The integration of AI-assisted biomedical image analysis into clinical practice demands AI-generated findings that are not only accurate but also interpretable to clinicians. However, existing biomedical AI models generally lack the ability to simultaneously generate diagnostic findings and localize corresponding biomedical objects. This limitation makes it challenging for clinicians to correlate AI-generated findings with visual evidence (e.g., tiny lesions) in images and interpret the results of AI models. To address this challenge, we introduce UniBiomed, the first universal foundation model for grounded biomedical image interpretation, which is capable of generating accurate diagnostic findings and simultaneously segmenting the corresponding biomedical targets. UniBiomed is based on a novel integration of Multi-modal Large Language Model and Segment Anything Model, which can effectively unify diverse biomedical tasks in universal training for advancing grounded interpretation. To develop UniBiomed, we curate a large-scale dataset comprising over 27 million triplets of images, region annotations, and text descriptions across ten biomedical imaging modalities. Extensive validatio
Accurate histopathologic interpretation is key for clinical decision-making; however, current deep learning models for digital pathology are often overconfident and poorly calibrated in out-of-distribution (OOD) settings, which limit trust and clinical adoption. Safety-critical medical imaging workflows benefit from intrinsic uncertainty-aware properties that can accurately reject OOD input. We implement the Spectral-normalized Neural Gaussian Process (SNGP), a set of lightweight modifications that apply spectral normalization and replace the final dense layer with a Gaussian process layer to improve single-model uncertainty estimation and OOD detection. We evaluate SNGP vs. deterministic and MonteCarlo dropout on six datasets across three biomedical classification tasks: white blood cells, amyloid plaques, and colorectal histopathology. SNGP has comparable in-distribution performance while significantly improving uncertainty estimation and OOD detection. Thus, SNGP or related models offer a useful framework for uncertainty-aware classification in digital pathology, supporting safe deployment and building trust with pathologists.
The ILC Technology Network (ITN) was established in 2022 by the ILC International Development Team, a subcommittee of the International Committee for Future Accelerators, to advance engineering studies toward the realisation of the International Linear Collider (ILC). While the ITN work packages focus on engineering activities for the ILC, their topics are also relevant to a broad range of accelerator applications in particle physics and beyond. These work packages are being carried out now by laboratories in Asia and Europe in close collaboration. This report summarises the current status of the ITN activities.
Large language models often perform well on biomedical NLP tasks but may fail to link ontology terms to their correct identifiers. We investigate why these failures occur by analyzing predictions across two major ontologies, Human Phenotype Ontology and Gene Ontology, and two high-performing models, GPT-4o and LLaMa 3.1 405B. We evaluate nine candidate features related to term familiarity, identifier usage, morphology, and ontology structure. Univariate and multivariate analyses show that exposure to ontology identifiers is the strongest predictor of linking success.
The Large Binocular Telescope, with its expansive collecting area, angular resolving power, and advanced optical design, provides a robust platform for development and operation of advanced instrumentation for astronomical research. The LBT currently hosts a mature suite of instruments for spectroscopy and imaging at optical through mid-infrared wavelengths, supported by sophisticated adaptive optics systems. This contribution summarizes the current state of instrumentation, including upgrades to existing instruments and commissioning of second generation instruments now in progress. The LBT is soliciting proposals for next generation instrument concepts, with participation open to consortium members and others interested in participation in the Observatory.
WST, the Wide-field Spectroscopic Telescope is a proposed new facility that will provide a transformational gain in spectroscopic survey capability over existing facilities. The WST is a 12 metre class telescope equipped with instrumentation to provide simultaneous observations in both multiple-object spectroscopy and integral field spectroscopy modes. This paper will describe the status of the instruments being designed for the WST, the fibre positioner module, the low and high-resolution multiple object spectrographs, the integral field spectrograph, disperser technology, sustainable detector and cryostat technology, and the calibration system. An overview of the overall layout of the instruments within the WST facility will be provided.
The Collaboration for Astronomy Signal Processing and Electronics Research (CASPER) has been working for a decade to reduce the time and cost of designing, building and deploying new digital radio-astronomy instruments. Today, CASPER open-source technology powers over 45 scientific instruments worldwide, and is used by scientists and engineers at dozens of academic institutions. In this paper we catalog the current offerings of the CASPER collaboration, and instruments past and present built by CASPER users and developers. We describe the ongoing state of software development, as CASPER looks to support a broader range of programming environments and hardware and ensure compatibility with the latest vendor tools.
A next-generation medium-energy (100 keV to 100 MeV) gamma-ray observatory will greatly enhance the identification and characterization of multimessenger sources in the coming decade. Coupling gamma-ray spectroscopy, imaging, and polarization to neutrino and gravitational wave detections will develop our understanding of various astrophysical phenomena including compact object mergers, supernovae remnants, active galactic nuclei and gamma-ray bursts. An observatory operating in the MeV energy regime requires technologies that are capable of measuring Compton scattered photons and photons interacting via pair production. AstroPix is a monolithic high voltage CMOS active pixel sensor which enables future gamma-ray telescopes in this energy range. AstroPix's design is iterating towards low-power (~1.5 mW/cm$^{2}$), high spatial (500 microns pixel pitch) and spectral (<5 keV at 122 keV) tracking of photon and charged particle interactions. Stacking planar arrays of AstroPix sensors in three dimensions creates an instrument capable of reconstructing the trajectories and energies of incident gamma rays over large fields of view. A prototype multi-layered AstroPix instrument, called th
Computer-assisted diagnostic and prognostic systems of the future should be capable of simultaneously processing multimodal data. Multimodal deep learning (MDL), which involves the integration of multiple sources of data, such as images and text, has the potential to revolutionize the analysis and interpretation of biomedical data. However, it only caught researchers' attention recently. To this end, there is a critical need to conduct a systematic review on this topic, identify the limitations of current work, and explore future directions. In this scoping review, we aim to provide a comprehensive overview of the current state of the field and identify key concepts, types of studies, and research gaps with a focus on biomedical images and texts joint learning, mainly because these two were the most commonly available data types in MDL research. This study reviewed the current uses of multimodal deep learning on five tasks: (1) Report generation, (2) Visual question answering, (3) Cross-modal retrieval, (4) Computer-aided diagnosis, and (5) Semantic segmentation. Our results highlight the diverse applications and potential of MDL and suggest directions for future research in the fi
Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph-based data models elucidate the interconnectedness between core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these "knowledge graphs" (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally-accepted, open-access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object-oriented classification and graph-oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates), representing biomedical entities such as ge
Automatic analysis of biomedical time series such as electroencephalogram (EEG) and electrocardiographic (ECG) signals has attracted great interest in the community of biomedical engineering due to its important applications in medicine. In this work, a simple yet effective bag-of-words representation that is able to capture both local and global structure similarity information is proposed for biomedical time series representation. In particular, similar to the bag-of-words model used in text document domain, the proposed method treats a time series as a text document and extracts local segments from the time series as words. The biomedical time series is then represented as a histogram of codewords, each entry of which is the count of a codeword appeared in the time series. Although the temporal order of the local segments is ignored, the bag-of-words representation is able to capture high-level structural information because both local and global structural information are well utilized. The performance of the bag-of-words model is validated on three datasets extracted from real EEG and ECG signals. The experimental results demonstrate that the proposed method is not only insens
The project "Novel Astronomical Instrumentation through photonic Reformatting" is a DFG-funded collaboration to exploit the recognized potential of photonics solutions for a radically new approach to astronomical instrumentation for optical/infrared high precision spectroscopy and high angular resolution imaging. We present a project overview and initial development results from our Adaptive Optics-photonic test bed, Ultrafast Laser Inscribed waveguides for interferometric beam combination and 3D printing structures for astronomical instrumentation. The project is expected to lead to important technological breakthroughs facilitating uniquely functionality and technical solutions for the next generation of instrumentation.
In the era of digital healthcare, the huge volumes of textual information generated every day in hospitals constitute an essential but underused asset that could be exploited with task-specific, fine-tuned biomedical language representation models, improving patient care and management. For such specialized domains, previous research has shown that fine-tuning models stemming from broad-coverage checkpoints can largely benefit additional training rounds over large-scale in-domain resources. However, these resources are often unreachable for less-resourced languages like Italian, preventing local medical institutions to employ in-domain adaptation. In order to reduce this gap, our work investigates two accessible approaches to derive biomedical language models in languages other than English, taking Italian as a concrete use-case: one based on neural machine translation of English resources, favoring quantity over quality; the other based on a high-grade, narrow-scoped corpus natively written in Italian, thus preferring quality over quantity. Our study shows that data quantity is a harder constraint than data quality for biomedical adaptation, but the concatenation of high-quality d
Over its more than thirty-year history, the Advanced Technologies and Instrumentation (ATI) program has provided grants to support technology development for ground-based astronomy. Research from this program has advanced adaptive optics, high resolution and multi-object spectroscopy, optical interferometry and synoptic surveys, to name just a few. Previous and ongoing scientific advances span the entire field of astronomy, from studies of the Sun to the distant universe. Through a combination of literature assessment and individual case studies, we present a survey of ATI funded research for optical-infrared astronomy. We find that technology development unfolds over a time period that is longer than an individual grant. A longitudinal perspective shows that substantial scientific gains have resulted from investments in technology.
Analog to digital conversion is a very important part of almost all beam instrumentation systems. Ideally, in a properly designed system, the used analog to digital converter (ADC) should not limit the system performance. However, despite recent improvements in ADC technology, quite often this is not possible and the choice of the ADC influences significantly or even restricts the system performance. It is therefore very important to estimate the requirements for the analog to digital conversion at an early stage of the system design and evaluate whether one can find an adequate ADC fulfilling the system specification. In case of beam instrumentation systems requiring both, high time and amplitude resolution, it often happens that the system specification cannot be met with the available ADCs without applying special processing to the analog signals prior to their digitisation. In such cases the requirements for the ADC even influence the system architecture. This paper aims at helping the designer of a beam instrumentation system in the process of selecting an ADC, which in many cases is iterative, requiring a trade off between system performance, complexity and cost. Analog to di
This CAS talk describes the role of beam instrumentation and diagnostics in particle therapy accelerators. It presents an extended view on instrumentation, feedbacks, detector technology, quality assurance (QA) and their interdependencies. Furthermore, some basics, examples and challenges in near future concerning diagnostics and instrumentation techniques used in particle therapy are reported.
Blockchain is an emerging digital technology allowing ubiquitous financial transactions among distributed untrusted parties, without the need of intermediaries such as banks. This article examines the impact of blockchain technology in agriculture and food supply chain, presents existing ongoing projects and initiatives, and discusses overall implications, challenges and potential, with a critical view over the maturity of these projects. Our findings indicate that blockchain is a promising technology towards a transparent supply chain of food, with many ongoing initiatives in various food products and food-related issues, but many barriers and challenges still exist, which hinder its wider popularity among farmers and systems. These challenges involve technical aspects, education, policies and regulatory frameworks.
NASA's suborbital program provides an opportunity to conduct unique science experiments above Earth's atmosphere and is a pipeline for the technology and personnel essential to future space astrophysics, heliophysics, and atmospheric science missions. In this paper, we describe three astronomy payloads developed (or in development) by the Ultraviolet Rocket Group at the University of Colorado. These far-ultraviolet (100 - 160 nm) spectrographic instruments are used to study a range of scientific topics, from gas in the interstellar medium (accessing diagnostics of material spanning five orders of magnitude in temperature in a single observation) to the energetic radiation environment of nearby exoplanetary systems. The three instruments, SLICE, CHESS, and SISTINE form a progression of instrument designs and component-level technology maturation. SLICE is a pathfinder instrument for the development of new data handling, storage, and telemetry techniques. CHESS and SISTINE are testbeds for technology and instrument design enabling high-resolution (R > 100,000) point source spectroscopy and high throughput imaging spectroscopy, respectively, in support of future Explorer, Probe, an
Causal precedence between biochemical interactions is crucial in the biomedical domain, because it transforms collections of individual interactions, e.g., bindings and phosphorylations, into the causal mechanisms needed to inform meaningful search and inference. Here, we analyze causal precedence in the biomedical domain as distinct from open-domain, temporal precedence. First, we describe a novel, hand-annotated text corpus of causal precedence in the biomedical domain. Second, we use this corpus to investigate a battery of models of precedence, covering rule-based, feature-based, and latent representation models. The highest-performing individual model achieved a micro F1 of 43 points, approaching the best performers on the simpler temporal-only precedence tasks. Feature-based and latent representation models each outperform the rule-based models, but their performance is complementary to one another. We apply a sieve-based architecture to capitalize on this lack of overlap, achieving a micro F1 score of 46 points.
The versatility of optics enables the design of a wide range of elegant beam instrumentation. Multiple properties of particle beams can be precisely measured by various optical techniques, which include: direct sampling of optical radiation emitted from a charged particle beam; monitoring interactions with an optical probe such as a laserwire; and by electro-optic conversion of the beam signal with high-bandwidth fibre readout. Such methods are typically minimally-invasive and non-destructive, thus permitting diagnostics during accelerator operation without perturbation of the particle beam or risk of damage to the instrument. These proceedings summarise three CAS lectures that introduce the basic principles of optics relevant for instrumentation design, outline the key laser technologies and setups, and review the state-of-the-art in laser-based beam instrumentation.