Qu, Feng
· Professor. Molecular Plant Virology, Plant Resistance.VerifiedOhio State University · Plant Pathology
Active 2012–2024
About
Feng Qu is a Professor in the Department of Plant Pathology at The Ohio State University, with a research focus on Molecular Plant Virology and Plant Resistance. Based at Selby Hall in Wooster, his work involves studying the molecular mechanisms underlying plant-virus interactions and developing strategies for disease resistance in crops. His contributions are centered on understanding how viruses infect plants and how plants can be bred or engineered for improved resistance, thereby supporting sustainable agriculture and crop protection.
Research topics
- Computer Science
- Artificial Intelligence
- Machine Learning
- Mathematics
- Computer graphics (images)
- Statistics
- Biology
- Computer vision
Selected publications
Edge-Connected Jaccard Similarity for Graph Link Prediction on FPGA
2022 · 11 citations
- Computer Science
- Computer Science
- Parallel computing
Graph analysis is a critical task in many fields, such as social networking, epidemiology, bioinformatics, and fraud de-tection. In particular, understanding and inferring relationships between graph elements lies at the core of many graph-based workloads. Real-world graph workloads and their associated data structures create irregular computational patterns that compli-cate the realization of high-performance kernels. Given these complications, there does not exist a de facto “best” architecture, language, or algorithmic approach that simultaneously balances performance, energy efficiency, portability, and productivity. In this paper, we realize different algorithms of edge-connected Jaccard similarity for graph link prediction and characterize their performance across a broad spectrum of graphs on an Intel Stratix 10 FPGA. By utilizing a high-level synthesis (HLS)-driven, high-productivity approach (via the C++-based SYCL language) we rapidly prototype two implementations - a from-scratch edge-centric version and a faithfully-ported commodity GPU implementation - which would have been intractable via a hardware description language. With these implementations, we further consider the benefit and necessity of four HLS-enabled optimizations, both in isolation and in concert - totaling seven distinct synthesized hardware pipelines. Leveraging real-world graphs of up to 516 million edges, we show empirically-measured speedups of up to 9.5 x over the initial HLS implementations when all optimizations work in concert.
Detail Me More: Improving GAN’s photo-realism of complex scenes
2021 IEEE/CVF International Conference on Computer Vision (ICCV) · 2021 · 18 citations
- Computer Science
- Artificial Intelligence
- Computer Science
Generative models can synthesize photo-realistic images of a single object. For example, for human faces, algorithms learn to model the local shape and shading of the face components, i.e., changes in the brows, eyes, nose, mouth, jaw line, etc. This is possible because all faces have two brows, two eyes, a nose and a mouth, approximately in the same location. The modeling of complex scenes is however much more challenging because the scene components and their location vary from image to image. For example, living rooms contain a varying number of products belonging to many possible categories and locations, e.g., a lamp may or may not be present in an endless number of possible locations. In the present work, we propose to add a "broker" module in Generative Adversarial Networks (GAN) to solve this problem. The broker is tasked to mediate the use of multiple discriminators in the appropriate image locales. For example, if a lamp is detected or wanted in a specific area of the scene, the broker assigns a fine-grained lamp discriminator to that image patch. This allows the generator to learn the shape and shading models of the lamp. The resulting multi-fine-grained optimization problem is able to synthesize complex scenes with almost the same level of photo-realism as single object images. We demonstrate the generability of the proposed approach on several GAN algorithms (BigGAN, ProGAN, StyleGAN, StyleGAN2), image resolutions (256 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> to 1024 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), and datasets. Our approach yields significant improvements over state-of-the-art GAN algorithms.
When do GANs replicate? On the choice of dataset size
2021 IEEE/CVF International Conference on Computer Vision (ICCV) · 2021 · 39 citations
1st authorCorresponding- Computer Science
- Computer Science
- Artificial Intelligence
Do GANs replicate training images? Previous studies have shown that GANs do not seem to replicate training data without significant change in the training procedure. This leads to a series of research on the exact condition needed for GANs to overfit to the training data. Although a number of factors has been theoretically or empirically identified, the effect of dataset size and complexity on GANs replication is still unknown. With empirical evidence from BigGAN and StyleGAN2, on datasets CelebA, Flower and LSUN-bedroom, we show that dataset size and its complexity play an important role in GANs replication and perceptual quality of the generated images. We further quantify this relationship, discovering that replication percentage decays exponentially with respect to dataset size and complexity, with a shared decaying factor across GAN-dataset combinations. Meanwhile, the perceptual image quality follows a U-shape trend w.r.t dataset size. This finding leads to a practical tool for one-shot estimation on minimal dataset size to prevent GAN replication which can be used to guide datasets construction and selection.
Quantum Engineering With Hybrid Magnonic Systems and Materials <i>(Invited Paper)</i>
IEEE Transactions on Quantum Engineering · 2021 · 143 citations
- Physics
- Quantum mechanics
Quantum technology has made tremendous strides over the past two decades with remarkable advances in materials engineering, circuit design, and dynamic operation. In particular, the integration of different quantum modules has benefited from hybrid quantum systems, which provide an important pathway for harnessing different natural advantages of complementary quantum systems and for engineering new functionalities. This review article focuses on the current frontiers with respect to utilizing magnons for novel quantum functionalities. Magnons are the fundamental excitations of magnetically ordered solid-state materials and provide great tunability and flexibility for interacting with various quantum modules for integration in diverse quantum systems. The concomitant-rich variety of physics and material selection enable exploration of novel quantum phenomena in materials science and engineering. In addition, the ease of generating strong coupling with other excitations makes hybrid magnonics a unique platform for quantum engineering. We start our discussion with circuit-based hybrid magnonic systems, which are coupled with microwave photons and acoustic phonons. Subsequently, we focus on the recent progress of magnon–magnon coupling within confined magnetic systems. Next, we highlight new opportunities for understanding the interactions between magnons and nitrogen-vacancy centers for quantum sensing and implementing quantum interconnects. Lastly, we focus on the spin excitations and magnon spectra of novel quantum materials investigated with advanced optical characterization.
Genome-wide detection of cytosine methylations in plant from Nanopore data using deep learning
Nature Communications · 2021 · 125 citations
- Computational biology
- Biology
- Genetics
In plants, cytosine DNA methylations (5mCs) can happen in three sequence contexts as CpG, CHG, and CHH (where H = A, C, or T), which play different roles in the regulation of biological processes. Although long Nanopore reads are advantageous in the detection of 5mCs comparing to short-read bisulfite sequencing, existing methods can only detect 5mCs in the CpG context, which limits their application in plants. Here, we develop DeepSignal-plant, a deep learning tool to detect genome-wide 5mCs of all three contexts in plants from Nanopore reads. We sequence Arabidopsis thaliana and Oryza sativa using both Nanopore and bisulfite sequencing. We develop a denoising process for training models, which enables DeepSignal-plant to achieve high correlations with bisulfite sequencing for 5mC detection in all three contexts. Furthermore, DeepSignal-plant can profile more 5mC sites, which will help to provide a more complete understanding of epigenetic mechanisms of different biological processes.
Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019
Journal of the American College of Cardiology · 2020 · 10765 citations
- Medicine
- Demography
- Environmental health
Cardiovascular diseases (CVDs), principally ischemic heart disease (IHD) and stroke, are the leading cause of global mortality and a major contributor to disability. This paper reviews the magnitude of total CVD burden, including 13 underlying causes of cardiovascular death and 9 related risk factors, using estimates from the Global Burden of Disease (GBD) Study 2019. GBD, an ongoing multinational collaboration to provide comparable and consistent estimates of population health over time, used all available population-level data sources on incidence, prevalence, case fatality, mortality, and health risks to produce estimates for 204 countries and territories from 1990 to 2019. Prevalent cases of total CVD nearly doubled from 271 million (95% uncertainty interval [UI]: 257 to 285 million) in 1990 to 523 million (95% UI: 497 to 550 million) in 2019, and the number of CVD deaths steadily increased from 12.1 million (95% UI:11.4 to 12.6 million) in 1990, reaching 18.6 million (95% UI: 17.1 to 19.7 million) in 2019. The global trends for disability-adjusted life years (DALYs) and years of life lost also increased significantly, and years lived with disability doubled from 17.7 million (95% UI: 12.9 to 22.5 million) to 34.4 million (95% UI:24.9 to 43.6 million) over that period. The total number of DALYs due to IHD has risen steadily since 1990, reaching 182 million (95% UI: 170 to 194 million) DALYs, 9.14 million (95% UI: 8.40 to 9.74 million) deaths in the year 2019, and 197 million (95% UI: 178 to 220 million) prevalent cases of IHD in 2019. The total number of DALYs due to stroke has risen steadily since 1990, reaching 143 million (95% UI: 133 to 153 million) DALYs, 6.55 million (95% UI: 6.00 to 7.02 million) deaths in the year 2019, and 101 million (95% UI: 93.2 to 111 million) prevalent cases of stroke in 2019. Cardiovascular diseases remain the leading cause of disease burden in the world. CVD burden continues its decades-long rise for almost all countries outside high-income countries, and alarmingly, the age-standardized rate of CVD has begun to rise in some locations where it was previously declining in high-income countries. There is an urgent need to focus on implementing existing cost-effective policies and interventions if the world is to meet the targets for Sustainable Development Goal 3 and achieve a 30% reduction in premature mortality due to noncommunicable diseases.
Scientific Reports · 2020 · 18 citations
- Computer Science
- Computer Science
- Algorithm
Despite decades of research, effective treatments for most cancers remain elusive. One reason is that different instances of cancer result from different combinations of multiple genetic mutations (hits). Therefore, treatments that may be effective in some cases are not effective in others. We previously developed an algorithm for identifying combinations of carcinogenic genes with mutations (multi-hit combinations), which could suggest a likely cause for individual instances of cancer. Most cancers are estimated to require three or more hits. However, the computational complexity of the algorithm scales exponentially with the number of hits, making it impractical for identifying combinations of more than two hits. To identify combinations of greater than two hits, we used a compressed binary matrix representation, and optimized the algorithm for parallel execution on an NVIDIA V100 graphics processing unit (GPU). With these enhancements, the optimized GPU implementation was on average an estimated 12,144 times faster than the original integer matrix based CPU implementation, for the 3-hit algorithm, allowing us to identify 3-hit combinations. The 3-hit combinations identified using a training set were able to differentiate between tumor and normal samples in a separate test set with 90% overall sensitivity and 93% overall specificity. We illustrate how the distribution of mutations in tumor and normal samples in the multi-hit gene combinations can suggest potential driver mutations for further investigation. With experimental validation, these combinations may provide insight into the etiology of cancer and a rational basis for targeted combination therapy.
DeepPower: Non-intrusive and Deep Learning-based Detection of IoT Malware Using Power Side Channels
2020 · 51 citations
- Computer Science
- Computer Science
- Computer Security
The vulnerability of Internet of Things (IoT) devices to malware attacks poses huge challenges to current Internet security. The IoT malware attacks are usually composed of three stages: intrusion, infection and monetization. Existing approaches for IoT malware detection cannot effectively identify the executed malicious activities at intrusion and infection stages, and thus cannot help stop potential attacks timely. In this paper, we present DeepPower, a non-intrusive approach to infer malicious activities of IoT malware via analyzing power side-channel signals using deep learning. DeepPower first filters raw power signals of IoT devices to obtain suspicious signals, and then performs a fine-grained analysis on these signals to infer corresponding executed activities inside the devices. DeepPower determines whether there exists an ongoing malware infection by conducting a correlation analysis on these identified activities. We implement a prototype of DeepPower leveraging low-cost sensors and devices and evaluate the effectiveness of DeepPower against real-world IoT malware using commodity IoT devices. Our experimental results demonstrate that DeepPower is able to detect infection activities of different IoT malware with a high accuracy without any changes to the monitored devices.
Alleviating Load Imbalance in Data Processing for Large-Scale Deep Learning
2020 · 8 citations
- Computer Science
- Artificial Intelligence
- Computer Science
Scalable deep learning remains an onerous challenge, as it is constrained by many factors, including those related to load imbalance. For many deep-learning software systems, multiple data-processing components-including neural network training, graph scheduling, input pipeline, and gradient synchronization-execute simultaneously and asynchronously. Such execution can cause the various data-processing components to contend with one another for the hardware resources, leading to severe load imbalance and, in turn, degraded scalability. In this paper, we present an in-depth analysis of state-of-the-art deep-learning software, TensorFlow and Horovod, to understand their scalability limitations. Based on this analysis, we propose four novel solutions that minimize resource contention and improve deep-learning performance by up to 35% for training various neural networks on 24,576 GPUs of the Summit supercomputer at Oak Ridge National Laboratory.
Hypertension · 2020 · 30 citations
- Cancer research
- Medicine
- Pharmacology
In recent years, mesenchymal stem cells (MSCs)-derived extracellular vesicles (EVs) are emerging as a potential therapeutic agent for pulmonary hypertension (PH). However, the full realization of MSCs-derived EVs therapy has been hampered by the absence of standardization in MSCs culture and the challenges of industrial scale-up. The study was to exploit an alternative replacement for MSCs using currently commercialized stem cell lines for effective targeted PH therapy. ReNcell VM-a human neural stem cell line-has been utilized here as a reliable and easily adoptable source of EVs. We first demonstrated that ReNcell-derived EVs (ReNcell-EVs) pretreatment effectively prevented Su/Hx (SU5416/hypoxia)-induced PH in mice. Then for targeted therapy, we conjugated ReNcell-EVs with CAR (CARSKNKDC) peptide (CAR-EVs)-a peptide identified to specifically target hypertensive pulmonary arteries, by bio-orthogonal chemistry. Intravenous administration of CAR-EVs selectively targeted hypertensive pulmonary artery lesions especially pulmonary artery smooth muscle cells. Moreover, compared with unmodified ReNcell-EVs, CAR-EVs treatment significantly improved therapeutic effect in reversing Su/Hx-induced PH in mice. Mechanistically, ReNcell-EVs inhibited hypoxia-induced proliferation, migration, and phenotype switch of pulmonary artery smooth muscle cells, at least in part, via the delivery of its endogenous highly expressed miRNAs, let-7b-5p, miR-92b-3p, and miR-100-5p. In addition, we also found that ReNcell-EVs inhibited hypoxia-induced cell apoptosis and endothelial-mesenchymal transition in human microvascular endothelial cells. Taken together, our results provide an alternative to MSCs-derived EVs-based PH therapy via using ReNcell as a reliable source of EVs. Particularly, our CAR-conjugated EVs may serve as a novel drug carrier that enhances the specificity and efficiency of drug delivery for effective PH-targeted therapy.
Frequent coauthors
- 15 shared
Aleix M. Martı́nez
Amazon (Germany)
- 7 shared
Chenqi Guo
North China Electric Power University
- 7 shared
Fabian Benitez-Quiroz
- 3 shared
Shiwei Zhong
- 3 shared
Ramprakash Srinivasan
- 3 shared
Yinglong Ma
- 3 shared
Raghudeep Gadde
Amazon (Germany)
- 2 shared
C. Fabian Benitez-Quiroz
The Ohio State University
Labs
Feng Qu LaboratoryPI
Education
- 2005
Ph.D., Plant Pathology
The Ohio State University
- 2001
M.S., Plant Pathology
The Ohio State University
- 1998
B.S., Plant Pathology
The Ohio State University
Awards & honors
- A. J. Hoffmann Award
- CC Allison Award
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