Efficient classification using parallel and scalable compressed model and Its application on intrusion detection

In order to achieve high efficiency of classification in intrusion detection, a compressed model is proposed in this paper which combines horizontal compression with vertical compression. OneR is utilized as horizontal com-pression for attribute reduction, and affinity propagation is employed as vertical compression to select small representative exemplars from large training data. As to be able to computationally compress the larger volume of training data with scalability, MapReduce based parallelization approach is then implemented and evaluated for each step of the model compression process abovementioned, on which common but efficient classification methods can be directly used. Experimental application study on two publicly available datasets of intrusion detection, KDD99 and CMDC2012, demonstrates that the classification using the compressed model proposed can effectively speed up the detection procedure at up to 184 times, most importantly at the cost of a minimal accuracy difference with less than 1% on average

Efficient Simulation of Airborne SAR Raw Data in Case of Motion Errors

In the simulation of SAR raw data, it is well-known that the frequency-domain algorithm is more efficient than a time-domain algorithm, making it is more suitable for extended scene simulation. However, the frequency-domain algorithm is perhaps better suited for ideal linear motion and requires some degrees of approximations to take the nonlinear motion effects. This chapter presents an efficient simulation approach based on hybrid time and frequency-domain algorithms under certain assumptions. The algorithm has high efficiency and is suitable for the simulation of extended scenes, which demands highly computational resources. The computational complexity of the proposed algorithm is analyzed, followed by numerical results to demonstrate the effectiveness and efficiency of the proposed approach

Population Properties of Gravitational-Wave Neutron Star--Black Hole Mergers

Over the course of the third observing run of LIGO-Virgo-KAGRA Collaboration, several gravitational-wave (GW) neutron star--black hole (NSBH) candidates have been announced. By assuming these candidates are real signals and of astrophysical origins, we analyze the population properties of the mass and spin distributions for GW NSBH mergers. We find that the primary BH mass distribution of NSBH systems, whose shape is consistent with that inferred from the GW binary BH (BBH) primaries, can be well described as a power-law with an index of $\alpha = 4.8^{+4.5}_{-2.8}$ plus a high-mass Gaussian component peaking at $\sim33^{+14}_{-9}\,M_\odot$. The NS mass spectrum could be shaped as a near flat distribution between $\sim1.0-2.1\,M_\odot$. The constrained NS maximum mass agrees with that inferred from NSs in our Galaxy. If GW190814 and GW200210 are NSBH mergers, the posterior results of the NS maximum mass would be always larger than $\sim2.5\,M_\odot$ and significantly deviate from that inferred in the Galactic NSs. The effective inspiral spin and effective precession spin of GW NSBH mergers are measured to potentially have near-zero distributions. The negligible spins for GW NSBH mergers imply that most events in the universe should be plunging events, which supports the standard isolated formation channel of NSBH binaries. More NSBH mergers to be discovered in the fourth observing run would help to more precisely model the population properties of cosmological NSBH mergers.Comment: 14 pages, 5 figures, 3 tables, accepted for publication in Ap

Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalization

In this paper, we analyse the generalization ability of binary classifiers for the task of deepfake detection. We find that the stumbling block to their generalization is caused by the unexpected learned identity representation on images. Termed as the Implicit Identity Leakage, this phenomenon has been qualitatively and quantitatively verified among various DNNs. Furthermore, based on such understanding, we propose a simple yet effective method named the ID-unaware Deepfake Detection Model to reduce the influence of this phenomenon. Extensive experimental results demonstrate that our method outperforms the state-of-the-art in both in-dataset and cross-dataset evaluation. The code is available at https://github.com/megvii-research/CADDM.Comment: Accepted by CVPR 202

Population Properties of Gravitational-wave Neutron Star-Black Hole Mergers

Over the course of the third observing run of the LIGO-Virgo-KAGRA Collaboration, several gravitational-wave (GW) neutron star-black hole (NSBH) candidates have been announced. By assuming that these candidates are real signals with astrophysical origins, we analyze the population properties of the mass and spin distributions for GW NSBH mergers. We find that the primary BH mass distribution of NSBH systems, whose shape is consistent with that inferred from the GW binary BH (BBH) primaries, can be well described as a power law with an index of α=4.8-2.8+4.5 plus a high-mass Gaussian component peaking at ∼33-9+14M⊙ . The NS mass spectrum could be shaped as a nearly flat distribution between ∼1.0 and 2.1 M ⊙. The constrained NS maximum mass agrees with that inferred from NSs in our Galaxy. If GW190814 and GW200210 are NSBH mergers, the posterior results of the NS maximum mass would be always larger than ∼2.5 M ⊙ and significantly deviate from that inferred in Galactic NSs. The effective inspiral spin and effective precession spin of GW NSBH mergers are measured to potentially have near-zero distributions. The negligible spins for GW NSBH mergers imply that most events in the universe should be plunging events, which support the standard isolated formation channel of NSBH binaries. More NSBH mergers to be discovered in the fourth observing run would help to more precisely model the population properties of cosmological NSBH mergers. © 2022. The Author(s). Published by the American Astronomical Society

Formation of Lower Mass-gap Black Hole--Neutron Star Binary Mergers through Super-Eddington Stable Mass Transfer

Super-Eddington accretion of neutron stars (NSs) has been suggested both observationally and theoretically. In this paper, we propose that NSs in close-orbit binary systems with companions of helium (He) stars, most of which systems form after the common-envelope phase, could experience super-Eddington stable Case BB/BC mass transfer (MT), and can sometimes occur accretion-induced collapses (AICs) to form lower mass-gap black holes (mgBHs). Our detailed binary evolution simulations reveal that AIC events tend to happen if the primaries NS have an initial mass $\gtrsim1.7\,M_\odot$ with an accretion rate of $\gtrsim300$ times the Eddington limit. These mgBHs would have a mass nearly equal to or slightly higher than the NS maximum mass. The remnant mgBH--NS binaries after the core collapses of He stars are potential progenitors of gravitational-wave (GW) source. Multimessenger observation between GW and kilonova signals from a population of high-mass binary NS and mgBH--NS mergers formed through super-Eddington stable MT are helpful in constraining the maximum mass and equation of state of NSs. S230529ay, a mgBH--NS merger candidate recently detected in the fourth observing run of the LIGO-Virgo-KAGRA Collaboration, could possibly originate from this formation scenario.Comment: Submitted to MNRAS on September 29th, 10 pages, 5 figures, comments are welcom

A multiscale point-supervised network for counting maize tassels in the wild

Accurate counting of maize tassels is essential for monitoring crop growth and estimating crop yield. Recently, deep-learning-based object detection methods have been used for this purpose, where plant counts are estimated from the number of bounding boxes detected. However, these methods suffer from 2 issues: (a) The scales of maize tassels vary because of image capture from varying distances and crop growth stage; and (b) tassel areas tend to be affected by occlusions or complex backgrounds, making the detection inefficient. In this paper, we propose a multiscale lite attention enhancement network (MLAENet) that uses only point-level annotations (i.e., objects labeled with points) to count maize tassels in the wild. Specifically, the proposed method includes a new multicolumn lite feature extraction module that generates a scale-dependent density map by exploiting multiple dilated convolutions with different rates, capturing rich contextual information at different scales more effectively. In addition, a multifeature enhancement module that integrates an attention strategy is proposed to enable the model to distinguish between tassel areas and their complex backgrounds. Finally, a new up-sampling module, UP-Block, is designed to improve the quality of the estimated density map by automatically suppressing the gridding effect during the up-sampling process. Extensive experiments on 2 publicly available tassel-counting datasets, maize tassels counting and maize tassels counting from unmanned aerial vehicle, demonstrate that the proposed MLAENet achieves marked advantages in counting accuracy and inference speed compared to state-of-the-art methods. The model is publicly available at https://github.com/ShiratsuyuShigure/MLAENet-pytorch/tree/main

A dual-branch weakly supervised learning based network for accurate mapping of woody vegetation from remote sensing images

Mapping woody vegetation from aerial images is an important task bluein environment monitoring and management. A few studies have shown that semantic segmentation methods involving deep learning achieve significantly better performance in mapping than methods involving field-based measurement and handcrafted features. However, current deep networks used for mapping vegetation require labour-intensive pixel-level annotations. Thus, this paper proposes the use of image-level annotations and a weakly supervised semantic segmentation (WSSS) network for mapping woody vegetation based on Unmanned Aerial Vehicle (UAV) imagery. The network comprises a Localization Branch (LB) and an Attention Relocation Branch (ARB). The LB is trained in stage 1 of the mapping to identify regions with the most discriminative vegetation, while the ARB is introduced to better mine semantic information, which enhances the ability of the class activation maps (CAMs) to represent useful information. The ARB inherits the weights from the LB in stage 2 and uses a Multi-layer Attention Refocus Structure (MARS) into the network to expand the receptive field to enable the model to process global features. Thus, same-category regions that are located farther apart are better captured. Finally, the region focused by the dual branches are integrated to more accurately cover the areas to be segmented. Using UAV imagery datasets, namely UOPNOA and MiniFrance, along with quantitative metrics and qualitative results, the network demonstrates performance better than existing state-of-the-art related methods. The effectiveness and generalization of each module of the network are validated by ablation experiments. The code for implementing the network will be accessible on https://github.com/Mr-catc/DWSLNet

Sources and distribution of particulate organic carbon in Great Wall Cove and Ardley Cove, King George Island, West Antarctica

Concentrations of chlorophyll-a (Chl-a), particulate organic carbon (POC) and its stable carbon isotope composition (δ13C) were analyzed to investigate the biogeochemical characteristics and sources of POC in Great Wall Cove (GWC) and Ardley Cove (AC) during the austral summer. POC concentrations ranged from 50.51 to 115.41 μg·L−1 (mean±1 standard deviation: 77.69±17.27 μg·L−1) in GWC and from 63.42 to 101.79 μg·L−1 (82.67±11.83 μg·L−1) in AC. The POC δ13C ranged from −30.83‰ to −26.12‰ (−27.40‰±0.96‰) in GWC and from −28.21‰ to −26.65‰ (−27.45‰±0.47‰) in AC. The temperature and salinity results showed distinct runoff signals in both GWC and AC, although the δ13C data and POC distribution indicate a negligible influence of land sources upon POC. The δ13C values suggest that POC is of predominantly marine origin. The POC/Chl-a ratio and the relationship between POC and Chl-a indicate that phytoplankton, organic detritus and heterotrophic organisms are significant contributors to POC in GWC and AC