147 research outputs found

    Adversarial Deep Structured Nets for Mass Segmentation from Mammograms

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    Mass segmentation provides effective morphological features which are important for mass diagnosis. In this work, we propose a novel end-to-end network for mammographic mass segmentation which employs a fully convolutional network (FCN) to model a potential function, followed by a CRF to perform structured learning. Because the mass distribution varies greatly with pixel position, the FCN is combined with a position priori. Further, we employ adversarial training to eliminate over-fitting due to the small sizes of mammogram datasets. Multi-scale FCN is employed to improve the segmentation performance. Experimental results on two public datasets, INbreast and DDSM-BCRP, demonstrate that our end-to-end network achieves better performance than state-of-the-art approaches. \footnote{https://github.com/wentaozhu/adversarial-deep-structural-networks.git}Comment: Accepted by ISBI2018. arXiv admin note: substantial text overlap with arXiv:1612.0597

    BSL: Understanding and Improving Softmax Loss for Recommendation

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    Loss functions steer the optimization direction of recommendation models and are critical to model performance, but have received relatively little attention in recent recommendation research. Among various losses, we find Softmax loss (SL) stands out for not only achieving remarkable accuracy but also better robustness and fairness. Nevertheless, the current literature lacks a comprehensive explanation for the efficacy of SL. Toward addressing this research gap, we conduct theoretical analyses on SL and uncover three insights: 1) Optimizing SL is equivalent to performing Distributionally Robust Optimization (DRO) on the negative data, thereby learning against perturbations on the negative distribution and yielding robustness to noisy negatives. 2) Comparing with other loss functions, SL implicitly penalizes the prediction variance, resulting in a smaller gap between predicted values and and thus producing fairer results. Building on these insights, we further propose a novel loss function Bilateral SoftMax Loss (BSL) that extends the advantage of SL to both positive and negative sides. BSL augments SL by applying the same Log-Expectation-Exp structure to positive examples as is used for negatives, making the model robust to the noisy positives as well. Remarkably, BSL is simple and easy-to-implement -- requiring just one additional line of code compared to SL. Experiments on four real-world datasets and three representative backbones demonstrate the effectiveness of our proposal. The code is available at https://github.com/junkangwu/BS

    On the Division Property of SIMON48 and SIMON64

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    {\sc Simon} is a family of lightweight block ciphers published by the U.S. National Security Agency (NSA) in 2013. Due to its novel and bit-based design, integral cryptanalysis on {\sc Simon} seems a tough job. At EUROCRYPT 2015 Todo proposed division property which is a generalized integral property, and he applied this technique to searching integral distinguishers of {\sc Simon} block ciphers by considering the left and right halves of {\sc Simon} independently. As a result, he found 11-round integral distinguishers for both {\sc Simon}48 and {\sc Simon}64. Recently, at FSE 2016 Todo \emph{et al.} proposed bit-based division property that considered each bit independently. This technique can find more accurate distinguishers, however, as pointed out by Todo \emph{et al.} the time and memory complexity is bounded by 2n 2^n for an n n-bit block cipher. Thus, bit-based division property is only applicable to {\sc Simon}32. In this paper we propose a new technique that achieves a trade-off between considering each bit independently and considering left and right halves as a whole, which is actually a trade-off between time-memory and the accuracy of the distinguishers. We proceed by splitting the state of {\sc Simon} into small pieces and study the division property propagations of circular shift and bitwise AND operations under the state partition. Moreover, we propose two different state partitions and study the influences of different partitions on the propagation of division property. We find that different partitions greatly impact the division property propagation of circular shift which will finally result in a big difference on the length of integral distinguishers. By using a tailored search algorithm for {\sc Simon}, we find 12-round integral distinguishers for {\sc Simon}48 and {\sc Simon}64 respectively, which improve Todo\u27s results by one round for both variants

    A Novel Statistical Approach for Clustering Positive Data Based on Finite Inverted Beta-Liouville Mixture Models

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    Nowadays, a great number of positive data has been occurred naturally in many applications, however, it was not adequately analyzed. In this article, we propose a novel statistical approach for clustering multivariate positive data. Our approach is based on a finite mixture model of inverted Beta-Liouville (IBL) distributions, which is proper choice for modeling and analysis of positive vector data. We develop two different approaches to learn the proposed mixture model. Firstly, the maximum likelihood (ML) is utilized to estimate parameters of the finite inverted Beta-Liouville mixture model in which the right number of mixture components is determined according to the minimum message length (MML) criterion. Secondly, the variational Bayes (VB) is adopted to learn our model where the parameters and the number of mixture components can be determined simultaneously in a unified framework, without the requirement of using information criteria. We investigate the effectiveness of our model by conducting a series of experiments on both synthetic and real data sets

    Original Article Establishing a rapid animal model of osteoporosis with ovariectomy plus low calcium diet in rats

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    Abstract: The objective of this study was to rapidly develop osteoporotic model animals by combining ovariectomy with a low calcium diet in rats. Thirty, eight-week-old, female, Sprague-Dawley rats were either sham-operated (Sham) or ovariectomized (Ovx) and divided into three groups: Sham, Ovx, and Ovx + low calcium diet. Rats in the Sham and Ovx groups were fed a standard diet containing 1.1% w/w calcium while rats in the Ovx + low calcium diet group were fed a diet containing 0.1% w/w calcium. Serum osteocalcin and bone mineral density (BMD) of the lumbar vertebrae were measured 4 and 8 weeks after surgery. The rats were euthanized 12 weeks after surgery, and the BMD of the right femur and histomorphometry of the femoral neck were assessed at that time. The Ovx + low-calcium diet group had a significantly lower mean BMD of the lumbar vertebra and higher mean serum osteocalcin concentration than the Sham and Ovx groups. Twelve weeks after surgery, rats in the Ovx + low calcium diet group had a significantly lower BMD, smaller Tb.Th and Tb.N, and larger Tb.Sp of the right femoral neck than did rats in the Sham and Ovx groups. These data indicate that a low calcium diet can significantly accelerate bone loss in ovariectomized rats. Combining ovariectomy and a low calcium diet can save considerable time in the creation of osteoporotic model animals

    Ultrafast Switching from the Charge Density Wave Phase to a Metastable Metallic State in 1T-TiSe2_2

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    The ultrafast electronic structures of the charge density wave material 1T-TiSe2_2 were investigated by high-resolution time- and angle-resolved photoemission spectroscopy. We found that the quasiparticle populations drove ultrafast electronic phase transitions in 1T-TiSe2_2 within 100 fs after photoexcitation, and a metastable metallic state, which was significantly different from the equilibrium normal phase, was evidenced far below the charge density wave transition temperature. Detailed time- and pump-fluence-dependent experiments revealed that the photoinduced metastable metallic state was a result of the halted motion of the atoms through the coherent electron-phonon coupling process, and the lifetime of this state was prolonged to picoseconds with the highest pump fluence used in this study. Ultrafast electronic dynamics were well captured by the time-dependent Ginzburg-Landau model. Our work demonstrates a mechanism for realizing novel electronic states by photoinducing coherent motion of atoms in the lattice.Comment: 13 Pages, 10 figure

    Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception

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    Vehicle-to-everything (V2X) autonomous driving opens up a promising direction for developing a new generation of intelligent transportation systems. Collaborative perception (CP) as an essential component to achieve V2X can overcome the inherent limitations of individual perception, including occlusion and long-range perception. In this survey, we provide a comprehensive review of CP methods for V2X scenarios, bringing a profound and in-depth understanding to the community. Specifically, we first introduce the architecture and workflow of typical V2X systems, which affords a broader perspective to understand the entire V2X system and the role of CP within it. Then, we thoroughly summarize and analyze existing V2X perception datasets and CP methods. Particularly, we introduce numerous CP methods from various crucial perspectives, including collaboration stages, roadside sensors placement, latency compensation, performance-bandwidth trade-off, attack/defense, pose alignment, etc. Moreover, we conduct extensive experimental analyses to compare and examine current CP methods, revealing some essential and unexplored insights. Specifically, we analyze the performance changes of different methods under different bandwidths, providing a deep insight into the performance-bandwidth trade-off issue. Also, we examine methods under different LiDAR ranges. To study the model robustness, we further investigate the effects of various simulated real-world noises on the performance of different CP methods, covering communication latency, lossy communication, localization errors, and mixed noises. In addition, we look into the sim-to-real generalization ability of existing CP methods. At last, we thoroughly discuss issues and challenges, highlighting promising directions for future efforts. Our codes for experimental analysis will be public at https://github.com/memberRE/Collaborative-Perception.Comment: 19 page
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