Correcting soft errors online in fast fourier transform

While many algorithm-based fault tolerance (ABFT) schemes have been proposed to detect soft errors offline in the fast Fourier transform (FFT) after computation finishes, none of the existing ABFT schemes detect soft errors online before the computation finishes. This paper presents an online ABFT scheme for FFT so that soft errors can be detected online and the corrupted computation can be terminated in a much more timely manner. We also extend our scheme to tolerate both arithmetic errors and memory errors, develop strategies to reduce its fault tolerance overhead and improve its numerical stability and fault coverage, and finally incorporate it into the widely used FFTW library - one of the today's fastest FFT software implementations. Experimental results demonstrate that: (1) the proposed online ABFT scheme introduces much lower overhead than the existing offline ABFT schemes; (2) it detects errors in a much more timely manner; and (3) it also has higher numerical stability and better fault coverage

RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extraction

Automatic rib labeling and anatomical centerline extraction are common prerequisites for various clinical applications. Prior studies either use in-house datasets that are inaccessible to communities, or focus on rib segmentation that neglects the clinical significance of rib labeling. To address these issues, we extend our prior dataset (RibSeg) on the binary rib segmentation task to a comprehensive benchmark, named RibSeg v2, with 660 CT scans (15,466 individual ribs in total) and annotations manually inspected by experts for rib labeling and anatomical centerline extraction. Based on the RibSeg v2, we develop a pipeline including deep learning-based methods for rib labeling, and a skeletonization-based method for centerline extraction. To improve computational efficiency, we propose a sparse point cloud representation of CT scans and compare it with standard dense voxel grids. Moreover, we design and analyze evaluation metrics to address the key challenges of each task. Our dataset, code, and model are available online to facilitate open research at https://github.com/M3DV/RibSegComment: 10 pages, 6 figures, journa

Efficient and tunable liquid crystal random laser based on plasmonic-enhanced FRET

Random lasers (RLs), which possess peculiar advantages (e.g., emission and coherence tunable) over traditional lasers with optical resonators, have witnessed rapid development in the past decades. However, it is still a challenge to tune the lasing peak of an RL over a wide range. Here, a temperature-dependent Förster resonance energy transfer (FRET) RL is demonstrated in pyrromethene 597 (PM597, “donor”) and Nile blue (NB, “acceptor”) doped chiral liquid crystals. By changing the temperature that drives the liquid crystal bandgap shift, our RL device exhibits a lasing output change from 560 nm (yellow) to 700 nm (red). While the intrinsic FRET efficiency between PM597 and NB is relatively low, the red lasing is weak. By introducing gold nanorods (GNRs) into these RL devices and utilizing GNRs’ localized surface plasmon resonance (LSPR) effect, the efficiency of FRET transfer is increased by 68.9%, thereby reducing the threshold of the RL devices. By tuning the longitudinal LSPR to match the emission wavelength of NB, the best 200-fold lasing intensity enhancement is recorded. Our findings open a pathway toward realizing LSPR-enhanced FRET tunable RLs and broaden the range of their possible exploration in photonics research and technologies

Zero-Shot Multi-View Indoor Localization via Graph Location Networks

Indoor localization is a fundamental problem in location-based applications. Current approaches to this problem typically rely on Radio Frequency technology, which requires not only supporting infrastructures but human efforts to measure and calibrate the signal. Moreover, data collection for all locations is indispensable in existing methods, which in turn hinders their large-scale deployment. In this paper, we propose a novel neural network based architecture Graph Location Networks (GLN) to perform infrastructure-free, multi-view image based indoor localization. GLN makes location predictions based on robust location representations extracted from images through message-passing networks. Furthermore, we introduce a novel zero-shot indoor localization setting and tackle it by extending the proposed GLN to a dedicated zero-shot version, which exploits a novel mechanism Map2Vec to train location-aware embeddings and make predictions on novel unseen locations. Our extensive experiments show that the proposed approach outperforms state-of-the-art methods in the standard setting, and achieves promising accuracy even in the zero-shot setting where data for half of the locations are not available. The source code and datasets are publicly available at https://github.com/coldmanck/zero-shot-indoor-localization-release.Comment: Accepted at ACM MM 2020. 10 pages, 7 figures. Code and datasets available at https://github.com/coldmanck/zero-shot-indoor-localization-releas

Inhibition of the CXCL12/CXCR4-axis as preventive therapy for radiation-induced pulmonary fibrosis

Background: A devastating late injury caused by radiation is pulmonary fibrosis. This risk may limit the volume of irradiation and compromise potentially curative therapy. Therefore, development of a therapy to prevent this toxicity can be of great benefit for this patient population. Activation of the chemokine receptor CXCR4 by its ligand stromal cell-derived factor 1 (SDF-1/CXCL12) may be important in the development of radiation-induced pulmonary fibrosis. Here, we tested whether MSX-122, a novel small molecule and partial CXCR4 antagonist, can block development of this fibrotic process. Methodology/Principal Findings: The radiation-induced lung fibrosis model used was C57BL/6 mice irradiated to the entire thorax or right hemithorax to 20 Gy. Our parabiotic model involved joining a transgenic C57BL/6 mouse expressing GFP with a wild-type mouse that was subsequently irradiated to assess for migration of GFP+ bone marrow-derived progenitor cells to the irradiated lung. CXCL12 levels in the bronchoalveolar lavage fluid (BALF) and serum after irradiation were determined by ELISA. CXCR4 and CXCL12 mRNA in the irradiated lung was determined by RNase protection assay. Irradiated mice were treated daily with AMD3100, an established CXCR4 antagonist; MSX-122; and their corresponding vehicles to determine impact of drug treatment on fibrosis development. Fibrosis was assessed by serial CTs and histology. After irradiation, CXCL12 levels increased in BALF and serum with a corresponding rise in CXCR4 mRNA within irradiated lungs consistent with recruitment of a CXCR4+ cell population. Using our parabiotic model, we demonstrated recruitment of CXCR4+ bone marrow-derived mesenchymal stem cells, identified based on marker expression, to irradiated lungs. Finally, irradiated mice that received MSX-122 had significant reductions in development of pulmonary fibrosis while AMD3100 did not significantly suppress this fibrotic process. Conclusions/Significance: CXCR4 inhibition by drugs such as MSX-122 may alleviate potential radiation-induced lung injury, presenting future therapeutic opportunities for patients requiring chest irradiation. © 2013 Shu et al

Research on a mmWave Beam-Prediction Algorithm with Situational Awareness Based on Deep Learning for Intelligent Transportation Systems

Simply speaking, automatic driving requires the calculation of a large amount of traffic data and, finally, the obtainment of the optimal driving route and speed. However, the key technical difficulty is the obtainment of data; thus, radar has become an indispensable hardware for automatic driving. Compared to the optical and infrared radar, millimeter-wave radar is not affected by the shape and color of the target, and it is not affected by the atmospheric turbulence, compared to ultrasonic, and so it has a stable detection performance and good environmental adaptability. It is little affected by changes in the weather, and the external environment, rain, snow, dust, and sunshine have no interference in it. The Doppler frequency shift is large, and the accuracy of the relative velocity measurement is improved. However, one challenge for vehicles in fast environments is millimeter-wave-based communication. Because of the short wavelength of the millimeter wave and the high path and penetration losses, the beamforming technology of a large-scale antenna array plays a key role in the construction and maintenance of millimeter-wave communication links. Millimeter waves have wide channel bandwidths, unique channel characteristics, and hardware limitations, and so there are many challenges in the direct use of beamforming technology in millimeter-wave communication. Traditional beam training cannot meet the requirements of low overhead and low delay. This paper, in order to obtain beam information, introduces the context-awareness module to the deep-learning net, which is derived from past observation data. This paper establishes a model that contains the receiver and the surrounding vehicles to perceive the environment. Then, a long short-term memory (LSTM) neural network is used to foresee the acquired power, which is quantized by several beam powers. According to the conclusion, the prediction accuracy is greatly increased, and the model could yield throughput with almost zero overhead and little performance loss