ON JAN. 20, 2025, his first day back in office,
President Donald Trump signed an executive order declaring that the United States would henceforth recognize only two sexes, male and female.
Trump framed the move as one meant to protect the dignity, safety, and well-being of women. But the order also upended a long-standing policy at the National Institutes of Health
— one that was crafted to ensure that females are adequately represented in biomedical research.
Imple…
CAGE: An Internal Source Scanning Cryostat for HPGe Characterization
G. Othman, C. Wiseman, T. H. Burritt, J. A. Detwiler, M. P. Held, R. Henning, T. Mathew, D. Peterson, W. Pettus, G. Song, T. D. Van Wechel
https://arxiv.org/abs/2602.06289 https://arxiv.org/pdf/2602.06289 https://arxiv.org/html/2602.06289
arXiv:2602.06289v1 Announce Type: new
Abstract: The success of current and future-generation neutrinoless double beta decay experiments relies on the ability to eliminate or reduce extraneous backgrounds. In addition to constructing experiments using radiopure materials and handling in underground laboratories, it is necessary to understand and reduce known backgrounds in data analysis. The Large Enriched Germanium Experiment for Neutrinoless double beta Decay is searching for this decay using 76Ge-enriched high-purity germanium detectors submerged in an active liquid argon veto. A significant background in LEGEND is surface events from shallowly-impinging radiation on detector surfaces. In this paper we introduce the Collimated Alphas, Gammas, and Electrons (CAGE) scanning system, an internal-source scanning vacuum cryostat, designed to perform studies of surface events on sensitive surfaces of HPGe in a surface-lab. CAGE features a collimated radionuclide source inside a movable infrared shield that is able to perform precision scans of detector surfaces by utilizing three independent motor stages for source positioning. This allows detailed studies of pulse shapes as a function of source position and incident angle, where defining features can be extracted and exploited for removing surface backgrounds in data analysis in LEGEND. In this paper, we describe CAGE and demonstrate its performance with a commissioning run with 241Am. The commissioning run was completed with the source at normal incidence, and we estimate a beam spot precision of 3.1 mm, which includes positioning uncertainties and the beam-spot size. Using the 59.5 keV gamma population from 241Am, we show that low-energy photon events near the passivated surface feature risetimes that increase with radial distance from the detector center. We suggest a specific metric that can be used to discriminate low-energy gamma backgrounds in LEGEND with similar characteristics.
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Sharing for a friend: 'Lead the development of the digital future of British art research' - Head of Systems, Data and Technology (Fixed term 18-months) Paul Mellon Centre for Studies in British Art (PMC) https://www.peridotpartners.co.uk/jobs/hea
Bristol Myers Squibb, an industry stalwart in need of a hit, is betting a pill called Cobenfy can become the first approved medicine for the millions of Americans diagnosed with psychosis related to Alzheimer’s disease.
That case rests almost entirely on a single clinical trial, conducted before the DVD was invented, in which its striking benefits were derailed by damning side effects.
Later this year, Bristol will get the results from three pivotal studies designed to make good …
Hierarchic-EEG2Text: Assessing EEG-To-Text Decoding across Hierarchical Abstraction Levels
Anupam Sharma, Harish Katti, Prajwal Singh, Shanmuganathan Raman, Krishna Miyapuram
https://arxiv.org/abs/2602.20932 https://arxiv.org/pdf/2602.20932 https://arxiv.org/html/2602.20932
arXiv:2602.20932v1 Announce Type: new
Abstract: An electroencephalogram (EEG) records the spatially averaged electrical activity of neurons in the brain, measured from the human scalp. Prior studies have explored EEG-based classification of objects or concepts, often for passive viewing of briefly presented image or video stimuli, with limited classes. Because EEG exhibits a low signal-to-noise ratio, recognizing fine-grained representations across a large number of classes remains challenging; however, abstract-level object representations may exist. In this work, we investigate whether EEG captures object representations across multiple hierarchical levels, and propose episodic analysis, in which a Machine Learning (ML) model is evaluated across various, yet related, classification tasks (episodes). Unlike prior episodic EEG studies that rely on fixed or randomly sampled classes of equal cardinality, we adopt hierarchy-aware episode sampling using WordNet to generate episodes with variable classes of diverse hierarchy. We also present the largest episodic framework in the EEG domain for detecting observed text from EEG signals in the PEERS dataset, comprising $931538$ EEG samples under $1610$ object labels, acquired from $264$ human participants (subjects) performing controlled cognitive tasks, enabling the study of neural dynamics underlying perception, decision-making, and performance monitoring.
We examine how the semantic abstraction level affects classification performance across multiple learning techniques and architectures, providing a comprehensive analysis. The models tend to improve performance when the classification categories are drawn from higher levels of the hierarchy, suggesting sensitivity to abstraction. Our work highlights abstraction depth as an underexplored dimension of EEG decoding and motivates future research in this direction.
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Beam Test Performance of AstroPix sensor with 120 GeV protons
Bobae Kim, Regina Caputo, Manoj Jadhav, Sylvester Joosten, Carolyn Kierans, Henry Klest, Adrien Laviron, Richard Leys, Jessica Metcalfe, Jared Richards, Nicolas Striebig, Amanda L. Steinhebel, Daniel Violette, Maria Zurek
https://arxiv.org/abs/2602.06084 https://arxiv.org/pdf/2602.06084 https://arxiv.org/html/2602.06084
arXiv:2602.06084v1 Announce Type: new
Abstract: AstroPix is a high-voltage CMOS (HV-CMOS) monolithic active pixel sensor (MAPS) developed for precision gamma-ray imaging and spectroscopy in the medium energy regime, as well as for precise shower imaging and tracking in the Barrel Imaging Calorimeter (BIC) of the Electron Proton/Ion Collider (ePIC) detector at the future Electron-Ion Collider (EIC). We present beam test results of the AstroPix v3 sensor using a 120 GeV proton beam at the Fermilab Test Beam Facility (FTBF), performed as part of the broader experimental campaign for the BIC prototype calorimeter. The sensor's 500 um pixel pitch enabled precise measurement of the beam profile, providing important information for calorimeter performance studies. Using the measured 120 GeV proton data, we measure the energy deposit of minimum ionizing particles and use them to extract the corresponding effective depletion depth.
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