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  1. Abstract Background

    Studies on maize evolution and domestication are largely limited to the nuclear genomes, and the contribution of cytoplasmic genomes to selection and domestication of modern maize remains elusive. Maize cytoplasmic genomes have been classified into fertile (NA and NB) and cytoplasmic-nuclear male-sterility (CMS-S, CMS-C, and CMS-T) groups, but their contributions to modern maize breeding have not been systematically investigated.

    Results

    Here we report co-selection and convergent evolution between nuclear and cytoplasmic genomes by analyzing whole genome sequencing data of 630 maize accessions modern maize and its relatives, including 24 fully assembled mitochondrial and chloroplast genomes. We show that the NB cytotype is associated with the expansion of modern maize to North America, gradually replaces the fertile NA cytotype probably through unequal division, and predominates in over 90% of modern elite inbred lines. The mode of cytoplasmic evolution is increased nucleotypic diversity among the genes involved in photosynthesis and energy metabolism, which are driven by selection and domestication. Furthermore, genome-wide association study reveals correlation of cytoplasmic nucleotypic variation with key agronomic and reproductive traits accompanied with the diversification of the nuclear genomes.

    Conclusions

    Our results indicate convergent evolution between cytoplasmic and nuclear genomes during maize domestication and breeding. These new insights into the important roles of mitochondrial and chloroplast genomes in maize domestication and improvement should help select elite inbred lines to improve yield stability and crop resilience of maize hybrids.

     
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  2. Possessing a unique combination of properties that are traditionally contradictory in other natural or synthetical materials, Ga-based liquid metals (LMs) exhibit low mechanical stiffness and flowability like a liquid, with good electrical and thermal conductivity like metal, as well as good biocompatibility and room-temperature phase transformation. These remarkable properties have paved the way for the development of novel reconfigurable or stretchable electronics and devices. Despite these outstanding properties, the easy oxidation, high surface tension, and low rheological viscosity of LMs have presented formidable challenges in high-resolution patterning. To address this challenge, various surface modifications or additives have been employed to tailor the oxidation state, viscosity, and patterning capability of LMs. One effective approach for LM patterning is breaking down LMs into microparticles known as liquid metal particles (LMPs). This facilitates LM patterning using conventional techniques such as stencil, screening, or inkjet printing. Judiciously formulated photo-curable LMP inks or the introduction of an adhesive seed layer combined with a modified lift-off process further provide the micrometer-level LM patterns. Incorporating porous and adhesive substrates in LM-based electronics allows direct interfacing with the skin for robust and long-term monitoring of physiological signals. Combined with self-healing polymers in the form of substrates or composites, LM-based electronics can provide mechanical-robust devices to heal after damage for working in harsh environments. This review provides the latest advances in LM-based composites, fabrication methods, and their novel and unique applications in stretchable or reconfigurable sensors and resulting integrated systems. It is believed that the advancements in LM-based material preparation and high-resolution techniques have opened up opportunities for customized designs of LM-based stretchable sensors, as well as multifunctional, reconfigurable, highly integrated, and even standalone systems. 
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  3. In this paper, a monotonic power side-channel attack (PSA) is proposed to analyze the security vulnerabilities of flash analog-to-digital converters (ADC), where the digital output of a flash ADC is determined by characterizing the monotonic relationship between the traces of the power consumed and the applied input signals. A novel technique that leverages clock phase division is proposed to secure the power side channel information of a 4-bit flash ADC. The proposed technique adds randomness to decorrelate the input signal from the given power trace as the execution phase of each comparator depends on a thermometer code computed from the previous seven clock cycles. The monotonic PSA is executed on both a secured and unsecured ADC, with results indicating 1.9 bits of information leakage from an unprotected ADC and no data leakage from a protected ADC as the bit-wise accuracy is approximately 50% when secured. The monotonic PSA is more effective at attacking a flash ADC architecture than either a convolutional neural network based PSA or a correlation template PSA. The secured ADC core occupies approximately 2% more area than a non-secure ADC in a 65 nm process, and provides a sampling frequency of up to 500 MHz at a supply voltage of 1.2 V. Index Terms—power side-channel, ADC, 
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  4. Labels are widely used in augmented reality (AR) to display digital information. Ensuring the readability of AR labels requires placing them occlusion-free while keeping visual linkings legible, especially when multiple labels exist in the scene. Although existing optimization-based methods, such as force-based methods, are effective in managing AR labels in static scenarios, they often struggle in dynamic scenarios with constantly moving objects. This is due to their focus on generating layouts optimal for the current moment, neglecting future moments and leading to sub-optimal or unstable layouts over time. In this work, we present RL-LABEL, a deep reinforcement learning-based method for managing the placement of AR labels in scenarios involving moving objects. RL-LABEL considers the current and predicted future states of objects and labels, such as positions and velocities, as well as the user’s viewpoint, to make informed decisions about label placement. It balances the trade-offs between immediate and long-term objectives. Our experiments on two real-world datasets show that RL-LABEL effectively learns the decision-making process for long-term optimization, outperforming two baselines (i.e., no view management and a force-based method) by minimizing label occlusions, line intersections, and label movement distance. Additionally, a user study involving 18 participants indicates that RL-LABEL excels over the baselines in aiding users to identify, compare, and summarize data on AR labels within dynamic scenes. 
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  5. Badminton is a fast-paced sport that requires a strategic combination of spatial, temporal, and technical tactics. To gain a competitive edge at high-level competitions, badminton professionals frequently analyze match videos to gain insights and develop game strategies. However, the current process for analyzing matches is time-consuming and relies heavily on manual note-taking, due to the lack of automatic data collection and appropriate visualization tools. As a result, there is a gap in effectively analyzing matches and communicating insights among badminton coaches and players. This work proposes an end-to-end immersive match analysis pipeline designed in close collaboration with badminton professionals, including Olympic and national coaches and players. We present VIRD, a VR Bird (i.e., shuttle) immersive analysis tool, that supports interactive badminton game analysis in an immersive environment based on 3D reconstructed game views of the match video. We propose a top-down analytic workflow that allows users to seamlessly move from a high-level match overview to a detailed game view of individual rallies and shots, using situated 3D visualizations and video. We collect 3D spatial and dynamic shot data and player poses with computer vision models and visualize them in VR. Through immersive visualizations, coaches can interactively analyze situated spatial data (player positions, poses, and shot trajectories) with flexible viewpoints while navigating between shots and rallies effectively with embodied interaction. We evaluated the usefulness of VIRD with Olympic and national-level coaches and players in real matches. Results show that immersive analytics supports effective badminton match analysis with reduced context-switching costs and enhances spatial understanding with a high sense of presence. 
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  6. Rowing requires physical strength and endurance in athletes as well as a precise rowing technique. The ideal rowing stroke is based on biomechanical principles and typically takes years to master. Except for time-consuming video analysis after practice, coaches currently have no means to quantitatively analyze a rower’s stroke sequence and body movement. We propose ARrow, an AR application for coaches and athletes that provides real-time and situated feedback on a rower’s body position and stroke. We use computer vision techniques to extract the rower’s 3D skeleton and to detect the rower’s stroke cycle. ARrow provides visual feedback on three levels: Tracking of basic performance metrics over time, visual feedback and guidance on a rower’s stroke sequence, and a rowing ghost view that helps synchronize the body movement of two rowers. We developed ARrow in close collaboration with international rowing coaches and demonstrated its usefulness in a user study with athletes and coaches. 
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