arXiv subjects

Chao Chen

Publications and source records attributed to Chao Chen.

At least 811 records · Page 45Linked to original sources

arXiv2021

A Frequency-Domain Approach to Nonlinear Negative Imaginary Systems Analysis

In this study, we extend the theory of negative imaginary (NI) systems to a nonlinear framework using a frequency-domain approach. The extended notion is completely characterized via a finite-frequency integration over a "kernel function" on energy-bounded input and output signal pairs. The notion is closely related to and carefully contrasted with the well-studied extension of negative imaginariness -- the theory of counterclockwise dynamics. A condition for feedback stability of the proposed nonlinear NI systems is then developed based on the technique of integral quadratic constraints. Examples and simulations on feedback interconnections of typical nonlinear systems are provided to demonstrate the effectiveness.

eess.SY↗

arXiv2021

Self-learn to Explain Siamese Networks Robustly

Learning to compare two objects are essential in applications, such as digital forensics, face recognition, and brain network analysis, especially when labeled data is scarce and imbalanced. As these applications make high-stake decisions and involve societal values like fairness and transparency, it is critical to explain the learned models. We aim to study post-hoc explanations of Siamese networks (SN) widely used in learning to compare. We characterize the instability of gradient-based explanations due to the additional compared object in SN, in contrast to architectures with a single input instance. We propose an optimization framework that derives global invariance from unlabeled data using self-learning to promote the stability of local explanations tailored for specific query-reference pairs. The optimization problems can be solved using gradient descent-ascent (GDA) for constrained optimization, or SGD for KL-divergence regularized unconstrained optimization, with convergence proofs, especially when the objective functions are nonconvex due to the Siamese architecture. Quantitative results and case studies on tabular and graph data from neuroscience and chemical engineering show that the framework respects the self-learned invariance while robustly optimizing the faithfulness and simplicity of the explanation. We further demonstrate the convergence of GDA experimentally.

cs.LG↗

arXiv2021

RCHOL: Randomized Cholesky Factorization for Solving SDD Linear Systems

We introduce a randomized algorithm, namely RCHOL, to construct an approximate Cholesky factorization for a given Laplacian matrix (a.k.a., graph Laplacian). From a graph perspective, the exact Cholesky factorization introduces a clique in the underlying graph after eliminating a row/column. By randomization, RCHOL only retains a sparse subset of the edges in the clique using a random sampling developed by Spielman and Kyng. We prove RCHOL is breakdown-free and apply it to solving large sparse linear systems with symmetric diagonally dominant matrices. In addition, we parallelize RCHOL based on the nested dissection ordering for shared-memory machines. We report numerical experiments that demonstrate the robustness and the scalability of RCHOL. For example, our parallel code scaled up to 64 threads on a single node for solving the 3D Poisson equation, discretized with the 7-point stencil on a $1024\times 1024 \times 1024$ grid, a problem that has one billion unknowns.

math.NA↗

arXiv2021

Minimizing Labeling Effort for Tree Skeleton Segmentation using an Automated Iterative Training Methodology

Training of convolutional neural networks for semantic segmentation requires accurate pixel-wise labeling which requires large amounts of human effort. The human-in-the-loop method reduces labeling effort; however, it requires human intervention for each image. This paper describes a general iterative training methodology for semantic segmentation, Automating-the-Loop. This aims to replicate the manual adjustments of the human-in-the-loop method with an automated process, hence, drastically reducing labeling effort. Using the application of detecting partially occluded apple tree segmentation, we compare manually labeled annotations, self-training, human-in-the-loop, and Automating-the-Loop methods in both the quality of the trained convolutional neural networks, and the effort needed to create them. The convolutional neural network (U-Net) performance is analyzed using traditional metrics and a new metric, Complete Grid Scan, which promotes connectivity and low noise. It is shown that in our application, the new Automating-the-Loop method greatly reduces the labeling effort while producing comparable performance to both human-in-the-loop and complete manual labeling methods.

cs.CV↗

arXiv2021

L-functions of certain exponential sums over finite fields II

In this paper, we compute the $q$-adic slopes of the L-functions of an important class of exponential sums arising from analytic number theory. Our main tools include Adolphson-Sperber's work on toric exponential sums and Wan's decomposition theorems.

math.NT↗

arXiv2021

Effective GPU Sharing Under Compiler Guidance

Modern computing platforms tend to deploy multiple GPUs (2, 4, or more) on a single node to boost system performance, with each GPU having a large capacity of global memory and streaming multiprocessors (SMs). GPUs are an expensive resource, and boosting utilization of GPUs without causing performance degradation of individual workloads is an important and challenging problem. Although services like MPS support simultaneous execution of multiple co-operative kernels on a single device, they do not solve the above problem for uncooperative kernels, MPS being oblivious to the resource needs of each kernel. We propose a fully automated compiler-assisted scheduling framework. The compiler constructs GPU tasks by identifying kernel launches and their related GPU operations (e.g. memory allocations). For each GPU task, a probe is instrumented in the host-side code right before its launch point. At runtime, the probe conveys the information about the task's resource requirements (e.g. memory and compute cores) to a scheduler, such that the scheduler can place the task on an appropriate device based on the task's resource requirements and devices' load in a memory-safe, resource-aware manner. To demonstrate its advantages, we prototyped a throughput-oriented scheduler based on the framework, and evaluated it with the Rodinia benchmark suite and the Darknet neural network framework on NVIDIA GPUs. The results show that the proposed solution outperforms existing state-of-the-art solutions by leveraging its knowledge about applications' multiple resource requirements, which include memory as well as SMs. It improves throughput by up to 2.5x for Rodinia benchmarks, and up to 2.7x for Darknet neural networks. In addition, it improves job turnaround time by up to 4.9x, and limits individual kernel performance degradation to at most 2.5%.

cs.DC↗

arXiv2021

L-functions of Certain Exponential Sums over Finite Fields

In this paper, we completely determine the slopes and weights of the L-functions of an important class of exponential sums arising from analytic number theory. Our main tools include Adolphson-Sperber's work on toric exponential sums and Wan's decomposition theorems. One consequence of our main result is a sharp estimate of these exponential sums. Another consequence is to obtain an explicit counterexample of Adolphson-Sperber's conjecture on weights of toric exponential sums.

math.NT↗

arXiv2021

The Audio Auditor: User-Level Membership Inference in Internet of Things Voice Services

With the rapid development of deep learning techniques, the popularity of voice services implemented on various Internet of Things (IoT) devices is ever increasing. In this paper, we examine user-level membership inference in the problem space of voice services, by designing an audio auditor to verify whether a specific user had unwillingly contributed audio used to train an automatic speech recognition (ASR) model under strict black-box access. With user representation of the input audio data and their corresponding translated text, our trained auditor is effective in user-level audit. We also observe that the auditor trained on specific data can be generalized well regardless of the ASR model architecture. We validate the auditor on ASR models trained with LSTM, RNNs, and GRU algorithms on two state-of-the-art pipelines, the hybrid ASR system and the end-to-end ASR system. Finally, we conduct a real-world trial of our auditor on iPhone Siri, achieving an overall accuracy exceeding 80\%. We hope the methodology developed in this paper and findings can inform privacy advocates to overhaul IoT privacy.

cs.CR↗

arXiv2021

Link Prediction with Persistent Homology: An Interactive View

Link prediction is an important learning task for graph-structured data. In this paper, we propose a novel topological approach to characterize interactions between two nodes. Our topological feature, based on the extended persistent homology, encodes rich structural information regarding the multi-hop paths connecting nodes. Based on this feature, we propose a graph neural network method that outperforms state-of-the-arts on different benchmarks. As another contribution, we propose a novel algorithm to more efficiently compute the extended persistence diagrams for graphs. This algorithm can be generally applied to accelerate many other topological methods for graph learning tasks.

cs.LG↗

arXiv2021

Topological Detection of Trojaned Neural Networks

Deep neural networks are known to have security issues. One particular threat is the Trojan attack. It occurs when the attackers stealthily manipulate the model's behavior through Trojaned training samples, which can later be exploited. Guided by basic neuroscientific principles we discover subtle -- yet critical -- structural deviation characterizing Trojaned models. In our analysis we use topological tools. They allow us to model high-order dependencies in the networks, robustly compare different networks, and localize structural abnormalities. One interesting observation is that Trojaned models develop short-cuts from input to output layers. Inspired by these observations, we devise a strategy for robust detection of Trojaned models. Compared to standard baselines it displays better performance on multiple benchmarks.

cs.LG↗

arXiv2021

Structure Guided Lane Detection

Recently, lane detection has made great progress with the rapid development of deep neural networks and autonomous driving. However, there exist three mainly problems including characterizing lanes, modeling the structural relationship between scenes and lanes, and supporting more attributes (e.g., instance and type) of lanes. In this paper, we propose a novel structure guided framework to solve these problems simultaneously. In the framework, we first introduce a new lane representation to characterize each instance. Then a topdown vanishing point guided anchoring mechanism is proposed to produce intensive anchors, which efficiently capture various lanes. Next, multi-level structural constraints are used to improve the perception of lanes. In the process, pixel-level perception with binary segmentation is introduced to promote features around anchors and restore lane details from bottom up, a lane-level relation is put forward to model structures (i.e., parallel) around lanes, and an image-level attention is used to adaptively attend different regions of the image from the perspective of scenes. With the help of structural guidance, anchors are effectively classified and regressed to obtain precise locations and shapes. Extensive experiments on public benchmark datasets show that the proposed approach outperforms state-of-the-art methods with 117 FPS on a single GPU.

cs.CV↗

arXiv2021

Information flow based defensive chain for data leakage detection and prevention: a survey

Mobile and IoT applications have greatly enriched our daily life by providing convenient and intelligent services. However, these smart applications have been a prime target of adversaries for stealing sensitive data. It poses a crucial threat to users' identity security, financial security, or even life security. Research communities and industries have proposed many Information Flow Control (IFC) techniques for data leakage detection and prevention, including secure modeling, type system, static analysis, dynamic analysis, \textit{etc}. According to the application's development life cycle, although most attacks are conducted during the application's execution phase, data leakage vulnerabilities have been introduced since the design phase. With a focus on lifecycle protection, this survey reviews the recent representative works adopted in different phases. We propose an information flow based defensive chain, which provides a new framework to systematically understand various IFC techniques for data leakage detection and prevention in Mobile and IoT applications. In line with the phases of the application life cycle, each reviewed work is comprehensively studied in terms of technique, performance, and limitation. Research challenges and future directions are also pointed out by consideration of the integrity of the defensive chain.

cs.CR↗

arXiv2021

Convolutional Block Design for Learned Fractional Downsampling

The layers of convolutional neural networks (CNNs) can be used to alter the resolution of their inputs, but the scaling factors are limited to integer values. However, in many image and video processing applications, the ability to resize by a fractional factor would be advantageous. One example is conversion between resolutions standardized for video compression, such as from 1080p to 720p. To solve this problem, we propose an alternative building block, formulated as a conventional convolutional layer followed by a differentiable resizer. More concretely, the convolutional layer preserves the resolution of the input, while the resizing operation is fully handled by the resizer. In this way, any CNN architecture can be adapted for non-integer resizing. As an application, we replace the resizing convolutional layer of a modern deep downsampling model by the proposed building block, and apply it to an adaptive bitrate video streaming scenario. Our experimental results show that an improvement in coding efficiency over the conventional Lanczos algorithm is attained, in terms of PSNR, SSIM, and VMAF on test videos.

eess.IV↗

arXiv2021

Alignment dependence of photoelectron momentum distributions for diatomic molecules N$_2$ in strong elliptical laser fields

We study ionization dynamics of aligned diatomic molecules N$_2$ in strong elliptical laser fields experimentally and theoretically. The alignment dependence of photoelectron momentum distributions (PMDs) of N$_2$ measured in experiments is highlighted with comparing to Ar measured synchronously. Our results show that the PMDs of N$_2$ depend strongly on the alignment of the molecule, relative to the main axis of the laser ellipse. In particular, the most-probable electron-emission angle which is often used in attosecond measurement, differs remarkably when changing the molecular alignment. We show that the interplay of two-center interference and tunneling when the electron goes through the laser-Coulomb-formed barrier, plays an important role in these phenomena. Our work gives suggestions on studying ultrafast electron motion inside aligned molecules.

physics.atom-ph↗

arXiv2021

TopoTxR: A Topological Biomarker for Predicting Treatment Response in Breast Cancer

Characterization of breast parenchyma on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity of underlying tissue structures. Current quantitative approaches, including radiomics and deep learning models, do not explicitly capture the complex and subtle parenchymal structures, such as fibroglandular tissue. In this paper, we propose a novel method to direct a neural network's attention to a dedicated set of voxels surrounding biologically relevant tissue structures. By extracting multi-dimensional topological structures with high saliency, we build a topology-derived biomarker, TopoTxR. We demonstrate the efficacy of TopoTxR in predicting response to neoadjuvant chemotherapy in breast cancer. Our qualitative and quantitative results suggest differential topological behavior of breast tissue on treatment-naïve imaging, in patients who respond favorably to therapy versus those who do not.

q-bio.QM↗

arXiv2021

Privacy Inference Attacks and Defenses in Cloud-based Deep Neural Network: A Survey

Deep Neural Network (DNN), one of the most powerful machine learning algorithms, is increasingly leveraged to overcome the bottleneck of effectively exploring and analyzing massive data to boost advanced scientific development. It is not a surprise that cloud computing providers offer the cloud-based DNN as an out-of-the-box service. Though there are some benefits from the cloud-based DNN, the interaction mechanism among two or multiple entities in the cloud inevitably induces new privacy risks. This survey presents the most recent findings of privacy attacks and defenses appeared in cloud-based neural network services. We systematically and thoroughly review privacy attacks and defenses in the pipeline of cloud-based DNN service, i.e., data manipulation, training, and prediction. In particular, a new theory, called cloud-based ML privacy game, is extracted from the recently published literature to provide a deep understanding of state-of-the-art research. Finally, the challenges and future work are presented to help researchers to continue to push forward the competitions between privacy attackers and defenders.

cs.CR↗

arXiv2021

Impacts of Hawking Radiation from Primordial Black Holes in Critical Collapse Model on the Light Element Abundances

We study the photodisintegration process triggered by the nonthermal electromagnetic Hawking radiation from primordial black holes (PBHs) in critical collapse model. We consider the simplest case that all PBHs formed at a single epoch stemming from an inflationary spectrum with a narrow peak, and an extended mass distribution is obtained due to critical phenomena of gravitational collapse. The presence of a low-mass tail of critical collapse mass function could lead to an enhancement of energetic photon emissions from Hawking radiation of PBHs. Nuclear photodisintegration rates are calculated with a nonthermal photon spectrum derived by solving the Boltzmann equation iteratively. The exact spectrum is much different than that based on an often-used analytical bended power-law spectrum and it is found to significantly depend on the adopted PBH mass functions. With the newest observational limit on the $^3$He abundance in Galactic H II regions, the updated $^3$He constraints on PBH mass spectrum in the horizon mass range $10^{12} - 10^{13}$ g are derived. Our results for the first time show that $^3$He constraints on the critical mass function are about one order of magnitude severer than the monochromatic one although the fraction of PBHs in the low-mass tail region is relatively small. The $^6$Li elemental abundance is also enhanced significantly for the critical mass function. More precise measurement of $^6$Li abundance is highly desirable to provide a promising constraint on PBHs in the future. For monochromatic mass function, we provide the analytical bounds for photodisintegration and hadrodissociation from PBH radiation, and we report discrepancies between our updated $^3$He constraints and the previous results.

astro-ph.CO↗

arXiv2021

Phase of Nonlinear Systems

In this paper, we propose a definition of phase for a class of stable nonlinear systems called semi-sectorial systems, from an input-output perspective. The definition involves the Hilbert transform as a critical instrument to complexify real-valued signals since the notion of phase arises most naturally in the complex domain. The proposed nonlinear system phase, serving as a counterpart of $\mathcal{L}_2$-gain, quantifies the passivity and is highly related to the dissipativity. It also possesses a nice physical interpretation which quantifies the tradeoff between the real energy and reactive energy. A nonlinear small phase theorem is then established for feedback stability analysis of semi-sectorial systems. Additionally, its generalized version is proposed via the use of multipliers. These nonlinear small phase theorems generalize a version of the classical passivity theorem and a recently appeared linear time-invariant small phase theorem.

eess.SY↗