arXiv subjects

He Li

Publications and source records attributed to He Li.

At least 127 records · Page 7Linked to original sources

arXiv2021

AOSLO-net: A deep learning-based method for automatic segmentation of retinal microaneurysms from adaptive optics scanning laser ophthalmoscope images

Microaneurysms (MAs) are one of the earliest signs of diabetic retinopathy (DR), a frequent complication of diabetes that can lead to visual impairment and blindness. Adaptive optics scanning laser ophthalmoscopy (AOSLO) provides real-time retinal images with resolution down to 2 $μm$ and thus allows detection of the morphologies of individual MAs, a potential marker that might dictate MA pathology and affect the progression of DR. In contrast to the numerous automatic models developed for assessing the number of MAs on fundus photographs, currently there is no high throughput image protocol available for automatic analysis of AOSLO photographs. To address this urgency, we introduce AOSLO-net, a deep neural network framework with customized training policies to automatically segment MAs from AOSLO images. We evaluate the performance of AOSLO-net using 87 DR AOSLO images and our results demonstrate that the proposed model outperforms the state-of-the-art segmentation model both in accuracy and cost and enables correct MA morphological classification.

eess.IV↗

arXiv2021

Model Hierarchy Predictive Control of Robotic Systems

This letter presents a new predictive control architecture for high-dimensional robotic systems. As opposed to a conventional Model Predictive Control (MPC) approach to locomotion that formulates a hierarchical sequence of optimization problems, the proposed work formulates a single optimization problem posed over a hierarchy of models, and is thus named Model Hierarchy Predictive Control (MHPC). MHPC is formulated as a multi-phase receding-horizon Trajectory Optimization (TO) problem, and can be implemented using any general multi-phase TO solver. MHPC is benchmarked in simulation on a quadruped, a biped, and a quadrotor, demonstrating control performance on par or exceeding whole-body MPC while maintaining a lower computational cost in each case. A preliminary gap jumping experiment is conducted on the MIT Mini Cheetah with the control policy generated offline, demonstrating the physical validity of the generated trajectories and motivating online MHPC in future work.

cs.RO↗

arXiv2021

Distributed Estimation for Principal Component Analysis: an Enlarged Eigenspace Analysis

The growing size of modern data sets brings many challenges to the existing statistical estimation approaches, which calls for new distributed methodologies. This paper studies distributed estimation for a fundamental statistical machine learning problem, principal component analysis (PCA). Despite the massive literature on top eigenvector estimation, much less is presented for the top-$L$-dim ($L>1$) eigenspace estimation, especially in a distributed manner. We propose a novel multi-round algorithm for constructing top-$L$-dim eigenspace for distributed data. Our algorithm takes advantage of shift-and-invert preconditioning and convex optimization. Our estimator is communication-efficient and achieves a fast convergence rate. In contrast to the existing divide-and-conquer algorithm, our approach has no restriction on the number of machines. Theoretically, the traditional Davis-Kahan theorem requires the explicit eigengap assumption to estimate the top-$L$-dim eigenspace. To abandon this eigengap assumption, we consider a new route in our analysis: instead of exactly identifying the top-$L$-dim eigenspace, we show that our estimator is able to cover the targeted top-$L$-dim population eigenspace. Our distributed algorithm can be applied to a wide range of statistical problems based on PCA, such as principal component regression and single index model. Finally, We provide simulation studies to demonstrate the performance of the proposed distributed estimator.

cs.DC↗

arXiv2020

Data-driven quantitative modeling of bacterial active nematics

Active matter comprises individual units that convert energy into mechanical motion. In many examples, such as bacterial systems and biofilament assays, constituent units are elongated and can give rise to local nematic orientational order. Such `active nematics' systems have attracted much attention from both theorists and experimentalists. However, despite intense research efforts, data-driven quantitative modeling has not been achieved, a situation mainly due to the lack of systematic experimental data and to the large number of parameters of current models. Here we introduce a new active nematics system made of swarming filamentous bacteria. We simultaneously measure orientation and velocity fields and show that the complex spatiotemporal dynamics of our system can be quantitatively reproduced by a new type of microscopic model for active suspensions whose important parameters are all estimated from comprehensive experimental data. This provides unprecedented access to key effective parameters and mechanisms governing active nematics. Our approach is applicable to different types of dense suspensions and shows a path towards more quantitative active matter research.

cond-mat.soft↗

arXiv2020

Optical Push Broom On a Chip

Here we report the first experimental demonstration of light trapping by a refractive index front in a silicon waveguide, the optical push broom effect. The front generated by a fast pump pulse collects and traps the energy of a CW signal with smaller group velocity and tuned near to the band gap of the Bragg grating introduced in the waveguide. This situation represents an optical analogue of light trapping in a tapered plasmonic waveguide where light is stopped without reflection. The energy of the CW signal is accumulated inside the front and distributed in frequency. In this experiment a 2 ps free carrier front was generated via two photon absorption of the pump in silicon waveguide. It collects approximately a 30 ps long packet of the CW signal. The presented effect can be utilized to compress signals in time and space.

physics.optics↗

arXiv2020

Cumulative Message Authentication Codes for Resource-Constrained IoT Networks

In resource-constrained IoT networks, the use of conventional message authentication codes (MACs) to provide message authentication and integrity is not possible due to the large size of the MAC output. A straightforward yet naive solution to this problem is to employ a truncated MAC which undesirably sacrifices cryptographic strength in exchange for reduced communication overhead. In this paper, we address this problem by proposing a novel approach for message authentication called \textit{Cumulative Message Authentication Code} (CuMAC), which consists of two distinctive procedures: \textit{aggregation} and \textit{accumulation}. In aggregation, a sender generates compact authentication tags from segments of multiple MACs by using a systematic encoding procedure. In accumulation, a receiver accumulates the cryptographic strength of the underlying MAC by collecting and verifying the authentication tags. Embodied with these two procedures, CuMAC enables the receiver to achieve an advantageous trade-off between the cryptographic strength and the latency in processing of the authentication tags. Furthermore, for some latency-sensitive messages where this trade-off may be unacceptable, we propose a variant of CuMAC that we refer to as \textit{CuMAC with Speculation} (CuMAC/S). In addition to the aggregation and accumulation procedures, CuMAC/S enables the sender and receiver to employ a speculation procedure for predicting future message values and pre-computing the corresponding MAC segments. For the messages which can be reliably speculated, CuMAC/S significantly reduces the MAC verification latency without compromising the cryptographic strength. We have carried out comprehensive evaluation of CuMAC and CuMAC/S through simulation and a prototype implementation on a real car.

cs.CR↗

arXiv2020

Hybrid Systems Differential Dynamic Programming for Whole-Body Motion Planning of Legged Robots

This paper presents a Differential Dynamic Programming (DDP) framework for trajectory optimization (TO) of hybrid systems with state-based switching. The proposed Hybrid Systems DDP (HS-DDP) approach is considered for application to whole-body motion planning with legged robots. Specifically, HS-DDP incorporates three algorithmic advances: an impact-aware DDP step addressing the impact event in legged locomotion, an Augmented Lagrangian (AL) method dealing with the switching constraint, and a Switching Time Optimization (STO) algorithm that optimizes switching times by leveraging the structure of DDP. Further, a Relaxed Barrier (ReB) method is used to manage inequality constraints and is integrated into HS-DDP for locomotion planning. The performance of the developed algorithms is benchmarked on a simulation model of the MIT Mini Cheetah executing a bounding gait. We demonstrate the effectiveness of AL and ReB for handling switching constraints, friction constraints, and torque limits. By comparing to previous solutions, we show that the STO algorithm achieves 2.3 times more reduction of total switching times, demonstrating the efficiency of our method.

cs.RO↗

arXiv2020

The g-extra connectivity of the Mycielskian

The $g$-extra connectivity is an important parameter to measure the ability of tolerance and reliability of interconnection networks. Given a connected graph $G=(V,E)$ and a non-negative integer $g$, a subset $S\subseteq V$ is called a $g$-extra cut of $G$ if $G-S$ is disconnected and every component of $G-S$ has at least $g+1$ vertices. The cardinality of the minimum $g$-extra cut is defined as the $g$-extra connectivity of $G$, denoted by $κ_g(G)$. In a search for triangle-free graphs with arbitrarily large chromatic numbers, Mycielski developed a graph transformation that transforms a graph $G$ into a new graph $μ(G)$, which is called the Mycielskian of $G$. This paper investigates the relationship of the g-extra connectivity of the Mycielskian $μ(G)$ and the graph $G$, moreover, show that $κ_{2g+1}(μ(G))=2κ_{g}(G)+1$ for $g\geq 1$ and $κ_{g}(G)\leq min\{g+1, \lfloor\frac{n}{2}\rfloor\}$.

math.CO↗

arXiv2020

Digit Stability Inference for Iterative Methods Using Redundant Number Representation

In our recent work on iterative computation in hardware, we showed that arbitrary-precision solvers can perform more favorably than their traditional arithmetic equivalents when the latter's precisions are either under- or over-budgeted for the solution of the problem at hand. Significant proportions of these performance improvements stem from the ability to infer the existence of identical most-significant digits between iterations. This technique uses properties of algorithms operating on redundantly represented numbers to allow the generation of those digits to be skipped, increasing efficiency. It is unable, however, to guarantee that digits will stabilize, i.e., never change in any future iteration. In this article, we address this shortcoming, using interval and forward error analyses to prove that digits of high significance will become stable when computing the approximants of systems of linear equations using stationary iterative methods. We formalize the relationship between matrix conditioning and the rate of growth in most-significant digit stability, using this information to converge to our desired results more quickly. Versus our previous work, an exemplary hardware realization of this new technique achieves an up-to 2.2x speedup in the solution of a set of variously conditioned systems using the Jacobi method.

math.NA↗

arXiv2020

First-Principles Exploration of Defect-Pairs in GaN

Using first-principles calculations, we explored all the 21 defect-pairs in GaN and considered 6 configurations with different defect-defect distances for each defect-pair. 15 defect-pairs with short defect-defect distances are found to be stable during structural relaxation, so they can exist in the GaN lattice once formed during the irradiation of high-energy particles. 9 defect-pairs have formation energies lower than 10 eV in the neutral state. The vacancy-pair VN-VN is found to have very low formation energies, as low as 0 eV in p-type and Ga-rich GaN, and act as efficient donors producing two deep donor levels, which can limit the p-type doping and minority carrier lifetime in GaN. VN-VN has been overlooked in the previous study of defects in GaN. Most of these defect-pairs act as donors and produce a large number of defect levels in the band gap. Their formation energies and concentrations are sensitive to the chemical potentials of Ga and N, so their influences on the electrical and optical properties of Ga-rich and N-rich GaN after irradiation should differ significantly. These results about the defect-pairs provide fundamental data for understanding the radiation damage mechanism in GaN and simulating the defect formation and diffusion behavior under irradiation.

cond-mat.mtrl-sci↗

arXiv2019

ATMPA: Attacking Machine Learning-based Malware Visualization Detection Methods via Adversarial Examples

Since the threat of malicious software (malware) has become increasingly serious, automatic malware detection techniques have received increasing attention, where machine learning (ML)-based visualization detection methods become more and more popular. In this paper, we demonstrate that the state-of-the-art ML-based visualization detection methods are vulnerable to Adversarial Example (AE) attacks. We develop a novel Adversarial Texture Malware Perturbation Attack (ATMPA) method based on the gradient descent and L-norm optimization method, where attackers can introduce some tiny perturbations on the transformed dataset such that ML-based malware detection methods will completely fail. The experimental results on the MS BIG malware dataset show that a small interference can reduce the accuracy rate down to 0% for several ML-based detection methods, and the rate of transferability is 74.1% on average.

cs.CR↗

arXiv2019

ARCHITECT: Arbitrary-precision Hardware with Digit Elision for Efficient Iterative Compute

Many algorithms feature an iterative loop that converges to the result of interest. The numerical operations in such algorithms are generally implemented using finite-precision arithmetic, either fixed- or floating-point, most of which operate least-significant digit first. This results in a fundamental problem: if, after some time, the result has not converged, is this because we have not run the algorithm for enough iterations or because the arithmetic in some iterations was insufficiently precise? There is no easy way to answer this question, so users will often over-budget precision in the hope that the answer will always be to run for a few more iterations. We propose a fundamentally new approach: with the appropriate arithmetic able to generate results from most-significant digit first, we show that fixed compute-area hardware can be used to calculate an arbitrary number of algorithmic iterations to arbitrary precision, with both precision and approximant index increasing in lockstep. Consequently, datapaths constructed following our principles demonstrate efficiency over their traditional arithmetic equivalents where the latter's precisions are either under- or over-budgeted for the computation of a result to a particular accuracy. Use of most-significant digit-first arithmetic additionally allows us to declare certain digits to be stable at runtime, avoiding their recalculation in subsequent iterations and thereby increasing performance and decreasing memory footprints. Versus arbitrary-precision iterative solvers without the optimisations we detail herein, we achieve up-to 16$\times$ performance speedups and 1.9x memory savings for the evaluated benchmarks.

cs.AR↗

arXiv2019

Quantifying Intrinsic Uncertainty in Classification via Deep Dirichlet Mixture Networks

With the widespread success of deep neural networks in science and technology, it is becoming increasingly important to quantify the uncertainty of the predictions produced by deep learning. In this paper, we introduce a new method that attaches an explicit uncertainty statement to the probabilities of classification using deep neural networks. Precisely, we view that the classification probabilities are sampled from an unknown distribution, and we propose to learn this distribution through the Dirichlet mixture that is flexible enough for approximating any continuous distribution on the simplex. We then construct credible intervals from the learned distribution to assess the uncertainty of the classification probabilities. Our approach is easy to implement, computationally efficient, and can be coupled with any deep neural network architecture. Our method leverages the crucial observation that, in many classification applications such as medical diagnosis, more than one class labels are available for each observational unit. We demonstrate the usefulness of our approach through simulations and a real data example.

cs.LG↗

arXiv2018

Robust Video Content Alignment and Compensation for Clear Vision Through the Rain

Outdoor vision-based systems suffer from atmospheric turbulences, and rain is one of the worst factors for vision degradation. Current rain removal methods show limitations either for complex dynamic scenes, or under torrential rain with opaque occlusions. We propose a novel derain framework which applies superpixel (SP) segmentation to decompose the scene into depth consistent units. Alignment of scene contents are done at the SP level, which proves to be robust against rain occlusion interferences and fast camera motion. Two alignment output tensors, i.e., optimal temporal match tensor and sorted spatial-temporal match tensor, provide informative clues for the location of rain streaks and the occluded background contents. Different classical and novel methods such as Robust Principle Component Analysis and Convolutional Neural Networks are applied and compared for their respective advantages in efficiently exploiting the rich spatial-temporal features provided by the two tensors. Extensive evaluations show that advantage of up to 5dB is achieved on the scene restoration PSNR over state-of-the-art methods, and the advantage is especially obvious with highly complex and dynamic scenes. Visual evaluations show that the proposed framework is not only able to suppress heavy and opaque occluding rain streaks, but also large semi-transparent regional fluctuations and distortions.

cs.CV↗

arXiv2018

Robust Video Content Alignment and Compensation for Rain Removal in a CNN Framework

Rain removal is important for improving the robustness of outdoor vision based systems. Current rain removal methods show limitations either for complex dynamic scenes shot from fast moving cameras, or under torrential rain fall with opaque occlusions. We propose a novel derain algorithm, which applies superpixel (SP) segmentation to decompose the scene into depth consistent units. Alignment of scene contents are done at the SP level, which proves to be robust towards rain occlusion and fast camera motion. Two alignment output tensors, i.e., optimal temporal match tensor and sorted spatial-temporal match tensor, provide informative clues for rain streak location and occluded background contents to generate an intermediate derain output. These tensors will be subsequently prepared as input features for a convolutional neural network to restore high frequency details to the intermediate output for compensation of mis-alignment blur. Extensive evaluations show that up to 5 dB reconstruction PSNR advantage is achieved over state-of-the-art methods. Visual inspection shows that much cleaner rain removal is achieved especially for highly dynamic scenes with heavy and opaque rainfall from a fast moving camera.

cs.CV↗

arXiv2018

SDPMN: Privacy Preserving MapReduce Network Using SDN

MapReduce is a popular programming model and an associated implementation for parallel processing big data in the distributed environment. Since large scaled MapReduce data centers usually provide services to many users, it is an essential problem to preserve the privacy between different applications in the same network. In this paper, we propose SDPMN, a framework that using \textit{software defined network} (SDN) to distinguish the network between each application, which is a manageable and scalable method. We design this framework based on the existing SDN structure and Hadoop networks. Since the rule space of each SDN device is limited, we also propose the rule placement optimization for this framework to maximize the hardware supported isolated application networks. We state this problem in a general MapReduce network and design a heuristic algorithm to find the solution. From the simulation based evaluation, with our algorithm, the given network can support more privacy preserving application networks with SDN switches.

cs.DC↗

arXiv2018

FDRC: Flow-Driven Rule Caching Optimization in Software Defined Networking

With the sharp growth of cloud services and their possible combinations, the scale of data center network traffic has an inevitable explosive increasing in recent years. Software defined network (SDN) provides a scalable and flexible structure to simplify network traffic management. It has been shown that Ternary Content Addressable Memory (TCAM) management plays an important role on the performance of SDN. However, previous literatures, in point of view on rule placement strategies, are still insufficient to provide high scalability for processing large flow sets with a limited TCAM size. So caching is a brand new method for TCAM management which can provide better performance than rule placement. In this paper, we propose FDRC, an efficient flow-driven rule caching algorithm to optimize the cache replacement in SDN-based networks. Different from the previous packet-driven caching algorithm, FDRC is characterized by trying to deal with the challenges of limited cache size constraint and unpredictable flows. In particular, we design a caching algorithm with low-complexity to achieve high cache hit ratio by prefetching and special replacement strategy for predictable and unpredictable flows, respectively. By conducting extensive simulations, we demonstrate that our proposed caching algorithm significantly outperforms FIFO and least recently used (LRU) algorithms under various network settings.

cs.DC↗

arXiv2017

Modeling Biological Membrane and Red Blood Cells by Coarse- Grained Particle Method

In this work, we review previously developed coarse-grained (CG) particle models for biological membrane and red blood cells (RBCs) and discuss the advantages of the CG particle method over the continuum and atomic simulations on modeling biological phenomena. CG particle models can largely increase the length scale and time scale of atomic simulations by eliminating fast degrees of freedom while preserving the mesoscopic structures and properties of the simulated system. One the other hand, CG particle models can be used to capture microstructural alternations in diseased RBCs and simulate topological changes of biological membrane and RBCs, which are major challenges to typical continuum representations of membrane and RBCs. The power and versatility of the CG particle methods are demonstrated through simulating the dynamical processes involving significant topological changes, such as lipid self-assembly, vesicle fusion and membrane budding.

cond-mat.soft↗