arXiv subjects

Qian Chen

Publications and source records attributed to Qian Chen.

At least 361 records · Page 20Linked to original sources

arXiv2017

A Sequential Neural Encoder with Latent Structured Description for Modeling Sentences

In this paper, we propose a sequential neural encoder with latent structured description (SNELSD) for modeling sentences. This model introduces latent chunk-level representations into conventional sequential neural encoders, i.e., recurrent neural networks (RNNs) with long short-term memory (LSTM) units, to consider the compositionality of languages in semantic modeling. An SNELSD model has a hierarchical structure that includes a detection layer and a description layer. The detection layer predicts the boundaries of latent word chunks in an input sentence and derives a chunk-level vector for each word. The description layer utilizes modified LSTM units to process these chunk-level vectors in a recurrent manner and produces sequential encoding outputs. These output vectors are further concatenated with word vectors or the outputs of a chain LSTM encoder to obtain the final sentence representation. All the model parameters are learned in an end-to-end manner without a dependency on additional text chunking or syntax parsing. A natural language inference (NLI) task and a sentiment analysis (SA) task are adopted to evaluate the performance of our proposed model. The experimental results demonstrate the effectiveness of the proposed SNELSD model on exploring task-dependent chunking patterns during the semantic modeling of sentences. Furthermore, the proposed method achieves better performance than conventional chain LSTMs and tree-structured LSTMs on both tasks.

cs.CL↗

arXiv2017

Automated Behavioral Analysis of Malware A Case Study of WannaCry Ransomware

Ransomware, a class of self-propagating malware that uses encryption to hold the victims' data ransom, has emerged in recent years as one of the most dangerous cyber threats, with widespread damage; e.g., zero-day ransomware WannaCry has caused world-wide catastrophe, from knocking U.K. National Health Service hospitals offline to shutting down a Honda Motor Company in Japan[1]. Our close collaboration with security operations of large enterprises reveals that defense against ransomware relies on tedious analysis from high-volume systems logs of the first few infections. Sandbox analysis of freshly captured malware is also commonplace in operation. We introduce a method to identify and rank the most discriminating ransomware features from a set of ambient (non-attack) system logs and at least one log stream containing both ambient and ransomware behavior. These ranked features reveal a set of malware actions that are produced automatically from system logs, and can help automate tedious manual analysis. We test our approach using WannaCry and two polymorphic samples by producing logs with Cuckoo Sandbox during both ambient, and ambient plus ransomware executions. Our goal is to extract the features of the malware from the logs with only knowledge that malware was present. We compare outputs with a detailed analysis of WannaCry allowing validation of the algorithm's feature extraction and provide analysis of the method's robustness to variations of input data\textemdash changing quality/quantity of ambient data and testing polymorphic ransomware. Most notably, our patterns are accurate and unwavering when generated from polymorphic WannaCry copies, on which 63 (of 63 tested) anti-virus (AV) products fail.

cs.CR↗

arXiv2017

Adaptive compressed 3D imaging based on wavelet trees and Hadamard multiplexing with a single photon counting detector

Photon counting 3D imaging allows to obtain 3D images with single-photon sensitivity and sub-ns temporal resolution. However, it is challenging to scale to high spatial resolution. In this work, we demonstrate a photon counting 3D imaging technique with short-pulsed structured illumination and a single-pixel photon counting detector. The proposed multi-resolution photon counting 3D imaging technique acquires a high-resolution 3D image from a coarse image and edges at successfully finer resolution sampled by Hadamard multiplexing along the wavelet trees. The detected power is significantly increased thanks to the Hadamard multiplexing. Both the required measurements and the reconstruction time can be significantly reduced by performing wavelet-tree-based regions of edges predication and Hadamard demultiplexing, which makes the proposed technique suitable for scenes with high spatial resolution. The experimental results indicate that a 3D image at resolution up to 512*512 pixels can be acquired and retrieved with practical time as low as 17 seconds.

cs.CV↗

arXiv2017

Fast Depth Imaging Denoising with the Temporal Correlation of Photons

This paper proposes a novel method to filter out the false alarm of LiDAR system by using the temporal correlation of target reflected photons. Because of the inevitable noise, which is due to background light and dark counts of the detector, the depth imaging of LiDAR system exists a large estimation error. Our method combines the Poisson statistical model with the different distribution feature of signal and noise in the time axis. Due to selecting a proper threshold, our method can effectively filter out the false alarm of system and use the ToFs of detected signal photons to rebuild the depth image of the scene. The experimental results reveal that by our method it can fast distinguish the distance between two close objects, which is confused due to the high background noise, and acquire the accurate depth image of the scene. Our method need not increase the complexity of the system and is useful in power-limited depth imaging.

physics.ins-det↗

arXiv2017

Recurrent Neural Network-Based Sentence Encoder with Gated Attention for Natural Language Inference

The RepEval 2017 Shared Task aims to evaluate natural language understanding models for sentence representation, in which a sentence is represented as a fixed-length vector with neural networks and the quality of the representation is tested with a natural language inference task. This paper describes our system (alpha) that is ranked among the top in the Shared Task, on both the in-domain test set (obtaining a 74.9% accuracy) and on the cross-domain test set (also attaining a 74.9% accuracy), demonstrating that the model generalizes well to the cross-domain data. Our model is equipped with intra-sentence gated-attention composition which helps achieve a better performance. In addition to submitting our model to the Shared Task, we have also tested it on the Stanford Natural Language Inference (SNLI) dataset. We obtain an accuracy of 85.5%, which is the best reported result on SNLI when cross-sentence attention is not allowed, the same condition enforced in RepEval 2017.

cs.CL↗

arXiv2017

Analytical calculation for the gluon fragmentation into spin-triplet S-wave quarkonium

Fragmentation is the dominant mechanism for hadron production with high transverse momentum. For spin-triplet S-wave heavy quarkonium production, contribution of gluon fragmenting to color-singlet channel has been numerically calculated since 1993. However, there is still no analytic expression available up to now because of its complexity. In this paper, we calculate both polarization-summed and polarized fragmentation functions of gluon fragmenting to a heavy quark-antiquark pair with quantum number $^3S_1^{[1]}$. Our calculations are performed in two different frameworks. One is the widely used nonrelativistic QCD factorization, and the other is the newly proposed soft gluon factorization. In either case, we calculate at both leading order and next-to-leading order in velocity expansion. All of our final results are presented in terms of compact analytic expressions.

hep-ph↗

arXiv2017

Highly efficient quantitative phase microscopy using programmable annular LED illumination

In this work, we present a highly efficient quantitative phase imaging (QPI) approach using programmable annular LED illumination based on traditional bright-field microscope. As a new type of coded illumination, the LED array provides a flexible and compatible way to realize QPI. The proposed method modulates the transfer function of system by changing the LED illumination pattern, which achieves twice resolution of objective NA and gives noise-robust response of transfer function. The phase of a sample could be recovered from the intensity images with the inversion of transfer function. Moreover, the weak object transfer function (WOTF) of axisymmetric oblique source is derived, and the noise-free and noisy simulation results sufficiently validate the applicability of discrete annular source. The quantitative phase measurements of micro polystyrene bead and visible blazed transmission grating demonstrate the accuracy of proposed method. Finally, the experimental investigations of unstained human cancer cells using different types objective are presented, and these results show the possibility of widespread adoption of QPI in the morphology study of cellular processes and biomedical community.

physics.optics↗

arXiv2017

Bayesian Realized-GARCH Models for Financial Tail Risk Forecasting Incorporating Two-sided Weibull Distribution

The realized GARCH framework is extended to incorporate the two-sided Weibull distribution, for the purpose of volatility and tail risk forecasting in a financial time series. Further, the realized range, as a competitor for realized variance or daily returns, is employed in the realized GARCH framework. Further, sub-sampling and scaling methods are applied to both the realized range and realized variance, to help deal with inherent micro-structure noise and inefficiency. An adaptive Bayesian Markov Chain Monte Carlo method is developed and employed for estimation and forecasting, whose properties are assessed and compared with maximum likelihood, via a simulation study. Compared to a range of well-known parametric GARCH, GARCH with two-sided Weibull distribution and realized GARCH models, tail risk forecasting results across 7 market index return series and 2 individual assets clearly favor the realized GARCH models incorporating two-sided Weibull distribution, especially models employing the sub-sampled realized variance and sub-sampled realized range, over a six year period that includes the global financial crisis.

q-fin.RM↗

arXiv2017

Adaptive pixel-super-resolved lensfree holography for wide-field on-chip microscopy

High-resolution wide field-of-view (FOV) microscopic imaging plays an essential role in various fields of biomedicine, engineering, and physical sciences. As an alternative to conventional lens-based scanning techniques, lensfree holography provides a new way to effectively bypass the intrinsical trade-off between the spatial resolution and FOV of conventional microscopes. Unfortunately, due to the limited sensor pixel-size, unpredictable disturbance during image acquisition, and sub-optimum solution to the phase retrieval problem, typical lensfree microscopes only produce compromised imaging quality in terms of lateral resolution and signal-to-noise ratio (SNR). Here, we propose an adaptive pixel-super-resolved lensfree imaging (APLI) method which can solve, or at least partially alleviate these limitations. Our approach addresses the pixel aliasing problem by Z-scanning only, without resorting to subpixel shifting or beam-angle manipulation. Automatic positional error correction algorithm and adaptive relaxation strategy are introduced to enhance the robustness and SNR of reconstruction significantly. Based on APLI, we perform full-FOV reconstruction of a USAF resolution target ($\sim$29.85 $m{m^2}$) and achieve half-pitch lateral resolution of 770 $nm$, surpassing 2.17 times of the theoretical Nyquist-Shannon sampling resolution limit imposed by the sensor pixel-size (1.67 $μm$). Full-FOV imaging result of a typical dicot root is also provided to demonstrate its promising potential applications in biologic imaging.

physics.optics↗

arXiv2017

Micro Fourier Transform Profilometry ($μ$FTP): 3D shape measurement at 10,000 frames per second

Recent advances in imaging sensors and digital light projection technology have facilitated a rapid progress in 3D optical sensing, enabling 3D surfaces of complex-shaped objects to be captured with improved resolution and accuracy. However, due to the large number of projection patterns required for phase recovery and disambiguation, the maximum fame rates of current 3D shape measurement techniques are still limited to the range of hundreds of frames per second (fps). Here, we demonstrate a new 3D dynamic imaging technique, Micro Fourier Transform Profilometry ($μ$FTP), which can capture 3D surfaces of transient events at up to 10,000 fps based on our newly developed high-speed fringe projection system. Compared with existing techniques, $μ$FTP has the prominent advantage of recovering an accurate, unambiguous, and dense 3D point cloud with only two projected patterns. Furthermore, the phase information is encoded within a single high-frequency fringe image, thereby allowing motion-artifact-free reconstruction of transient events with temporal resolution of 50 microseconds. To show $μ$FTP's broad utility, we use it to reconstruct 3D videos of 4 transient scenes: vibrating cantilevers, rotating fan blades, bullet fired from a toy gun, and balloon's explosion triggered by a flying dart, which were previously difficult or even unable to be captured with conventional approaches.

physics.ins-det↗

arXiv2017

High-resolution transport-of-intensity quantitative phase microscopy with annular illumination

For quantitative phase imaging (QPI) based on transport-of-intensity equation (TIE), partially coherent illumination provides speckle-free imaging, compatibility with brightfield microscopy, and transverse resolution beyond coherent diffraction limit. Unfortunately, in a conventional microscope with circular illumination aperture, partial coherence tends to diminish the phase contrast, exacerbating the inherent noise-to-resolution tradeoff in TIE imaging, resulting in strong low-frequency artifacts and compromised imaging resolution. Here, we demonstrate how these issues can be effectively addressed by replacing the conventional circular illumination aperture with an annular one. The matched annular illumination not only strongly boosts the phase contrast for low spatial frequencies, but significantly improves the practical imaging resolution to near the incoherent diffraction limit. By incorporating high-numerical aperture (NA) illumination as well as high-NA objective, it is shown, for the first time, that TIE phase imaging can achieve a transverse resolution up to 208 nm, corresponding to an effective NA of 2.66. Time-lapse imaging of in vitro Hela cells revealing cellular morphology and subcellular dynamics during cells mitosis and apoptosis is exemplified. Given its capability for high-resolution QPI as well as the compatibility with widely available brightfield microscopy hardware, the proposed approach is expected to be adopted by the wider biology and medicine community.

physics.optics↗

arXiv2017

Enhanced LSTM for Natural Language Inference

Reasoning and inference are central to human and artificial intelligence. Modeling inference in human language is very challenging. With the availability of large annotated data (Bowman et al., 2015), it has recently become feasible to train neural network based inference models, which have shown to be very effective. In this paper, we present a new state-of-the-art result, achieving the accuracy of 88.6% on the Stanford Natural Language Inference Dataset. Unlike the previous top models that use very complicated network architectures, we first demonstrate that carefully designing sequential inference models based on chain LSTMs can outperform all previous models. Based on this, we further show that by explicitly considering recursive architectures in both local inference modeling and inference composition, we achieve additional improvement. Particularly, incorporating syntactic parsing information contributes to our best result---it further improves the performance even when added to the already very strong model.

cs.CL↗

arXiv2017

Exploring Question Understanding and Adaptation in Neural-Network-Based Question Answering

The last several years have seen intensive interest in exploring neural-network-based models for machine comprehension (MC) and question answering (QA). In this paper, we approach the problems by closely modelling questions in a neural network framework. We first introduce syntactic information to help encode questions. We then view and model different types of questions and the information shared among them as an adaptation task and proposed adaptation models for them. On the Stanford Question Answering Dataset (SQuAD), we show that these approaches can help attain better results over a competitive baseline.

cs.CL↗

arXiv2017

Adaptive Depth Imaging with Single-Photon Detectors

For active optical imaging, the use of single-photon detectors can greatly improve the detection sensitivity of the system. However, the traditional maximum-likelihood based imaging method needs a long acquisition time to capture clear three-dimensional (3D) image in low light-level. To tackle this problem, we present a novel imaging method for depth estimate, which can obtain the accurate 3D image in a short acquisition time. Our method combines the photon-count statistics with the temporal correlations of the reflected signal. According to the characteristics of the target surface, including the surface reflectivity, our method is capable of adaptively changing the dwell time in each pixel. The experimental results demonstrate that the proposed method can fast obtain the accurate depth image despite the existence of strong background noise.

physics.optics↗

arXiv2016

Enhanced superconductivity in hole-doped Nb$_{2}$PdS$_{5}$

We synthesized a series of Nb$_{2}$Pd$_{1-x}$Ru$_{x}$S$_{5}$ polycrystalline samples by a solid-state reaction method and systematically investigated the Ru-doping effect on superconductivity by transport and magnetic measurements. It is found that superconductivity is enhanced with Ru doping and is quite robust upon disorder. Hall coefficient measurements indicate that the charge transport is dominated by hole-type charge carriers similar to the case of Ir doping, suggesting multi-band superconductivity. Upon Ru or Ir doping, \emph H$_{c2}$/$\emph T_c$ exhibits a significant enhancement, exceeding the Pauli paramagnetic limit value by a factor of approximately 4. A comparison of $T_c$ and the upper critical field ($H_{c2}$) amongst the different doping elements on Pd site, reveals a significant role of spin--orbit coupling.

cond-mat.supr-con↗

arXiv2016

Distraction-Based Neural Networks for Document Summarization

Distributed representation learned with neural networks has recently shown to be effective in modeling natural languages at fine granularities such as words, phrases, and even sentences. Whether and how such an approach can be extended to help model larger spans of text, e.g., documents, is intriguing, and further investigation would still be desirable. This paper aims to enhance neural network models for such a purpose. A typical problem of document-level modeling is automatic summarization, which aims to model documents in order to generate summaries. In this paper, we propose neural models to train computers not just to pay attention to specific regions and content of input documents with attention models, but also distract them to traverse between different content of a document so as to better grasp the overall meaning for summarization. Without engineering any features, we train the models on two large datasets. The models achieve the state-of-the-art performance, and they significantly benefit from the distraction modeling, particularly when input documents are long.

cs.CL↗

arXiv2016

Giant Linear Magneto-resistance in Nonmagnetic PtBi2

We synthesized nonmagnetic PtBi$_2$ single crystals and observed a giant linear magneto-resistance (MR) up to 684\% under a magnetic field $μ_0H$ = 15 T at $T$ = 2 K. The linear MR decreases with increasing temperature, but it is still as large as 61\% under $μ_0H$ of 15 T at room temperature. Such a giant linear MR is unlikely to be described by the quantum model as the quantum condition is not satisfied. Instead, we found that the slope of MR scales with the Hall mobility, and it can be well explained by a classical disorder model.

cond-mat.mtrl-sci↗

arXiv2016

A New Ferromagnetic Superconductor: CsEuFe$_4$As$_4$

Superconductivity (SC) and ferromagnetism (FM) are in general antagonistic, which makes their coexistence very rare. Following our recent discovery of robust coexistence of SC and FM in RbEuFe$_4$As$_4$ [Y. Liu et al., arXiv: 1605.04396 (2016)], here we report another example of such a coexistence in its sister compound CsEuFe$_4$As$_4$, synthesized for the first time. The new material exhibits bulk SC at 35.2 K and Eu$^{2+}$-spin ferromagnetic ordering at 15.5 K, demonstrating that it is a new robust ferromagnetic superconductor.

cond-mat.supr-con↗