arXiv subjects

Hao Chen

Publications and source records attributed to Hao Chen.

At least 1,405 records · Page 78Linked to original sources

arXiv2017

Attacks on the Search-RLWE problem with small errors

The Ring Learning-With-Errors (RLWE) problem shows great promise for post-quantum cryptography and homomorphic encryption. We describe a new attack on the non-dual search RLWE problem with small error widths, using ring homomorphisms to finite fields and the chi-squared statistical test. In particular, we identify a "subfield vulnerability" (Section 5.2) and give a new attack which finds this vulnerability by mapping to a finite field extension and detecting non-uniformity with respect to the number of elements in the subfield. We use this attack to give examples of vulnerable RLWE instances in Galois number fields. We also extend the well-known search-to-decision reduction result to Galois fields with any unramified prime modulus q, regardless of the residue degree f of q, and we use this in our attacks. The time complexity of our attack is O(nq2f), where n is the degree of K and f is the residue degree of q in K. We also show an attack on the non-dual (resp. dual) RLWE problem with narrow error distributions in prime cyclotomic rings when the modulus is a ramified prime (resp. any integer). We demonstrate the attacks in practice by finding many vulnerable instances and successfully attacking them. We include the code for all attacks.

cs.CR↗

arXiv2017

Security considerations for Galois non-dual RLWE families

We explore further the hardness of the non-dual discrete variant of the Ring-LWE problem for various number rings, give improved attacks for certain rings satisfying some additional assumptions, construct a new family of vulnerable Galois number fields, and apply some number theoretic results on Gauss sums to deduce the likely failure of these attacks for 2-power cyclotomic rings and unramified moduli.

cs.CR↗

arXiv2017

Geo-referencing Place from Everyday Natural Language Descriptions

Natural language place descriptions in everyday communication provide a rich source of spatial knowledge about places. An important step to utilize such knowledge in information systems is geo-referencing all the places referred to in these descriptions. Current techniques for geo-referencing places from text documents are using place name recognition and disambiguation; however, place descriptions often contain place references that are not known by gazetteers, or that are expressed in other, more flexible ways. Hence, the approach for geo-referencing presented in this paper starts from a place graph that contains the place references as well as spatial relationships extracted from place descriptions. Spatial relationships are used to constrain the locations of places and allow the later best-matching process for geo-referencing. The novel geo-referencing process results in higher precision and recall compared to state-of-art toponym resolution approaches on several tested place description datasets.

cs.AI↗

arXiv2017

MagNet: a Two-Pronged Defense against Adversarial Examples

Deep learning has shown promising results on hard perceptual problems in recent years. However, deep learning systems are found to be vulnerable to small adversarial perturbations that are nearly imperceptible to human. Such specially crafted perturbations cause deep learning systems to output incorrect decisions, with potentially disastrous consequences. These vulnerabilities hinder the deployment of deep learning systems where safety or security is important. Attempts to secure deep learning systems either target specific attacks or have been shown to be ineffective. In this paper, we propose MagNet, a framework for defending neural network classifiers against adversarial examples. MagNet does not modify the protected classifier or know the process for generating adversarial examples. MagNet includes one or more separate detector networks and a reformer network. Different from previous work, MagNet learns to differentiate between normal and adversarial examples by approximating the manifold of normal examples. Since it does not rely on any process for generating adversarial examples, it has substantial generalization power. Moreover, MagNet reconstructs adversarial examples by moving them towards the manifold, which is effective for helping classify adversarial examples with small perturbation correctly. We discuss the intrinsic difficulty in defending against whitebox attack and propose a mechanism to defend against graybox attack. Inspired by the use of randomness in cryptography, we propose to use diversity to strengthen MagNet. We show empirically that MagNet is effective against most advanced state-of-the-art attacks in blackbox and graybox scenarios while keeping false positive rate on normal examples very low.

cs.CR↗

arXiv2017

Automated Pulmonary Nodule Detection via 3D ConvNets with Online Sample Filtering and Hybrid-Loss Residual Learning

In this paper, we propose a novel framework with 3D convolutional networks (ConvNets) for automated detection of pulmonary nodules from low-dose CT scans, which is a challenging yet crucial task for lung cancer early diagnosis and treatment. Different from previous standard ConvNets, we try to tackle the severe hard/easy sample imbalance problem in medical datasets and explore the benefits of localized annotations to regularize the learning, and hence boost the performance of ConvNets to achieve more accurate detections. Our proposed framework consists of two stages: 1) candidate screening, and 2) false positive reduction. In the first stage, we establish a 3D fully convolutional network, effectively trained with an online sample filtering scheme, to sensitively and rapidly screen the nodule candidates. In the second stage, we design a hybrid-loss residual network which harnesses the location and size information as important cues to guide the nodule recognition procedure. Experimental results on the public large-scale LUNA16 dataset demonstrate superior performance of our proposed method compared with state-of-the-art approaches for the pulmonary nodule detection task.

cs.CV↗

arXiv2017

Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets

Automatic and accurate whole-heart and great vessel segmentation from 3D cardiac magnetic resonance (MR) images plays an important role in the computer-assisted diagnosis and treatment of cardiovascular disease. However, this task is very challenging due to ambiguous cardiac borders and large anatomical variations among different subjects. In this paper, we propose a novel densely-connected volumetric convolutional neural network, referred as DenseVoxNet, to automatically segment the cardiac and vascular structures from 3D cardiac MR images. The DenseVoxNet adopts the 3D fully convolutional architecture for effective volume-to-volume prediction. From the learning perspective, our DenseVoxNet has three compelling advantages. First, it preserves the maximum information flow between layers by a densely-connected mechanism and hence eases the network training. Second, it avoids learning redundant feature maps by encouraging feature reuse and hence requires fewer parameters to achieve high performance, which is essential for medical applications with limited training data. Third, we add auxiliary side paths to strengthen the gradient propagation and stabilize the learning process. We demonstrate the effectiveness of DenseVoxNet by comparing it with the state-of-the-art approaches from HVSMR 2016 challenge in conjunction with MICCAI, and our network achieves the best dice coefficient. We also show that our network can achieve better performance than other 3D ConvNets but with fewer parameters.

cs.CV↗

arXiv2017

ScanNet: A Fast and Dense Scanning Framework for Metastatic Breast Cancer Detection from Whole-Slide Images

Lymph node metastasis is one of the most significant diagnostic indicators in breast cancer, which is traditionally observed under the microscope by pathologists. In recent years, computerized histology diagnosis has become one of the most rapidly expanding fields in medical image computing, which alleviates pathologists' workload and reduces misdiagnosis rate. However, automatic detection of lymph node metastases from whole slide images remains a challenging problem, due to the large-scale data with enormous resolutions and existence of hard mimics. In this paper, we propose a novel framework by leveraging fully convolutional networks for efficient inference to meet the speed requirement for clinical practice, while reconstructing dense predictions under different offsets for ensuring accurate detection on both micro- and macro-metastases. Incorporating with the strategies of asynchronous sample prefetching and hard negative mining, the network can be effectively trained. Extensive experiments on the benchmark dataset of 2016 Camelyon Grand Challenge corroborated the efficacy of our method. Compared with the state-of-the-art methods, our method achieved superior performance with a faster speed on the tumor localization task and surpassed human performance on the WSI classification task.

cs.CV↗

arXiv2017

Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge

Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few studies that provide a comparative performance evaluation of different systems on a common database. We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set. In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in one of the two tracks: 1) the complete nodule detection track where a complete CAD system should be developed, or 2) the false positive reduction track where a provided set of nodule candidates should be classified. This paper describes the setup of LUNA16 and presents the results of the challenge so far. Moreover, the impact of combining individual systems on the detection performance was also investigated. It was observed that the leading solutions employed convolutional networks and used the provided set of nodule candidates. The combination of these solutions achieved an excellent sensitivity of over 95% at fewer than 1.0 false positives per scan. This highlights the potential of combining algorithms to improve the detection performance. Our observer study with four expert readers has shown that the best system detects nodules that were missed by expert readers who originally annotated the LIDC-IDRI data. We released this set of additional nodules for further development of CAD systems.

cs.CV↗

arXiv2017

RGB-D Salient Object Detection Based on Discriminative Cross-modal Transfer Learning

In this work, we propose to utilize Convolutional Neural Networks to boost the performance of depth-induced salient object detection by capturing the high-level representative features for depth modality. We formulate the depth-induced saliency detection as a CNN-based cross-modal transfer problem to bridge the gap between the "data-hungry" nature of CNNs and the unavailability of sufficient labeled training data in depth modality. In the proposed approach, we leverage the auxiliary data from the source modality effectively by training the RGB saliency detection network to obtain the task-specific pre-understanding layers for the target modality. Meanwhile, we exploit the depth-specific information by pre-training a modality classification network that encourages modal-specific representations during the optimizing course. Thus, it could make the feature representations of the RGB and depth modalities as discriminative as possible. These two modules are pre-trained independently and then stitched to initialize and optimize the eventual depth-induced saliency detection model. Experiments demonstrate the effectiveness of the proposed novel pre-training strategy as well as the significant and consistent improvements of the proposed approach over other state-of-the-art methods.

cs.CV↗

arXiv2017

Frequency manipulation of light by photonic gauge potentials

The ability to manipulate the frequency of light is of great importance in both fundamental quantum sciences and practical applications. Traditional method for frequency conversion relies on nonlinear optical processes, which are faced with the obstacles of low efficiency and limited bandwidth. Recent developments of topological photonics introduce the concepts of gauge potentials and magnetic fields to the realm of photons. Here, we demonstrate versatile frequency manipulation of light via photonic gauge potentials in a fiber-optic communication system. The gauge potential of frequency dimension is realized by controlling the initial phase of electro-optic phase modulation. A maximum 50 GHz frequency shift and three-fold bandwidth expansion for frequency combs are achieved by choosing different gauge potentials. By adopting two cascaded phase modulators with different gauge potentials, we also realize "negative refraction" for frequency combs and frequency "perfect imaging"for arbitrarily input spectra. These results may pave the way towards versatile frequency management in quantum optics and classical optical communications.

physics.optics↗

arXiv2017

Scribability problems for polytopes

In this paper we study various scribability problems for polytopes. We begin with the classical $k$-scribability problem proposed by Steiner and generalized by Schulte, which asks about the existence of $d$-polytopes that cannot be realized with all $k$-faces tangent to a sphere. We answer this problem for stacked and cyclic polytopes for all values of $d$ and $k$. We then continue with the weak scribability problem proposed by Grünbaum and Shephard, for which we complete the work of Schulte by presenting non weakly circumscribable $3$-polytopes. Finally, we propose new $(i,j)$-scribability problems, in a strong and a weak version, which generalize the classical ones. They ask about the existence of $d$-polytopes that can not be realized with all their $i$-faces "avoiding" the sphere and all their $j$-faces "cutting" the sphere. We provide such examples for all the cases where $j-i \le d-3$.

math.MG↗

arXiv2017

Competition brings out the best: Modeling the frustration between curvature energy and chain stretching energy of lyotropic liquid crystals in bicontinuous cubic phases

It is commonly considered that the frustration between the curvature energy and the chain stretching energy plays an important role in the formation of lyotropic liquid crystals in bicontinuous cubic phases. Theoretic and numeric calculations were performed for two extreme cases: Parallel surfaces eliminate the variance of the chain length; constant mean curvature surfaces eliminate the variance of the mean curvature. We have implemented a model with Brakke's Surface Evolver which allows a competition between the two variances. The result shows a compromise of the two limiting geometries. With data from real systems, we are able to recover the G--D--P phase sequence which was observed in experiments.

cond-mat.soft↗

arXiv2016

Dense gas in low-metallicity galaxies

Stars form out of the densest parts of molecular clouds. Far-IR emission can be used to estimate the Star Formation Rate (SFR) and high dipole moment molecules, typically HCN, trace the dense gas. A strong correlation exists between HCN and Far-IR emission, with the ratio being nearly constant, over a large range of physical scales. A few recent observations have found HCN to be weak with respect to the Far-IR and CO in subsolar metallicity (low-Z) objects. We present observations of the Local Group galaxies M33, IC10, and NGC6822 with the IRAM 30meter and NRO 45m telescopes, greatly improving the sample of low-Z galaxies observed. HCN, HCO$^+$, CS, C$_2$H, and HNC have been detected. Compared to solar metallicity galaxies, the Nitrogen-bearing species are weak (HCN, HNC) or not detected (CN, HNCO, N$_2$H$^+$) relative to Far-IR or CO emission. HCO$^+$ and C$_2$H emission is normal with respect to CO and Far-IR. While $^{13}$CO is the usual factor 10 weaker than $^{12}$CO, C$^{18}$O emission was not detected down to very low levels. Including earlier data, we find that the HCN/HCO$^+$ ratio varies with metallicity (O/H) and attribute this to the sharply decreasing Nitrogen abundance. The dense gas fraction, traced by the HCN/CO and HCO$^+$/CO ratios, follows the SFR but in the low-Z objects the HCO$^+$ is much easier to measure. Combined with larger and smaller scale measurements, the HCO$^+$ line appears to be an excellent tracer of dense gas and varies linearly with the SFR for both low and high metallicities.

astro-ph.GA↗

arXiv2016

Dense Gas in the Outer Spiral Arm of M51

There is a linear relation between the mass of dense gas, traced by the HCN(1-0) luminosity, and the star formation rate (SFR), traced by the far-infrared luminosity. Recent observations of galactic disks have shown some systematic variations. In order to explore the SFR-dense gas link at high resolution ($\sim 4"$, $\sim 150$ pc) in the outer disk of an external galaxy, we have mapped a region about 5 kpc from the center along the northern spiral arm of M51 in the HCN(1-0), HCO$^+$(1-0) and HNC(1-0) emission lines using the Northern Extended Millimeter Array (NOEMA) interferometer. The HCN and HCO$^+$ lines were detected in 6 giant molecular associations (GMAs) while HNC emission was only detected in the two brightest GMAs. One of the GMAs hosts a powerful HII region and HCN is stronger than HCO$^+$ there. Comparing with observations of GMAs in the disks of M31 and M33 at similar angular resolution ($\sim 100$ pc), we find that GMAs in the outer disk of M51 are brighter in both HCN and HCO$^+$ lines by a factor of 3 on average. However, the $I_{HCN}/I_{CO}$ and $I_{HCO^+}/I_{CO}$ ratios are similar to the ratios in nearby galactic disks and the Galactic plane. Using the Herschel 70 $μ$m data to trace the total IR luminosity at the resolution of the GMAs, we find that both the L$_{IR}$-L$_{HCN}$ and L$_{IR}$-L$_{HCO^+}$ relations in the outer disk GMAs are consistent with the proportionality between the L$_{IR}$ and the dense gas mass established globally in galaxies within the scatter. The IR/HCN and IR/HCO$^+$ ratios of the GMAs vary by a factor of 3, probably depending on whether massive stars are forming or not.

astro-ph.GA↗

arXiv2016

Selectively Balancing Unit Vectors

A set $U$ of unit vectors is selectively balancing if one can find two disjoint subsets $U^+$ and $U^-$, not both empty, such that the Euclidean distance between the sum of $U^+$ and the sum of $U^-$ is smaller than $1$. We prove that the minimum number of unit vectors that guarantee a selectively balancing set in $\mathbb{R}^n$ is asymptotically $\frac{1}{2} n \log n$.

math.MG↗

arXiv2016

Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest

Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer. In clinical practice, the morphology of intestinal glands, including architectural appearance and glandular formation, is used by pathologists to inform prognosis and plan the treatment of individual patients. However, achieving good inter-observer as well as intra-observer reproducibility of cancer grading is still a major challenge in modern pathology. An automated approach which quantifies the morphology of glands is a solution to the problem. This paper provides an overview to the Gland Segmentation in Colon Histology Images Challenge Contest (GlaS) held at MICCAI'2015. Details of the challenge, including organization, dataset and evaluation criteria, are presented, along with the method descriptions and evaluation results from the top performing methods.

cs.CV↗

arXiv2016

VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation

Recently deep residual learning with residual units for training very deep neural networks advanced the state-of-the-art performance on 2D image recognition tasks, e.g., object detection and segmentation. However, how to fully leverage contextual representations for recognition tasks from volumetric data has not been well studied, especially in the field of medical image computing, where a majority of image modalities are in volumetric format. In this paper we explore the deep residual learning on the task of volumetric brain segmentation. There are at least two main contributions in our work. First, we propose a deep voxelwise residual network, referred as VoxResNet, which borrows the spirit of deep residual learning in 2D image recognition tasks, and is extended into a 3D variant for handling volumetric data. Second, an auto-context version of VoxResNet is proposed by seamlessly integrating the low-level image appearance features, implicit shape information and high-level context together for further improving the volumetric segmentation performance. Extensive experiments on the challenging benchmark of brain segmentation from magnetic resonance (MR) images corroborated the efficacy of our proposed method in dealing with volumetric data. We believe this work unravels the potential of 3D deep learning to advance the recognition performance on volumetric image segmentation.

cs.CV↗

arXiv2016

Iterative Multi-domain Regularized Deep Learning for Anatomical Structure Detection and Segmentation from Ultrasound Images

Accurate detection and segmentation of anatomical structures from ultrasound images are crucial for clinical diagnosis and biometric measurements. Although ultrasound imaging has been widely used with superiorities such as low cost and portability, the fuzzy border definition and existence of abounding artifacts pose great challenges for automatically detecting and segmenting the complex anatomical structures. In this paper, we propose a multi-domain regularized deep learning method to address this challenging problem. By leveraging the transfer learning from cross domains, the feature representations are effectively enhanced. The results are further improved by the iterative refinement. Moreover, our method is quite efficient by taking advantage of a fully convolutional network, which is formulated as an end-to-end learning framework of detection and segmentation. Extensive experimental results on a large-scale database corroborated that our method achieved a superior detection and segmentation accuracy, outperforming other methods by a significant margin and demonstrating competitive capability even compared to human performance.

cs.CV↗