allowing for fine-tunes and serving as baselines when presenting new models. However, most current text-to-audio models are private and not accessible for artists and researchers to build upon. Here we describe the architecture and training process of a new open-weights text-to-audio model trained with Creative Commons data. Our evaluation shows that the model's performance is competitive with the state-of-the-art across various metrics. Notably, the reported FDopenl3 results (measuring the realism of the generations) showcase its potential for high-quality stereo sound synthesis at 44.1kHz.

It is well known that the state operator of an open quantum system can be generically represented as the solution of a time-local equation -- a quantum master equation. Unraveling in quantum trajectories offers a picture of open system dynamics dual to solving master equations. In the unraveling picture, physical indicators are computed as Monte-Carlo averages over a stochastic process valued in the Hilbert space of the system. This approach is particularly adapted to simulate systems in large Hilbert spaces. We show that the dynamics of an open quantum system generically admits an unraveling in the Hilbert space of the system described by a Markov process generated by ordinary stochastic differential equations for which rigorous concentration estimates are available. The unraveling can be equivalently formulated in terms of norm-preserving state vectors or in terms of linear ``ostensible" processes trace preserving only on average. We illustrate the results in the case of a two level system in a simple boson environment. Next, we derive the state-of-the-art form of the Diosi-Gisin-Strunz Gaussian random ostensible state equation in the context of a model problem. This equation pro

This text is a short introduction to the physics of driven-dissipative many-body systems, focusing on a few selected topics. Beyond its more ``historical'' interest in the study of atomic physics and quantum optics, presently the modeling and studying dissipative phenomena in open quantum systems is pivotal to understanding quantum hardware platforms. While the lack of a thermodynamic potential for these out-of-equilibrium open systems makes it theoretically challenging to investigate their physics, at the same time it allows going beyond the thermodynamic paradigms and investigating new and exotic phenomena. We will focus on one of the simplest, yet most effective, descriptions of open quantum systems, namely the (Gorini-Kossakowski-Sudarshan-) Lindblad master equation. This phenomenological approach describes quantum systems that weakly interact with their surrounding environment. Although many of the results derived below will apply to any quantum system, we will focus in particular on bosonic/spin systems.

Open web-scale pre-training corpora remain concentrated in English, limiting multilingual LLM development. We introduce MultiSynt/MT, an open synthetic parallel corpus with approximately 4.8 trillion target-language tokens across 36 European languages, produced by translating 100 billion high-quality Nemotron-CC tokens with Tower+ and OPUS-MT/HPLT-MT systems. For many medium- and lower-resource European languages, this is the largest openly available pre-training resource. On a broad multilingual benchmark suite, reference LLMs trained on MultiSynt/MT reach the final score of HPLT 2.0, a native-data baseline, using roughly 72% fewer pre-training tokens, and outperform it by approximately 15% relative at a matched 100B-token training budget. Our analyses also identify evaluation blind spots: standard multiple-choice benchmarks miss translation-quality differences that a fluency-sensitive LLM-as-judge evaluation cleanly recovers on the trained LLMs (with no fluency deficit in MultiSynt itself), and Norwegian idiomatic and culturally grounded tasks remain better served by native data. We release the corpus, including row-aligned translations from multiple systems, to support controlle