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- Kristanl, Matej, et al.
(author)
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The Seventh Visual Object Tracking VOT2019 Challenge Results
- 2019
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In: 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW). - : IEEE COMPUTER SOC. - 9781728150239 ; , s. 2206-2241
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Conference paper (peer-reviewed)abstract
- The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOT-ST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2019 focused on long-term tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard short-term, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website(1).
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- Wang, Guo-dong, et al.
(author)
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The genomics of selection in dogs and the parallel evolution between dogs and humans
- 2013
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In: Nature Communications. - : Nature Publishing Group. - 2041-1723. ; 4, s. 1860-
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Journal article (peer-reviewed)abstract
- The genetic bases of demographic changes and artificial selection underlying domestication are of great interest in evolutionary biology. Here we perform whole-genome sequencing of multiple grey wolves, Chinese indigenous dogs and dogs of diverse breeds. Demographic analysis show that the split between wolves and Chinese indigenous dogs occurred 32,000 years ago and that the subsequent bottlenecks were mild. Therefore, dogs may have been under human selection over a much longer time than previously concluded, based on molecular data, perhaps by initially scavenging with humans. Population genetic analysis identifies a list of genes under positive selection during domestication, which overlaps extensively with the corresponding list of positively selected genes in humans. Parallel evolution is most apparent in genes for digestion and metabolism, neurological process and cancer. Our study, for the first time, draws together humans and dogs in their recent genomic evolution.
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- Arzoumanian, Doris, et al.
(author)
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Dust polarized emission observations of NGC 6334: BISTRO reveals the details of the complex but organized magnetic field structure of the high-mass star-forming hub-filament network
- 2021
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In: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 647
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Journal article (peer-reviewed)abstract
- Context. Molecular filaments and hubs have received special attention recently thanks to new studies showing their key role in star formation. While the (column) density and velocity structures of both filaments and hubs have been carefully studied, their magnetic field (B-field) properties have yet to be characterized. Consequently, the role of B-fields in the formation and evolution of hub-filament systems is not well constrained. Aims. We aim to understand the role of the B-field and its interplay with turbulence and gravity in the dynamical evolution of the NGC 6334 filament network that harbours cluster-forming hubs and high-mass star formation. Methods. We present new observations of the dust polarized emission at 850 μm toward the 2 pc × 10 pc map of NGC 6334 at a spatial resolution of 0.09 pc obtained with the James Clerk Maxwell Telescope (JCMT) as part of the B-field In STar-forming Region Observations (BISTRO) survey. We study the distribution and dispersion of the polarized intensity (PI), the polarization fraction (PF), and the plane-of-The-sky B-field angle (χB_POS) toward the whole region, along the 10 pc-long ridge and along the sub-filaments connected to the ridge and the hubs. We derived the power spectra of the intensity and χBPOS along the ridge crest and compared them with the results obtained from simulated filaments. Results. The observations span 3 orders of magnitude in Stokes I and PI and 2 orders of magnitude in PF (from 0.2 to 20%). A large scatter in PI and PF is observed for a given value of I. Our analyses show a complex B-field structure when observed over the whole region ( 10 pc); however, at smaller scales (1 pc), χBPOS varies coherently along the crests of the filament network. The observed power spectrum of χBPOS can be well represented with a power law function with a slope of-1.33 ± 0.23, which is 20% shallower than that of I. We find that this result is compatible with the properties of simulated filaments and may indicate the physical processes at play in the formation and evolution of star-forming filaments. Along the sub-filaments, χBPOS rotates frombeing mostly perpendicular or randomly oriented with respect to the crests to mostly parallel as the sub-filaments merge with the ridge and hubs. This variation of the B-field structure along the sub-filaments may be tracing local velocity flows of infalling matter in the ridge and hubs. Our analysis also suggests a variation in the energy balance along the crests of these sub-filaments, from magnetically critical or supercritical at their far ends to magnetically subcritical near the ridge and hubs. We also detect an increase in PF toward the high-column density (NH2 â 1023 cm-2) star cluster-forming hubs. These latter large PF values may be explained by the increase in grain alignment efficiency due to stellar radiation from the newborn stars, combined with an ordered B-field structure. Conclusions. These observational results reveal for the first time the characteristics of the small-scale (down to 0.1 pc) B-field structure of a 10 pc-long hub-filament system. Our analyses show variations in the polarization properties along the sub-filaments that may be tracing the evolution of their physical properties during their interaction with the ridge and hubs. We also detect an impact of feedback from young high-mass stars on the local B-field structure and the polarization properties, which could put constraints on possible models for dust grain alignment and provide important hints as to the interplay between the star formation activity and interstellar B-fields.
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- Benabdallah, Nadia, et al.
(author)
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Beyond Average : a-Particle Distribution and Dose Heterogeneity in Bone Metastatic Prostate Cancer
- 2024
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In: Journal of Nuclear Medicine. - 0161-5505. ; 65:2, s. 245-251
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Journal article (peer-reviewed)abstract
- a-particle emitters are emerging as a potent modality for disseminated cancer therapy because of their high linear energy transfer and localized absorbed dose profile. Despite great interest and pharmaceutical development, there is scant information on the distribution of these agents at the scale of the a-particle pathlength. We sought to determine the distribution of clinically approved [223Ra]RaCl2 in bone metastatic castration-resistant prostate cancer at this resolution, for the first time to our knowledge, to inform activity distribution and dose at the near-cell scale. Methods: Biopsy specimens and blood were collected from 7 patients 24 h after administration. 223Ra activity in each sample was recorded, and the microstructure of biopsy specimens was analyzed by micro-CT. Quantitative autoradiography and histopathology were segmented and registered with an automated procedure. Activity distributions by tissue compartment and dosimetry calculations based on the MIRD formalism were performed. Results: We revealed the activity distribution differences across and within patient samples at the macro- and microscopic scales. Microdistribution analysis confirmed localized high-activity regions in a background of low-activity tissue. We evaluated heterogeneous a-particle emission distribution concentrated at bone–tissue interfaces and calculated spatially nonuniform absorbed-dose profiles. Conclusion: Primary patient data of radiopharmaceutical therapy distribution at the small scale revealed that 223Ra uptake is nonuniform. Dose estimates present both opportunities and challenges to enhance patient outcomes and are a first step toward personalized treatment approaches and improved understanding of a-particle radiopharmaceutical therapies.
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| 8. |
- Chen, Jiayu, et al.
(author)
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An Active Learning Method Based on Uncertainty and Complexity for Gearbox Fault Diagnosis
- 2019
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In: IEEE Access. - : IEEE. - 2169-3536. ; 7, s. 9022-9031
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Journal article (peer-reviewed)abstract
- It is crucial to implement an effective and accurate fault diagnosis of a gearbox for mechanical systems. However, being composed of many mechanical parts, a gearbox has a variety of failure modes resulting in the difficulty of accurate fault diagnosis. Moreover, it is easy to obtain raw vibration signals from real gearbox applications, but it requires significant costs to label them, especially for multi-fault modes. These issues challenge the traditional supervised learning methods of fault diagnosis. To solve these problems, we develop an active learning strategy based on uncertainty and complexity. Therefore, a new diagnostic method for a gearbox is proposed based on the present active learning, empirical mode decomposition-singular value decomposition (EMD-SVD) and random forests (RF). First, the EMD-SVD is used to obtain feature vectors from raw signals. Second, the proposed active learning scheme selects the most valuable unlabeled samples, which are then labeled and added to the training data set. Finally, the RF, trained by the new training data, is employed to recognize the fault modes of a gearbox. Two cases are studied based on experimental gearbox fault diagnostic data, and a supervised learning method, as well as other active learning methods, are compared. The results show that the proposed method outperforms the two common types of methods, thus validating its effectiveness and superiority.
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| 9. |
- Chen, Qinyu, et al.
(author)
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An Efficient Streaming Accelerator for Low Bit-Width Convolutional Neural Networks
- 2019
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In: Electronics. - : MDPI. - 2079-9292. ; 8:4
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Journal article (peer-reviewed)abstract
- Convolutional Neural Networks (CNNs) have been widely applied in various fields, such as image recognition, speech processing, as well as in many big-data analysis tasks. However, their large size and intensive computation hinder their deployment in hardware, especially on the embedded systems with stringent latency, power, and area requirements. To address this issue, low bit-width CNNs are proposed as a highly competitive candidate. In this paper, we propose an efficient, scalable accelerator for low bit-width CNNs based on a parallel streaming architecture. With a novel coarse grain task partitioning (CGTP) strategy, the proposed accelerator with heterogeneous computing units, supporting multi-pattern dataflows, can nearly double the throughput for various CNN models on average. Besides, a hardware-friendly algorithm is proposed to simplify the activation and quantification process, which can reduce the power dissipation and area overhead. Based on the optimized algorithm, an efficient reconfigurable three-stage activation-quantification-pooling (AQP) unit with the low power staged blocking strategy is developed, which can process activation, quantification, and max-pooling operations simultaneously. Moreover, an interleaving memory scheduling scheme is proposed to well support the streaming architecture. The accelerator is implemented with TSMC 40 nm technology with a core size of . It can achieve TOPS/W energy efficiency and area efficiency at 100.1mW, which makes it a promising design for the embedded devices.
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| 10. |
- Chen, Qinyu, et al.
(author)
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Smilodon : An Efficient Accelerator for Low Bit-Width CNNs with Task Partitioning
- 2019
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In: 2019 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS). - : IEEE. - 9781728103976
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Conference paper (peer-reviewed)abstract
- Convolutional Neural Networks (CNNs) have been widely applied in various fields such as image and video recognition, recommender systems, and natural language processing. However, the massive size and intensive computation loads prevent its feasible deployment in practice, especially on the embedded systems. As a highly competitive candidate, low bit-width CNNs are proposed to enable efficient implementation. In this paper, we propose Smilodon, a scalable, efficient accelerator for low bit-width CNNs based on a parallel streaming architecture, optimized with a task partitioning strategy. We also present the 3D systolic-like computing arrays fitting for convolutional layers. Our design is implemented on Zynq XC7ZO20 FPGA, which can satisfy the needs of real-time with a frame rate of 1, 622 FPS throughput, while consuming 2.1 Watt. To the best of our knowledge, our accelerator is superior to the state-of-the-art works in the tradeoff among throughput, power efficiency, and area efficiency.
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