Browsing by Author "Zhang, Lei"
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- Acoustic differences between healthy and depressed people: a cross-situation studyWang, Jingying; Zhang, Lei; Liu, Tianli; Pan, Wei; Hu, Bin; Zhu, Tingshao (2019-10-15)Background Abnormalities in vocal expression during a depressed episode have frequently been reported in people with depression, but less is known about if these abnormalities only exist in special situations. In addition, the impacts of irrelevant demographic variables on voice were uncontrolled in previous studies. Therefore, this study compares the vocal differences between depressed and healthy people under various situations with irrelevant variables being regarded as covariates. Methods To examine whether the vocal abnormalities in people with depression only exist in special situations, this study compared the vocal differences between healthy people and patients with unipolar depression in 12 situations (speech scenarios). Positive, negative and neutral voice expressions between depressed and healthy people were compared in four tasks. Multiple analysis of covariance (MANCOVA) was used for evaluating the main effects of variable group (depressed vs. healthy) on acoustic features. The significances of acoustic features were evaluated by both statistical significance and magnitude of effect size. Results The results of multivariate analysis of covariance showed that significant differences between the two groups were observed in all 12 speech scenarios. Although significant acoustic features were not the same in different scenarios, we found that three acoustic features (loudness, MFCC5 and MFCC7) were consistently different between people with and without depression with large effect magnitude. Conclusions Vocal differences between depressed and healthy people exist in 12 scenarios. Acoustic features including loudness, MFCC5 and MFCC7 have potentials to be indicators for identifying depression via voice analysis. These findings support that depressed people’s voices include both situation-specific and cross-situational patterns of acoustic features.
- Bilevel Optimization in the Deep Learning Era: Methods and ApplicationsZhang, Lei (Virginia Tech, 2024-01-05)Neural networks, coupled with their associated optimization algorithms, have demonstrated remarkable efficacy and versatility across an extensive array of tasks, encompassing image recognition, speech recognition, object detection, sentiment analysis, and more. The inherent strength of neural networks lies in their capability to autonomously learn intricate representations that map input data to corresponding output labels seamlessly. Nevertheless, not all tasks can be neatly encapsulated within the confines of an end-to-end learning paradigm. The complexity and diversity of real-world challenges necessitate innovative approaches that extend beyond conventional formulations. This calls for the exploration of specialized architectures and optimization strategies tailored to the unique intricacies of specific tasks, ensuring a more nuanced and effective solution to the myriad demands of diverse applications. The bi-level optimization problem stands out as a distinctive form of optimization, characterized by the embedding or nesting of one problem within another. Its relevance persists significantly in the current era dominated by deep learning. A notable instance of its application in the realm of deep learning is observed in hyperparameter optimization. In the context of neural networks, the automatic training of weights through backpropagation represents a crucial aspect. However, certain hyperparameters, such as the learning rate (lr) and the number of layers, must be predetermined and cannot be optimized through the conventional chain rule employed in backpropagation. This underscores the importance of bi-level optimization in addressing the intricate task of fine-tuning these hyperparameters to enhance the overall performance of deep learning models. The domain of deep learning presents a fertile ground for further exploration and discoveries in optimization. The untapped potential for refining hyperparameters and optimizing various aspects of neural network architectures highlights the ongoing opportunities for advancements and breakthroughs in this dynamic field. Within this thesis, we delve into significant bi-level optimization challenges, applying these techniques to pertinent real-world tasks. Given that bi-level optimization entails dual layers of optimization, we explore scenarios where neural networks are present in the upper-level, the inner-level, or both. To be more specific, we systematically investigate four distinct tasks: optimizing neural networks towards optimizing neural networks, optimizing attractors towards optimizing neural networks, optimizing graph structures towards optimizing neural network performance, and optimizing architecture towards optimizing neural networks. For each of these tasks, we formulate the problems using the bi-level optimization approach mathematically, introducing more efficient optimization strategies. Furthermore, we meticulously evaluate the performance and efficiency of our proposed techniques. Importantly, our methodologies and insights transcend the realm of bi-level optimization, extending their applicability broadly to various deep learning models. The contributions made in this thesis offer valuable perspectives and tools for advancing optimization techniques in the broader landscape of deep learning.
- Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural NetworksChen, Zhiqian; Chen, Fanglan; Zhang, Lei; Ji, Taoran; Fu, Kaiqun; Zhao, Liang; Chen, Feng; Wu, Lingfei; Aggarwal, Charu; Lu, Chang-Tien (ACM, 2023-10)Deep learning's performance has been extensively recognized recently. Graph neural networks (GNNs) are designed to deal with graph-structural data that classical deep learning does not easily manage. Since most GNNs were created using distinct theories, direct comparisons are impossible. Prior research has primarily concentrated on categorizing existing models, with little attention paid to their intrinsic connections. The purpose of this study is to establish a unified framework that integrates GNNs based on spectral graph and approximation theory. The framework incorporates a strong integration between spatial- and spectral-based GNNs while tightly associating approaches that exist within each respective domain.
- Deep Graph Learning for Circuit DeobfuscationChen, Zhiqian; Zhang, Lei; Kolhe, Gaurav; Kamali, Hadi Mardani; Rafatirad, Setareh; Pudukotai Dinakarrao, Sai Manoj; Homayoun, Houman; Lu, Chang-Tien; Zhao, Liang (2021-05-24)Circuit obfuscation is a recently proposed defense mechanism to protect the intellectual property (IP) of digital integrated circuits (ICs) from reverse engineering. There have been effective schemes, such as satisfiability (SAT)-checking based attacks that can potentially decrypt obfuscated circuits, which is called deobfuscation. Deobfuscation runtime could be days or years, depending on the layouts of the obfuscated ICs. Hence, accurately pre-estimating the deobfuscation runtime within a reasonable amount of time is crucial for IC designers to optimize their defense. However, it is challenging due to (1) the complexity of graph-structured circuit; (2) the varying-size topology of obfuscated circuits; (3) requirement on efficiency for deobfuscation method. This study proposes a framework that predicts the deobfuscation runtime based on graph deep learning techniques to address the challenges mentioned above. A conjunctive normal form (CNF) bipartite graph is utilized to characterize the complexity of this SAT problem by analyzing the SAT attack method. Multi-order information of the graph matrix is designed to identify the essential features and reduce the computational cost. To overcome the difficulty in capturing the dynamic size of the CNF graph, an energy-based kernel is proposed to aggregate dynamic features into an identical vector space. Then, we designed a framework, Deep Survival Analysis with Graph (DSAG), which integrates energy-based layers and predicts runtime inspired by censored regression in survival analysis. Integrating uncensored data with censored data, the proposed model improves the standard regression significantly. DSAG is an end-to-end framework that can automatically extract the determinant features for deobfuscation runtime. Extensive experiments on benchmarks demonstrate its effectiveness and efficiency.
- Exploring Effect of Level of Storytelling Richness on Science Learning in Interactive and Immersive Virtual RealityZhang, Lei; Bowman, Douglas A. (ACM, 2022-06-21)Immersive and interactive storytelling in virtual reality (VR) is an emerging creative practice that has been thriving in recent years. Educational applications using immersive VR storytelling to explain complex science concepts have very promising pedagogical benefts because on the one hand, storytelling breaks down the complexity of science concepts by bridging them to people’s everyday experiences and familiar cognitive models, and on the other hand, the learning process is further reinforced through rich interactivity aforded by the VR experiences. However, it is unclear how diferent amounts of storytelling in an interactive VR storytelling experience may afect learning outcomes due to a paucity of literature on educational VR storytelling research. This preliminary study aims to add to the literature through an exploration of variations in the designs of essential storytelling elements in educational VR storytelling experiences and their impact on the learning of complex immunology concepts.
- Fast and adaptive dynamics-on-graphs to dynamics-of-graphs translationZhang, Lei; Chen, Zhiqian; Lu, Chang-Tien; Zhao, Liang (Frontiers, 2023-11-17)Numerous networks in the real world change with time, producing dynamic graphs such as human mobility networks and brain networks. Typically, the “dynamics on graphs” (e.g., changing node attribute values) are visible, and they may be connected to and suggestive of the “dynamics of graphs” (e.g., evolution of the graph topology). Due to two fundamental obstacles, modeling and mapping between them have not been thoroughly explored: (1) the difficulty of developing a highly adaptable model without solid hypotheses and (2) the ineffectiveness and slowness of processing data with varying granularity. To solve these issues, we offer a novel scalable deep echo-state graph dynamics encoder for networks with significant temporal duration and dimensions. A novel neural architecture search (NAS) technique is then proposed and tailored for the deep echo-state encoder to ensure strong learnability. Extensive experiments on synthetic and actual application data illustrate the proposed method's exceptional effectiveness and efficiency.
- Immunology Virtual Reality (VR): Exploring Educational VR Experience Design for Science LearningZhang, Lei (Virginia Tech, 2018-05-14)Immunology Virtual Reality (VR) project is an immersive educational virtual reality experience that intends to provide an informal learning experience of specific immunology concepts to college freshmen in the Department of Biological Sciences at Virginia Tech (VT). The project is an interdisciplinary endeavor between my collaboration between people from different domain areas at VT: Creative Technologies, Education, Biological Sciences, and Computer Sciences. This thesis elaborates on the whole design process of how I created a working prototype of the project demo and shares insights from my design experience.
- Investigating Interactivity and Storytelling in Immersive Virtual Reality for Science EducationZhang, Lei (Virginia Tech, 2022-01-19)Immersive and interactive storytelling in virtual reality (VR) is an emerging creative practice that has been thriving in recent years. Educational applications using immersive VR storytelling to explain complex science concepts have very promising pedagogical benefits because on the one hand, storytelling breaks down the complexity of science concepts by bridging them to people's everyday experiences and familiar cognitive models, and on the other hand, the learning process is further reinforced through rich interactivity afforded by the VR experiences. However, it is unclear how different amounts of storytelling and interactivity in an interactive VR storytelling experience may affect learning outcomes due to a paucity of literature on educational VR storytelling research. This dissertation aims to add to the literature through an exploration of interactivity and essential storytelling elements in educational VR storytelling experiences and their impact on learning. We designed a working prototype of interactive and immersive VR storytelling experience, Immunology VR, that focuses on the learning of specific immunology concepts: neutrophil transmigration and killing mechanisms. Based on the initial prototype, we further developed six variations that allowed us to conduct two major experiments below. Our first experiment explored designs of three different levels of interactivity, low, medium, and high, in the VR storytelling experiences and their effects on immunology learning. We found subjective evidence to support our research hypothesis that increased level of interactivity will lead to increased engagement in VR learning. Our finding suggests that interactivity is a key design element in VR learning design for effective learning and should be considered in all VR learning applications. Our second experiment focused on the designs of the level of storytelling richness and their effects on learning. Specifically, we designed three storytelling conditions, minimal storytelling, basic storytelling, and advanced storytelling, and investigated how each of them affected immunology learning. Subjective evidence from our user interview data suggested that participants from higher levels of storytelling conditions were more likely to perceive storytelling elements as the most useful features in the VR experience that helped with their learning. It is also suggested that higher levels of richness in essential storytelling elements may trigger certain emotions and empathy in more users and positively affect their learning.
- Measurement of Step Angle for Quantifying the Gait Impairment of Parkinson's Disease by Wearable Sensors: Controlled StudyWang, Jingying; Gong, Dawei; Luo, Huichun; Zhang, Wenbin; Zhang, Lei; Zhang, Han; Zhou, Junhong; Wang, Shouyan (2020-03-20)Background: Gait impairments including shuffling gait and hesitation are common in people with Parkinson's disease (PD), and have been linked to increased fall risk and freezing of gait. Nowadays the gait metrics mostly focus on the spatiotemporal characteristics of gait, but less is known of the angular characteristics of the gait, which may provide helpful information pertaining to the functional status and effects of the treatment in PD. Objective: This study aimed to quantify the angles of steps during walking, and explore if this novel step angle metric is associated with the severity of PD and the effects of the treatment including the acute levodopa challenge test (ALCT) and deep brain stimulation (DBS). Methods: A total of 18 participants with PD completed the walking test before and after the ALCT, and 25 participants with PD completed the test with the DBS on and off. The walking test was implemented under two conditions: walking normally at a preferred speed (single task) and walking while performing a cognitive serial subtraction task (dual task). A total of 17 age-matched participants without PD also completed this walking test. The angular velocity was measured using wearable sensors on each ankle, and three gait angular metrics were obtained, that is mean step angle, initial step angle, and last step angle. The conventional gait metrics (ie, step time and step number) were also calculated. Results: The results showed that compared to the control, the following three step angle metrics were significantly smaller in those with PD: mean step angle (F-1,F-48=69.75, P<.001, partial eta-square=0.59), initial step angle (F-1,F-48=15.56, P<.001, partial eta-square=0.25), and last step angle (F-1,F-48=61.99, P<.001, partial eta-square=0.56). Within the PD cohort, both the ALCT and DBS induced greater mean step angles (ACLT: F-1,F-38=5.77, P=.02, partial eta-square=0.13; DBS: F-1,F-52=8.53, P=.005, partial eta-square=0.14) and last step angles (ACLT: F-1,F-38=10, P=.003, partial eta-square=0.21; DBS: F-1,F-52=4.96, P=.003, partial eta-square=0.09), but no significant changes were observed in step time and number after the treatments. Additionally, these step angles were correlated with the Unified Parkinson's Disease Rating Scale, Part III score: mean step angle (single task: r=-0.60, P<.001; dual task: r=-0.52, P<.001), initial step angle (single task: r=-0.35, P=.006; dual task: r=-0.35, P=.01), and last step angle (single task: r=-0.43, P=.001; dual task: r=-0.41, P=.002). Conclusions: This pilot study demonstrated that the gait angular characteristics, as quantified by the step angles, were sensitive to the disease severity of PD and, more importantly, can capture the effects of treatments on the gait, while the traditional metrics cannot. This indicates that these metrics may serve as novel markers to help the assessment of gait in those with PD as well as the rehabilitation of this vulnerable cohort.
- Measurement of the B0 lifetime and flavor-oscillation frequency using hadronic decays reconstructed in 2019-2021 Belle II dataAblikim, M.; Achasov, M. N.; Adlarson, P.; Ahmed, S.; Albrecht, M.; Amoroso, A.; An, Q.; Bai, X. H.; Bai, Y.; Bakina, O.; Ferroli, R. Baldini; Balossino, I.; Ban, Y.; Begzsuren, K.; Bennett, J.; Berger, N.; Bertani, M.; Bettoni, D.; Bianchi, F.; Biernat, J.; Bloms, J.; Bortone, A.; Boyko, I.; Briere, R. A.; Cai, H.; Cai, X.; Calcaterra, A.; Cao, G. F.; Cao, N.; Cetin, S. A.; Chang, J. F.; Chang, W. L.; Chelkov, G.; Chen, D. Y.; Chen, G.; Chen, H. S.; Chen, M. L.; Chen, S. J.; Chen, X. R.; Chen, Y. B.; Cheng, W.; Cibinetto, G.; Cossio, F.; Cui, X. F.; Dai, H. L.; Dai, J. P.; Dai, X. C.; Dbeyssi, A.; de Boer, R. B.; Dedovich, D.; Deng, Z. Y.; Denig, A.; Denysenko, I.; Destefanis, M.; De Mori, F.; Ding, Y.; Dong, C.; Dong, J.; Dong, L. Y.; Dong, M. Y.; Du, S. X.; Fang, J.; Fang, S. S.; Fang, Y.; Farinelli, R.; Fava, L.; Feldbauer, F.; Felici, G.; Feng, C. Q.; Fritsch, M.; Fu, C. D.; Fu, Y.; Gao, X. L.; Gao, Y.; Gao, Y.; Gao, Y. G.; Garzia, I.; Gersabeck, E. M.; Gilman, A.; Goetzen, K.; Gong, L.; Gong, W. X.; Gradl, W.; Greco, M.; Gu, L. M.; Gu, M. H.; Gu, S.; Gu, Y. T.; Guan, C. Y.; Guo, A. Q.; Guo, L. B.; Guo, R. P.; Guo, Y. P.; Guskov, A.; Han, S.; Han, T. T.; Han, T. Z.; Hao, X. Q.; Harris, F. A.; He, K. L.; Heinsius, F. H.; Held, T.; Heng, Y. K.; Himmelreich, M.; Holtmann, T.; Hou, Y. R.; Hou, Z. L.; Hu, H. M.; Hu, J. F.; Hu, T.; Hu, Y.; Huang, G. S.; Huang, L. Q.; Huang, X. T.; Huesken, N.; Hussain, T.; Andersson, W. Ikegami; Imoehl, W.; Irshad, M.; Jaeger, S.; Ji, Q.; Ji, Q. P.; Ji, X. B.; Ji, X. L.; Jiang, H. B.; Jiang, X. S.; Jiang, X. Y.; Jiao, J. B.; Jiao, Z.; Jin, S.; Jin, Y.; Johansson, T.; Kalantar-Nayestanaki, N.; Kang, X. S.; Kappert, R.; Kavatsyuk, M.; Ke, B. C.; Keshk, I. K.; Khoukaz, A.; Kiese, P.; Kiuchi, R.; Kliemt, R.; Koch, L.; Kolcu, O. B.; Kopf, B.; Kuemmel, M.; Kuessner, M.; Kupsc, A.; Kurth, M. G.; Kuehn, W.; Lane, J. J.; Lange, J. S.; Larin, P.; Lavezzi, L.; Leithoff, H.; Lellmann, M.; Lenz, T.; Li, C.; Li, C. H.; Li, Cheng; Li, D. M.; Li, F.; Li, G.; Li, H. B.; Li, H. J.; Li, J. L.; Li, J. Q.; Li, Ke; Li, L. K.; Li, Lei; Li, P. L.; Li, P. R.; Li, W. D.; Li, W. G.; Li, X. H.; Li, X. L.; Li, Z. B.; Li, Z. Y.; Liang, H.; Liang, H.; Liang, Y. F.; Liang, Y. T.; Liao, L. Z.; Libby, J.; Lin, C. X.; Liu, B.; Liu, B. J.; Liu, C. X.; Liu, D.; Liu, D. Y.; Liu, F. H.; Liu, Fang; Liu, Feng; Liu, H. B.; Liu, H. M.; Liu, Huanhuan; Liu, Huihui; Liu, J. B.; Liu, J. Y.; Liu, K.; Liu, K. Y.; Liu, Ke; Liu, L.; Liu, L. Y.; Liu, Q.; Liu, S. B.; Liu, T.; Liu, X.; Liu, Y. B.; Liu, Z. A.; Liu, Zhiqing; Long, Y. F.; Lou, X. C.; Lu, H. J.; Lu, J. D.; Lu, J. G.; Lu, X. L.; Lu, Y.; Lu, Y. P.; Luo, C. L.; Luo, M. X.; Luo, P. W.; Luo, T.; Luo, X. L.; Lusso, S.; Lyu, X. R.; Ma, F. C.; Ma, H. L.; Ma, L. L.; Ma, M. M.; Ma, Q. M.; Ma, R. Q.; Ma, R. T.; Ma, X. N.; Ma, X. X.; Ma, X. Y.; Ma, Y. M.; Maas, F. E.; Maggiora, M.; Maldaner, S.; Malde, S.; Malik, Q. A.; Mangoni, A.; Mao, Y. J.; Mao, Z. P.; Marcello, S.; Meng, Z. X.; Messchendorp, J. G.; Mezzadri, G.; Min, T. J.; Mitchell, R. E.; Mo, X. H.; Mo, Y. J.; Muchnoi, N. Yu; Muramatsu, H.; Nakhoul, S.; Nefedov, Y.; Nerling, F.; Nikolaev, I. B.; Ning, Z.; Nisar, S.; Olsen, S. L.; Ouyang, Q.; Pacetti, S.; Pan, Y.; Pan, Y.; Papenbrock, M.; Pathak, A.; Patteri, P.; Pelizaeus, M.; Peng, H. P.; Peters, K.; Pettersson, J.; Ping, J. L.; Ping, R. G.; Pitka, A.; Poling, R.; Prasad, V.; Qi, H.; Qi, M.; Qi, T. Y.; Qian, S.; Qiao, C. F.; Qin, L. Q.; Qin, X. P.; Qin, X. S.; Qin, Z. H.; Qiu, J. F.; Qu, S. Q.; Rashid, K. H.; Ravindran, K.; Redmer, C. F.; Rivetti, A.; Rodin, V.; Rolo, M.; Rong, G.; Rosner, Ch; Rump, M.; Sarantsev, A.; Savrie, M.; Schelhaas, Y.; Schnier, C.; Schoenning, K.; Shan, W.; Shan, X. Y.; Shao, M.; Shen, C. P.; Shen, P. X.; Shen, X. Y.; Shi, H. C.; Shi, R. S.; Shi, X.; Shi, X. D.; Song, J. J.; Song, Q. Q.; Song, Y. X.; Sosio, S.; Spataro, S.; Sui, F. F.; Sun, G. X.; Sun, J. F.; Sun, L.; Sun, S. S.; Sun, T.; Sun, W. Y.; Sun, Y. J.; Sun, Y. K.; Sun, Y. Z.; Sun, Z. T.; Tan, Y. X.; Tang, C. J.; Tang, G. Y.; Thoren, V.; Tsednee, B.; Uman, I.; Wang, B.; Wang, B. L.; Wang, C. W.; Wang, D. Y.; Wang, H. P.; Wang, K.; Wang, L. L.; Wang, M.; Wang, M. Z.; Wang, Meng; Wang, W. P.; Wang, X.; Wang, X. F.; Wang, X. L.; Wang, Y.; Wang, Y.; Wang, Y. D.; Wang, Y. F.; Wang, Y. Q.; Wang, Z.; Wang, Z. Y.; Wang, Ziyi; Wang, Zongyuan; Weber, T.; Wei, D. H.; Weidenkaff, P.; Weidner, F.; Wen, H. W.; Wen, S. P.; White, D. J.; Wiedner, U.; Wilkinson, G.; Wolke, M.; Wollenberg, L.; Wu, J. F.; Wu, L. H.; Wu, L. J.; Wu, Z.; Xia, L.; Xiao, S. Y.; Xiao, Y. J.; Xiao, Z. J.; Xie, Y. G.; Xie, Y. H.; Xing, T. Y.; Xiong, X. A.; Xu, G. F.; Xu, J. J.; Xu, Q. J.; Xu, W.; Xu, X. P.; Yan, L.; Yan, W. B.; Yan, W. C.; Yang, H. J.; Yang, H. X.; Yang, L.; Yang, R. X.; Yang, S. L.; Yang, Y. H.; Yang, Y. X.; Yang, Yifan; Yang, Zhi; Ye, M.; Ye, M. H.; Yin, J. H.; You, Z. Y.; Yu, B. X.; Yu, C. X.; Yu, G.; Yu, J. S.; Yu, T.; Yuan, C. Z.; Yuan, W.; Yuan, X. Q.; Yuan, Y.; Yue, C. X.; Yuncu, A.; Zafar, A. A.; Zeng, Y.; Zhang, B. X.; Zhang, Guangyi; Zhang, H. H.; Zhang, H. Y.; Zhang, J. L.; Zhang, J. Q.; Zhang, J. W.; Zhang, J. Y.; Zhang, J. Z.; Zhang, Jianyu; Zhang, Jiawei; Zhang, L.; Zhang, Lei; Zhang, S.; Zhang, S. F.; Zhang, T. J.; Zhang, X. Y.; Zhang, Y.; Zhang, Y. H.; Zhang, Y. T.; Zhang, Yan; Zhang, Yao; Zhang, Yi; Zhang, Z. H.; Zhang, Z. Y.; Zhao, G.; Zhao, J.; Zhao, J. Y.; Zhao, J. Z.; Zhao, Lei; Zhao, Ling; Zhao, M. G.; Zhao, Q.; Zhao, S. J.; Zhao, Y. B.; Zhao, Z. G.; Zhemchugov, A.; Zheng, B.; Zheng, J. P.; Zheng, Y.; Zheng, Y. H.; Zhong, B.; Zhong, C.; Zhou, L. P.; Zhou, Q.; Zhou, X.; Zhou, X. K.; Zhou, X. R.; Zhu, A. N.; Zhu, J.; Zhu, K.; Zhu, K. J.; Zhu, S. H.; Zhu, W. J.; Zhu, X. L.; Zhu, Y. C.; Zhu, Z. A.; Zou, B. S.; Zou, J. H. (American Physical Society, 2023-05-15)We measure the B0 lifetime and flavor-oscillation frequency using B0→D(∗)-π+ decays collected by the Belle II experiment in asymmetric-energy e+e- collisions produced by the SuperKEKB collider operating at the ϒ(4S) resonance. We fit the decay-time distribution of signal decays, where the initial flavor is determined by identifying the flavor of the other B meson in the event. The results, based on 33000 signal decays reconstructed in a data sample corresponding to 190 fb-1, are τB0=(1.499±0.013±0.008) ps, Δmd=(0.516±0.008±0.005) ps-1, where the first uncertainties are statistical and the second are systematic. These results are consistent with the world-average values.
- New artificial fluoro-cofactor of hydride transfer with novel fluorescence assay for redox biocatalysisZhang, Lei; Yuan, Jun; Xu, Y.; Zhang, Y. H. Percival; Qian, Xuhong (Royal Society of Chemistry, 2016-01-01)A new artificial fluoro-cofactor was developed for the replacement of natural cofactors NAD(P), exhibiting a high hydride transfer ability. More importantly, we established a new and fast screening method for the evaluation of the properties of artificial cofactors based on the fluorescence assay and visible color change.
- Temperature Correction and Analysis of Pavement Skid Resistance Performance Based on RIOHTrack Full-Scale TrackWu, Jiangfeng; Wang, Xudong; Wang, Linbing; Zhang, Lei; Xiao, Qian; Yang, Hailu (MDPI, 2020-08-28)The pavement skid resistance performance index is one of the most important indexes to ensure driving safety. Based on the test data of RIOHTrack full-scale track, this paper analyzes the decay law of pavement skid resistance performance, including the sideway force coefficient (SFC), British pendulum number (BPN), mean texture depth measured by sand patch method (MTD) and sensor measured texture depth by laser method (SMTD), under different equivalent single axle load times (ESALs). The paper analyzes the influence of different methods and conditions on the different indicators and excavates the internal correlation of different pavement skid resistance performance indexes, aiming to improve the effectiveness and accuracy of pavement skid resistance performance detection. The shortcomings of the temperature correction method of BPN and SFC are verified, which cannot correct the skid resistance performance effectively in different temperatures. Based on the assumption that there is a sensitive range of temperature influence on skid resistance performance, a new temperature correction method of skid resistance performance index is proposed based on the Boltzmann model and equivalent temperature of the asphalt surface layer. It can truly reflect the decay law of skid resistance pavement performance. At the same time, the internal correlation between BPN and MTD indicators is analyzed. It is found that there is a linear growth law between two indexes whose correlation coefficient is 0.999, which provides a reference for the research of pavement skid resistance performance.
- We Are Going Places: Travel Poster ExhibitionZhang, Lei; Fralin, Scott (Virginia Tech, 2015-01-20)This exhibition showcases students’ work from ART 2575, Introduction to Graphic Design II. The class focused on a refinement of the students’ design skills and a mastery of design technology in creating stylized computer graphics and illustrations for specific visual communication needs. Throughout the semester, students in the class learned advanced techniques and skills with Adobe Creative Cloud’s core programs and used them in combination with their design skills and knowledge in a variety of illustrative designs. Posters in the exhibition were collected from the second class project: designing an illustrative travel poster. For that project, each student first chose a favorite place for the travel poster, real or imaginary. Then, they went through a design process of researching graphic styles, sketching, detailing, computer digitizing and coloring, and final printing. In terms of design tools, students mainly used Adobe Illustrator, InDesign, and Photoshop to create these posters. 2015/01/20 - 2015/03/01
- We Thought the Future Would be CoolerFralin, Scott; Grimes, Matt; Stettler Kleine, Marie; Zhang, Lei; Fallah, Navid; Philips, Amanda K.; Risha, Zachary; Kulak, Andrew; Mouchrek, Najla; Sharma, Manisha; Shih, Bono (Virginia Tech, 2016-04-26)Interactive installation in which students from a transdisciplinary collaboration commandeer the challenge of Virginia Tech’s university-wide Beyond Boundaries initiative. The exhibit explores Virginia Tech’s possible futures, but also questions who has a say in imagining them, and to what ends. The project results from STS 6614: Origins of Innovation, a seminar that challenges our beliefs about, and our participation in, innovation. 2016/04/26 - 2016/06/06