arXiv subjects

He Zhang

Publications and source records attributed to He Zhang.

At least 271 records · Page 15Linked to original sources

arXiv2020

Large-scale Ising Emulation with Four-Body Interaction and All-to-All Connection

We propose and demonstrate a nonlinear optics approach to emulate Ising machines containing up to a million spins and with tailored two and four-body interactions with all-to-all connections. It uses a spatial light modulator to encode and control the spins in the form of the binary phase values of wavelets in coherent laser beams, and emulates the high-order interaction with frequency conversion in a nonlinear crystal at the Fourier plane. By adaptive feedback control, the system can be evolved into effective spin configurations that well approximate the ground states of Ising Hamiltonians with all-to-all connected many-body interactions. Our technique could serve as a new tool to probe complex, many-body physics and give rise to exciting applications in big data optimization, computing, and analytics.

physics.optics↗

arXiv2020

ThreshKnot: Thresholded ProbKnot for Improved RNA Secondary Structure Prediction

RNA structure prediction is a challenging problem, especially with pseudoknots. Recently, there has been a shift from the classical minimum free energy-based methods (MFE) to partition function-based ones that assemble structures using base-pairing probabilities. Two examples of the latter group are the popular maximum expected accuracy (MEA) method and the ProbKnot method. ProbKnot is a fast heuristic that pairs nucleotides that are reciprocally most probable pairing partners, and unlike MEA, can also predict structures with pseudoknots. However, ProbKnot's full potential has been largely overlooked. In particular, when introduced, it did not have an MEA-like hyperparameter that can balance between positive predictive value (PPV) and sensitivity. We show that a simple thresholded version of ProbKnot, which we call ThreshKnot, leads to more accurate overall predictions by filtering out unlikely pairs whose probabilities fall under a given threshold. We also show that on three widely-used folding engines (RNAstructure, Vienna RNAfold, and CONTRAfold), ThreshKnot always outperforms the much more involved MEA algorithm in (1) its higher structure prediction accuracy, (2) its capability to predict pseudoknots, and (3) its faster runtime and easier implementation. This suggests that ThreshKnot should replace MEA as the default partition function-based structure prediction algorithm. ThreshKnot is already available in the widely used RNAstructure software package version 6.2 (released November 27, 2019): https://rna.urmc.rochester.edu/RNAstructure.html

q-bio.BM↗

arXiv2019

LinearFold: linear-time approximate RNA folding by 5'-to-3' dynamic programming and beam search

Motivation: Predicting the secondary structure of an RNA sequence is useful in many applications. Existing algorithms (based on dynamic programming) suffer from a major limitation: their runtimes scale cubically with the RNA length, and this slowness limits their use in genome-wide applications. Results: We present a novel alternative $O(n^3)$-time dynamic programming algorithm for RNA folding that is amenable to heuristics that make it run in $O(n)$ time and $O(n)$ space, while producing a high-quality approximation to the optimal solution. Inspired by incremental parsing for context-free grammars in computational linguistics, our alternative dynamic programming algorithm scans the sequence in a left-to-right (5'-to-3') direction rather than in a bottom-up fashion, which allows us to employ the effective beam pruning heuristic. Our work, though inexact, is the first RNA folding algorithm to achieve linear runtime (and linear space) without imposing constraints on the output structure. Surprisingly, our approximate search results in even higher overall accuracy on a diverse database of sequences with known structures. More interestingly, it leads to significantly more accurate predictions on the longest sequence families in that database (16S and 23S Ribosomal RNAs), as well as improved accuracies for long-range base pairs (500+ nucleotides apart), both of which are well known to be challenging for the current models. Availability: Our source code is available at https://github.com/LinearFold/LinearFold, and our webserver is at http://linearfold.org (sequence limit: 100,000nt).

q-bio.BM↗

arXiv2019

Dual affine moment invariants

Affine transformation is one of the most common transformations in nature, which is an important issue in the field of computer vision and shape analysis. And affine transformations often occur in both shape and color space simultaneously, which can be termed as Dual-Affine Transformation (DAT). In general, we should derive invariants of different data formats separately, such as 2D color images, 3D color objects, or even higher-dimensional data. To the best of our knowledge, there is no general framework to derive invariants for all of these data formats. In this paper, we propose a general framework to derive moment invariants under DAT for objects in M-dimensional space with N channels, which can be called dual-affine moment invariants (DAMI). Following this framework, we present the generating formula of DAMI under DAT for 3D color objects. Then, we instantiated a complete set of DAMI for 3D color objects with orders and degrees no greater than 4. Finally, we analyze the characteristic of these DAMI and conduct classification experiments to evaluate the stability and discriminability of them. The results prove that DAMI is robust for DAT. Our derivation framework can be applied to data in any dimension with any number of channels.

cs.CV↗

arXiv2019

Ptychographic modulation engine (PME): a low-cost DIY microscope add-on for coherent super-resolution imaging

Imaging of biological cells and tissues often relies on fluorescent labels, which offer high contrast with molecular specificity. The use of exogenous labeling agents, however, may alter the normal physiology of the bio-specimens. Complementary to the established fluorescence microscopy, label-free quantitative phase imaging provides an objective morphological measurement tool for bio-specimens and is free of variability introduced by contrast agents. Here we report a simple and low-cost microscope add-on, termed Ptychographic Modulation Engine (PME), for super-resolution quantitative phase imaging. In this microscope add-on module, we attach a diffuser to a 3D-printed holder that can be mechanically moved to different x-y positions. We then use two vibrational motors to introduce random positional shifts to the diffuser. The add-on module can be placed between the objective lens and the specimen in most existing microscope platforms. Thanks to the diffuser modulation process, the otherwise inaccessible high-resolution object information can now be encoded into the captured images. In the ptychographic phase retrieval process, we jointly recover the complex object wavefront, the complex diffuser profile, and the unknown positional shifts of the diffuser. We demonstrate a 4-fold resolution gain over the diffraction limit of the employed 2X objective lens. We also test our approach for in-vivo cell imaging, where we are able to adjust the focus after the data has been captured. The reported microscope add-on provides a turnkey solution for super-resolution quantitative phase imaging. It may find applications in label-free bio-imaging where both large field-of-view and high resolution are needed.

physics.ins-det↗

arXiv2019

Mode Selective Image Upconversion over Turbulence

We experimentally study a nonlinear optical approach to selective manipulation and detection of structured images mixed with turbulent noise. Unlike any existing adaptive-optics method by applying compensating modulation directly on the images, here we account for the turbulence indirectly, by modulating only the pump driving the nonlinear process but not the images themselves. This indirect approach eliminates any signal modulation loss or noise, while allowing more flexible and capable operations. Using specifically sum frequency generation in a lithium niobate crystal, we demonstrate selective upconversion of Laguerre-Gaussian spatial modes mixed with turbulent noise. The extinction reaches $\sim$40 dB without turbulence, and maintains $\sim$20 dB in the presence of strong turbulence. This technique could find utilities in classical and quantum communications, compressive imaging, pattern recognition, and so on.

physics.optics↗

arXiv2019

Super-resolution microscopy via ptychographic structured modulation of a diffuser

We report a new coherent imaging technique, termed ptychographic structured modulation (PSM), for quantitative super-resolution microscopy. In this technique, we place a thin diffuser (i.e., a scattering lens) in between the sample and the objective lens to modulate the complex light waves from the object. The otherwise inaccessible high-resolution object information can thus be encoded into the captured images. We then employ a ptychographic phase retrieval process to jointly recover the exit wavefront of the complex object and the unknown diffuser profile. Unlike the illumination-based super-resolution approach, the recovered image of our approach depends upon how the complex wavefront exits the sample - not enters it. Therefore, the sample thickness becomes irrelevant during reconstruction. After recovery, we can propagate the super-resolution complex wavefront to any position along the optical axis. We validate our approach using a resolution target, a quantitative phase target, a two-layer sample, and a thick PDMS sample. We demonstrate a 4.5-fold resolution gain over the diffraction limit. We also show that a 4-fold resolution gain can be achieved with as few as ~30 images. The reported approach may provide a quantitative super-resolution strategy for coherent light, X-ray, and electron imaging.

eess.IV↗

arXiv2019

Image De-raining Using a Conditional Generative Adversarial Network

Severe weather conditions such as rain and snow adversely affect the visual quality of images captured under such conditions thus rendering them useless for further usage and sharing. In addition, such degraded images drastically affect performance of vision systems. Hence, it is important to solve the problem of single image de-raining/de-snowing. However, this is a difficult problem to solve due to its inherent ill-posed nature. Existing approaches attempt to introduce prior information to convert it into a well-posed problem. In this paper, we investigate a new point of view in addressing the single image de-raining problem. Instead of focusing only on deciding what is a good prior or a good framework to achieve good quantitative and qualitative performance, we also ensure that the de-rained image itself does not degrade the performance of a given computer vision algorithm such as detection and classification. In other words, the de-rained result should be indistinguishable from its corresponding clear image to a given discriminator. This criterion can be directly incorporated into the optimization framework by using the recently introduced conditional generative adversarial networks (GANs). To minimize artifacts introduced by GANs and ensure better visual quality, a new refined loss function is introduced. Based on this, we propose a novel single image de-raining method called Image De-raining Conditional General Adversarial Network (ID-CGAN), which considers quantitative, visual and also discriminative performance into the objective function. Experiments evaluated on synthetic images and real images show that the proposed method outperforms many recent state-of-the-art single image de-raining methods in terms of quantitative and visual performance.

cs.CV↗

arXiv2019

A microscale nanosecond time-resolved platinum thermometer probing Gaussian pulsed laser induced temperature

Pulsed laser processing is playing a crucial role in additive manufacturing and nanomaterial processing. However, probing transient temperature during laser interaction with the processed materials is challenging with both high spatial and temporal resolution. Here, we demonstrate 9ns rise time 50μm sized Pt thin film sensor for probing the temperature field induced by a nanosecond pulsed laser on a semiconductor thin film. The error sources and associated improvements are discussed regarding the fabrication, sensor pattern and electrical circuits. We further carried out the first experimental and theoretical analysis of spatial resolution and accuracy for measuring gaussian pulse on the serpentine structure. Transparent silica and sapphire substrates, as well as 7-45nm insulation layer thickness, are compared for sensing accuracy and temporal resolution. Lastly, the measured absolute temperature magnitude is validated through the laser-induced melting of the 40nm thick amorphous silicon film. Preliminary study shows its potential application for probing heat conduction among ultrathin films.

physics.ins-det↗

arXiv2019

Rapid and robust whole slide imaging based on LED-array illumination and color-multiplexed single-shot autofocusing

Background: The use of whole slide imaging (WSI) for digital pathology has recently been cleared for primary diagnosis in the US. A conventional WSI system scans the tissue slide to different positions and acquires the digital images. In a typical implementation, a focus map is created prior to the scanning process, leading to significant overhead time and a necessity for high positional accuracy of the mechanical system. The resulting cost of WSI system is often prohibitive for frozen section procedure during surgery. Methods: We report a novel WSI scheme based on a programmable LED array for sample illumination. In between two regular brightfield image acquisitions, we acquire one additional image by turning on a red and a green LED for color multiplexed illumination. We then identify the translational shift of the red- and green-channel images by maximizing the image mutual information or cross-correlation. The resulting translational shift is used for dynamic focus correction in the scanning process. Since we track the differential focus during adjacent acquisitions, there is no positional repeatability requirement in our scheme. Results: We demonstrate a prototype WSI platform with a mean focusing error of ~0.3 microns. Different from previous implementations, this prototype platform requires no focus map surveying, no secondary camera or additional optics, and allows for continuous sample motion in the focus tracking process. Conclusions: A programmable LED array can be used for color-multiplexed single-shot autofocusing in WSI. The reported scheme may enable the development of cost-effective WSI platforms without positional repeatability requirement. It may also provide a turnkey solution for other high-content microscopy applications.

eess.IV↗

arXiv2019

Full-field Fourier ptychography (FFP): spatially varying pupil modeling and its application for rapid field-dependent aberration metrology

Digital aberration measurement and removal play a prominent role in computational imaging platforms aimed at achieving simple and compact optical arrangements. A recent important class of such platforms is Fourier ptychography, which is geared towards efficiently creating gigapixel images with high resolution and large field of view (FOV). In current FP implementations, pupil aberration is often recovered at each small segment of the entire FOV. This reconstruction strategy fails to consider the field-dependent nature of the optical pupil. Given the power series expansion of the wavefront aberration, the spatially varying pupil can be fully characterized by tens of coefficients over the entire FOV. With this observation, we report a Full-field Fourier Ptychography (FFP) scheme for rapid and robust aberration metrology. The meaning of 'full-field' in FFP is referred to the recovering of the 'full-field' coefficients that govern the field-dependent pupil over the entire FOV. The optimization degrees of freedom are at least two orders of magnitude lower than the previous implementations. We show that the image acquisition process of FFP can be completed in ~1s and the spatially varying aberration of the entire FOV can be recovered in ~35s using a CPU. The reported approach may facilitate the further development of Fourier ptychography. Since no moving part or calibration target is needed in this approach, it may find important applications in aberration metrology. The derivation of the full-field coefficients and its extension for Zernike modes also provide a general tool for analyzing spatially varying aberrations in computational imaging systems.

physics.optics↗

arXiv2019

Joint Transmission Map Estimation and Dehazing using Deep Networks

Single image haze removal is an extremely challenging problem due to its inherent ill-posed nature. Several prior-based and learning-based methods have been proposed in the literature to solve this problem and they have achieved superior results. However, most of the existing methods assume constant atmospheric light model and tend to follow a two-step procedure involving prior-based methods for estimating transmission map followed by calculation of dehazed image using the closed form solution. In this paper, we relax the constant atmospheric light assumption and propose a novel unified single image dehazing network that jointly estimates the transmission map and performs dehazing. In other words, our new approach provides an end-to-end learning framework, where the inherent transmission map and dehazed result are learned directly from the loss function. Extensive experiments on synthetic and real datasets with challenging hazy images demonstrate that the proposed method achieves significant improvements over the state-of-the-art methods.

cs.CV↗

arXiv2019

Field-portable quantitative lensless microscopy based on translated speckle illumination and sub-sampled ptychographic phase retrieval

We report a compact, cost-effective and field-portable lensless imaging platform for quantitative microscopy. In this platform, the object is placed on top of an image sensor chip without using any lens. We use a low-cost galvo scanner to rapidly scan an unknown laser speckle pattern on the object. To address the positioning repeatability and accuracy issues, we directly recover the positional shifts of the speckle pattern based on the phase correlation of the captured images. To bypass the resolution limit set by the imager pixel size, we employ a sub-sampled ptychographic phase retrieval process to recover the complex object. We validate our approach using a resolution target, a phase target, and a biological sample. Our results show that accurate, high-quality complex images can be obtained from a lensless dataset with as few as ~10 images. We also demonstrate the reported approach to achieve a 6.4 mm by 4.6 mm field of view and a half pitch resolution of 1 miron. The reported approach may provide a quantitative lensless imaging strategy for addressing point-of-care, global-health, and telemedicine related challenges.

eess.IV↗

arXiv2019

Convolutional Sparse Coding for Compressed Sensing CT Reconstruction

Over the past few years, dictionary learning (DL)-based methods have been successfully used in various image reconstruction problems. However, traditional DL-based computed tomography (CT) reconstruction methods are patch-based and ignore the consistency of pixels in overlapped patches. In addition, the features learned by these methods always contain shifted versions of the same features. In recent years, convolutional sparse coding (CSC) has been developed to address these problems. In this paper, inspired by several successful applications of CSC in the field of signal processing, we explore the potential of CSC in sparse-view CT reconstruction. By directly working on the whole image, without the necessity of dividing the image into overlapped patches in DL-based methods, the proposed methods can maintain more details and avoid artifacts caused by patch aggregation. With predetermined filters, an alternating scheme is developed to optimize the objective function. Extensive experiments with simulated and real CT data were performed to validate the effectiveness of the proposed methods. Qualitative and quantitative results demonstrate that the proposed methods achieve better performance than several existing state-of-the-art methods.

physics.med-ph↗

arXiv2019

A Parameter Estimation Method Using Linear Response Statistics: Numerical Scheme

This paper presents a numerical method to implement the parameter estimation method using response statistics that was recently formulated by the authors. The proposed approach formulates the parameter estimation problem of Itô drift diffusions as a nonlinear least-squares problem. To avoid solving the model repeatedly when using an iterative scheme in solving the resulting least-squares problems, a polynomial surrogate model is employed on appropriate response statistics with smooth dependence on the parameters. The existence of minimizers of the approximate polynomial least-squares problems that converge to the solution of the true least square problem is established under appropriate regularity assumption of the essential statistics as functions of parameters. Numerical implementation of the proposed method is conducted on two prototypical examples that belong to classes of models with wide range of applications, including the Langevin dynamics and the stochastically forced gradient flows. Several important practical issues, such as the selection of the appropriate response operator to ensure the identifiability of the parameters and the reduction of the parameter space, are discussed. From the numerical experiments, it is found that the proposed approach is superior compared to the conventional approach that uses equilibrium statistics to determine the parameters.

math.NA↗

arXiv2019

Near-field Fourier ptychography: super-resolution phase retrieval via speckle illumination

Achieving high spatial resolution is the goal of many imaging systems. Designing a high-resolution lens with diffraction-limited performance over a large field of view remains a difficult task in imaging system design. On the other hand, creating a complex speckle pattern with wavelength-limited spatial features is effortless and can be implemented via a simple random diffuser. With this observation and inspired by the concept of near-field ptychography, we report a new imaging modality, termed near-field Fourier ptychography, for tackling high-resolution imaging challenges in both microscopic and macroscopic imaging settings. The meaning of 'near-field' is referred to placing the object at a short defocus distance with a large Fresnel number. In our implementations, we project a speckle pattern with fine spatial features on the object instead of directly resolving the spatial features via a high-resolution lens. We then translate the object (or speckle) to different positions and acquire the corresponding images using a low-resolution lens. A ptychographic phase retrieval process is used to recover the complex object, the unknown speckle pattern, and the coherent transfer function at the same time. In a microscopic imaging setup, we use a 0.12 numerical aperture (NA) lens to achieve a NA of 0.85 in the reconstruction process. In a macroscale photographic imaging setup, we achieve ~7-fold resolution gain using a photographic lens. The final achievable resolution is not determined by the collection optics. Instead, it is determined by the feature size of the speckle pattern. The reported imaging modality can be employed in light, coherent X-ray, and transmission electron imaging systems to increase resolution and provide quantitative absorption and phase contrast of the object.

physics.optics↗

arXiv2019

Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework

Intrusion detection systems (IDSs) play an important role in identifying malicious attacks and threats in networking systems. As fundamental tools of IDSs, learning based classification methods have been widely employed. When it comes to detecting network intrusions in small sample sizes (e.g., emerging intrusions), the limited number and imbalanced proportion of training samples usually cause significant challenges in training supervised and semi-supervised classifiers. In this paper, we propose a general network intrusion detection framework to address the challenges of both \emph{data scarcity} and \emph{data imbalance}. The novelty of the proposed framework focuses on incorporating deep adversarial learning with statistical learning and exploiting learning based data augmentation. Given a small set of network intrusion samples, it first derives a Poisson-Gamma joint probabilistic generative model to generate synthesised intrusion data using Monte Carlo methods. Those synthesised data are then augmented by deep generative neural networks through adversarial learning. Finally, it adopts the augmented intrusion data to train supervised models for detecting network intrusions. Comprehensive experimental validations on KDD Cup 99 dataset show that the proposed framework outperforms the existing learning based IDSs in terms of improved accuracy, precision, recall, and F1-score.

cs.CR↗

arXiv2019

Polarimetric Thermal to Visible Face Verification via Attribute Preserved Synthesis

Thermal to visible face verification is a challenging problem due to the large domain discrepancy between the modalities. Existing approaches either attempt to synthesize visible faces from thermal faces or extract robust features from these modalities for cross-modal matching. In this paper, we take a different approach in which we make use of the attributes extracted from the visible image to synthesize the attribute-preserved visible image from the input thermal image for cross-modal matching. A pre-trained VGG-Face network is used to extract the attributes from the visible image. Then, a novel Attribute Preserved Generative Adversarial Network (AP-GAN) is proposed to synthesize the visible image from the thermal image guided by the extracted attributes. Finally, a deep network is used to extract features from the synthesized image and the input visible image for verification. Extensive experiments on the ARL Polarimetric face dataset show that the proposed method achieves significant improvements over the state-of-the-art methods.

cs.CV↗