A Deep Learning Technique to Control the Non-linear Dynamics of a Gravitational-wave Interferometer

In this work we developed a deep learning technique that successfully solves a non-linear dynamic control problem. Instead of directly tackling the control problem, we combined methods in probabilistic neural networks and a Kalman-Filter-inspired model to build a non-linear state estimator for the system. We then used the estimated states to implement a trivial controller for the now fully observable system. We applied this technique to a crucial non-linear control problem that arises in the operation of the LIGO system, an interferometric gravitational-wave observatory. We demonstrated in simulation that our approach can learn from data to estimate the state of the system, allowing a successful control of the interferometer's mirror . We also developed a computationally efficient model that can run in real time at high sampling rate on a single modern CPU core, one of the key requirements for the implementation of our solution in the LIGO digital control system. We believe these techniques could be used to help tackle similar non-linear control problems in other applications

A Deep Neural Network Based Reverse Radio Spectrogram Search Algorithm

Modern radio astronomy instruments generate vast amounts of data, and the increasingly challenging radio frequency interference (RFI) environment necessitates ever-more sophisticated RFI rejection algorithms. The "needle in a haystack" nature of searches for transients and technosignatures requires us to develop methods that can determine whether a signal of interest has unique properties, or is a part of some larger set of pernicious RFI. In the past, this vetting has required onerous manual inspection of very large numbers of signals. In this paper we present a fast and modular deep learning algorithm to search for lookalike signals of interest in radio spectrogram data. First, we trained a B-Variational Autoencoder on signals returned by an energy detection algorithm. We then adapted a positional embedding layer from classical Transformer architecture to a embed additional metadata, which we demonstrate using a frequency-based embedding. Next we used the encoder component of the B-Variational Autoencoder to extract features from small (~ 715,Hz, with a resolution of 2.79Hz per frequency bin) windows in the radio spectrogram. We used our algorithm to conduct a search for a given query (encoded signal of interest) on a set of signals (encoded features of searched items) to produce the top candidates with similar features. We successfully demonstrate that the algorithm retrieves signals with similar appearance, given only the original radio spectrogram data. This algorithm can be used to improve the efficiency of vetting signals of interest in technosignature searches, but could also be applied to a wider variety of searches for "lookalike" signals in large astronomical datasets.Comment: 8 pages, 8 figure

Automated pectoral muscle identification on MLOâ view mammograms: Comparison of deep neural network to conventional computer vision

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149204/1/mp13451_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149204/2/mp13451.pd

OV-DINO: Unified Open-Vocabulary Detection with Language-Aware Selective Fusion

Open-vocabulary detection is a challenging task due to the requirement of detecting objects based on class names, including those not encountered during training. Existing methods have shown strong zero-shot detection capabilities through pre-training and pseudo-labeling on diverse large-scale datasets. However, these approaches encounter two main challenges: (i) how to effectively eliminate data noise from pseudo-labeling, and (ii) how to efficiently leverage the language-aware capability for region-level cross-modality fusion and alignment. To address these challenges, we propose a novel unified open-vocabulary detection method called OV-DINO, which is pre-trained on diverse large-scale datasets with language-aware selective fusion in a unified framework. Specifically, we introduce a Unified Data Integration (UniDI) pipeline to enable end-to-end training and eliminate noise from pseudo-label generation by unifying different data sources into detection-centric data format. In addition, we propose a Language-Aware Selective Fusion (LASF) module to enhance the cross-modality alignment through a language-aware query selection and fusion process. We evaluate the performance of the proposed OV-DINO on popular open-vocabulary detection benchmarks, achieving state-of-the-art results with an AP of 50.6% on the COCO benchmark and 40.1% on the LVIS benchmark in a zero-shot manner, demonstrating its strong generalization ability. Furthermore, the fine-tuned OV-DINO on COCO achieves 58.4% AP, outperforming many existing methods with the same backbone. The code for OV-DINO is available at https://github.com/wanghao9610/OV-DINO.Comment: Technical Repor

Identification and modulation of electronic band structures of single-phase B-(AlxGa1-x)2O3 alloys grown by laser molecular beam epitaxy

Understanding the band structure evolution of (AlxGa1x)2O3 alloys is of fundamental importance for developing Ga2O3-based power electronic devices and vacuum ultraviolet super-radiation hard detectors. Here, we report on the bandgap engineering of b-(AlxGa1x)2O3 thin films and the identification of compositionally dependent electronic band structures by a combination of absorption spectra analyses and density functional theory calculations. Single-monoclinic b-phase (AlxGa1x)2O3 (0 x 0.54) films with a preferred (201) orientation were grown by laser molecular beam epitaxy with tunable bandgap ranging from 4.5 to 5.5 eV. The excellent fitting of absorption spectra by the relation of (ah) 1/2 / (h-E) unambiguously identifies that b-(AlxGa1x)2O3 alloys are indirect bandgap semiconductors. Theoretical calculations predict that the indirect nature of b-(AlxGa1x)2O3 becomes more pronounced with increased Al composition due to the increased eigenvalue energy gap between M and U points in the valence band. The experimentally determined indirect bandgap exhibits almost a linear relationship with Al composition, which is consistent with the theoretical calculation and indicates a small bowing effect and a good miscibility. The identification and modulation of (AlxGa1x)2O3 band structures allows rational design of ultra-wide bandgap oxide heterostructures for the applications in power electronics and solar-blind or X-ray detection.This research was supported by the National Key Research and Development Project (Grant No. 2017YFB0403003), the National Natural Science Foundation of China (Grant Nos. 61774081, 61322403, and 11227904), the Natural Science Foundation of Jiangsu Province (Grant Nos. BK20130013 and BK20161401), the Six Talent Peaks Project in Jiangsu Province (2014XXRJ001), the Fundamental Research Funds for the Central Universities (021014380093 and 021014380085) and the Australian Research Council. The computational part of this research was undertaken with the assistance of resources from the National Computational Infrastructure (NCI), which is supported by the Australian Government under the NCRIS program

A deep-learning search for technosignatures from 820 nearby stars

The goal of the search for extraterrestrial intelligence (SETI) is to quantify the prevalence of technological life beyond Earth via their ‘technosignatures’. One theorized technosignature is narrowband Doppler drifting radio signals. The principal challenge in conducting SETI in the radio domain is developing a generalized technique to reject human radiofrequency interference. Here we present a comprehensive deep-learning-based technosignature search on 820 stellar targets from the Hipparcos catalogue, totalling over 480 h of on-sky data taken with the Robert C. Byrd Green Bank Telescope as part of the Breakthrough Listen initiative. We implement a novel ?-convolutional variational autoencoder to identify technosignature candidates in a semi-unsupervised manner while keeping the false-positive rate manageably low, reducing the number of candidate signals by approximately two orders of magnitude compared with previous analyses on the same dataset. Our work also returned eight promising extraterrestrial intelligence signals of interest not previously identified. Re-observations on these targets have so far not resulted in re-detections of signals with similar morphology. This machine-learning approach presents itself as a leading solution in accelerating SETI and other transient research into the age of data-driven astronomy

Knowledge and attitudes of healthcare workers in Chinese intensive care units regarding 2009 H1N1 influenza pandemic

<p>Abstract</p> <p>Background</p> <p>To describe the knowledge and attitudes of critical care clinicians during the 2009 H1N1 influenza pandemic.</p> <p>Methods</p> <p>A survey conducted in 21 intensive care units in 17 provinces in China.</p> <p>Results</p> <p>Out of 733 questionnaires distributed, 695 were completed. Three hundred and fifty-six respondents (51.2%) reported their experience of caring for H1N1 patients. Despite the fact that 88.5% of all respondents ultimately finished an H1N1 training program, only 41.9% admitted that they had the knowledge of 2009 H1N1 influenza. A total of 572 respondents (82.3%) expressed willingness to care for H1N1 patients. Independent variables associated with increasing likelihood to care for patients in the logistic regression analysis were physicians or nurses rather than other professionals (odds ratio 4.056 and 3.235, p = 0.002 and 0.007, respectively), knowledge training prior to patient care (odds ratio 1.531, p = 0.044), and the confidence to know how to protect themselves and their patients (odds ratio 2.109, p = 0.001).</p> <p>Conclusion</p> <p>Critical care clinicians reported poor knowledge of H1N1 influenza, even though most finished a relevant knowledge training program. Implementation of appropriate education program might improve compliance to infection control measures, and willingness to work in a pandemic.</p

A teosinte-derived allele of ZmSC improves salt tolerance in maize

Maize, a salt-sensitive crop, frequently suffers severe yield losses due to soil salinization. Enhancing salt tolerance in maize is crucial for maintaining yield stability. To address this, we developed an introgression line (IL76) through introgressive hybridization between maize wild relatives Zea perennis, Tripsacum dactyloides, and inbred Zheng58, utilizing the tri-species hybrid MTP as a genetic bridge. Previously, genetic variation analysis identified a polymorphic marker on Zm00001eb244520 (designated as ZmSC), which encodes a vesicle-sorting protein described as a salt-tolerant protein in the NCBI database. To characterize the identified polymorphic marker, we employed gene cloning and homologous cloning techniques. Gene cloning analysis revealed a non-synonymous mutation at the 1847th base of ZmSCIL76, where a guanine-to-cytosine substitution resulted in the mutation of serine to threonine at the 119th amino acid sequence (using ZmSCZ58 as the reference sequence). Moreover, homologous cloning demonstrated that the variation site derived from Z. perennis. Functional analyses showed that transgenic Arabidopsis lines overexpressing ZmSCZ58 exhibited significant reductions in leaf number, root length, and pod number, alongside suppression of the expression of genes in the SOS and CDPK pathways associated with Ca2+ signaling. Similarly, fission yeast strains expressing ZmSCZ58 displayed inhibited growth. In contrast, the ZmSCIL76 allele from Z. perennis alleviated these negative effects in both Arabidopsis and yeast, with the lines overexpressing ZmSCIL76 exhibiting significantly higher abscisic acid (ABA) content compared to those overexpressing ZmSCZ58. Our findings suggest that ZmSC negatively regulates salt tolerance in maize by suppressing downstream gene expression associated with Ca2+ signaling in the CDPK and SOS pathways. The ZmSCIL76 allele from Z. perennis, however, can mitigate this negative regulatory effect. These results provide valuable insights and genetic resources for future maize salt tolerance breeding programs