arXiv subjects

Xue Bai

Publications and source records attributed to Xue Bai.

33 records · Page 2Linked to original sources

arXiv2023

Danlu Tongdu tablets treat lumbar spinal stenosis through reducing reactive oxygen species and apoptosis by regulating CDK2/CDK4/CDKN1A expression

Lumbar spinal stenosis (LSS) is caused by the compression of the nerve root or cauda equina nerve by stenosis of the lumbar spinal canal or intervertebral foramen, and is manifested as chronic low back and leg pain. Danlu Tongdu (DLTD) tablets can relieve chronic pain caused by LSS, but the molecular mechanism remains largely unknown. In this study, the potential molecular mechanism of DLTD tablets in the treatment of LSS was firstly predicted by network pharmacology method. Results showed that DLTD functions in regulating anti-oxidative, apoptosis, and inflammation signaling pathways. Furthermore, the flow cytometry results showed that DLTD tablets efficiently reduced ROS content and inhibited rat neural stem cell apoptosis induced by hydrogen peroxide. DLTD also inhibited the mitochondrial membrane potential damage induced by hydrogen peroxide. Elisa analysis showed that DLTD induced cell cycle related protein, CDK2 and CDK4 and reduced CDKN1A protein expression level. Taken together, our study provided new insights of DLTD in treating LSS through reducing ROS content, decreasing apoptosis by inhibiting CDKN1A and promoting CDK2 and CDK4 expression levels.

q-bio.QM↗

arXiv2020

A Two-Stage Approach to Device-Robust Acoustic Scene Classification

To improve device robustness, a highly desirable key feature of a competitive data-driven acoustic scene classification (ASC) system, a novel two-stage system based on fully convolutional neural networks (CNNs) is proposed. Our two-stage system leverages on an ad-hoc score combination based on two CNN classifiers: (i) the first CNN classifies acoustic inputs into one of three broad classes, and (ii) the second CNN classifies the same inputs into one of ten finer-grained classes. Three different CNN architectures are explored to implement the two-stage classifiers, and a frequency sub-sampling scheme is investigated. Moreover, novel data augmentation schemes for ASC are also investigated. Evaluated on DCASE 2020 Task 1a, our results show that the proposed ASC system attains a state-of-the-art accuracy on the development set, where our best system, a two-stage fusion of CNN ensembles, delivers a 81.9% average accuracy among multi-device test data, and it obtains a significant improvement on unseen devices. Finally, neural saliency analysis with class activation mapping (CAM) gives new insights on the patterns learnt by our models.

cs.SD↗

arXiv2020

Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data Augmentation

In this technical report, we present a joint effort of four groups, namely GT, USTC, Tencent, and UKE, to tackle Task 1 - Acoustic Scene Classification (ASC) in the DCASE 2020 Challenge. Task 1 comprises two different sub-tasks: (i) Task 1a focuses on ASC of audio signals recorded with multiple (real and simulated) devices into ten different fine-grained classes, and (ii) Task 1b concerns with classification of data into three higher-level classes using low-complexity solutions. For Task 1a, we propose a novel two-stage ASC system leveraging upon ad-hoc score combination of two convolutional neural networks (CNNs), classifying the acoustic input according to three classes, and then ten classes, respectively. Four different CNN-based architectures are explored to implement the two-stage classifiers, and several data augmentation techniques are also investigated. For Task 1b, we leverage upon a quantization method to reduce the complexity of two of our top-accuracy three-classes CNN-based architectures. On Task 1a development data set, an ASC accuracy of 76.9\% is attained using our best single classifier and data augmentation. An accuracy of 81.9\% is then attained by a final model fusion of our two-stage ASC classifiers. On Task 1b development data set, we achieve an accuracy of 96.7\% with a model size smaller than 500KB. Code is available: https://github.com/MihawkHu/DCASE2020_task1.

eess.AS↗

arXiv2020

An Acoustic Segment Model Based Segment Unit Selection Approach to Acoustic Scene Classification with Partial Utterances

In this paper, we propose a sub-utterance unit selection framework to remove acoustic segments in audio recordings that carry little information for acoustic scene classification (ASC). Our approach is built upon a universal set of acoustic segment units covering the overall acoustic scene space. First, those units are modeled with acoustic segment models (ASMs) used to tokenize acoustic scene utterances into sequences of acoustic segment units. Next, paralleling the idea of stop words in information retrieval, stop ASMs are automatically detected. Finally, acoustic segments associated with the stop ASMs are blocked, because of their low indexing power in retrieval of most acoustic scenes. In contrast to building scene models with whole utterances, the ASM-removed sub-utterances, i.e., acoustic utterances without stop acoustic segments, are then used as inputs to the AlexNet-L back-end for final classification. On the DCASE 2018 dataset, scene classification accuracy increases from 68%, with whole utterances, to 72.1%, with segment selection. This represents a competitive accuracy without any data augmentation, and/or ensemble strategy. Moreover, our approach compares favourably to AlexNet-L with attention.

eess.AS↗

arXiv2020

Wettability and surface energy of parylene F

Parylenes are barrier materials employed as protective layers. However, many parylenes are unsuitable for applications under harsh conditions. A new material, parylene F, demonstrates considerable potential for a wide range of applications due to its high temperature and UV resistance. For the first time, the wettability and surface energy of parylene F were investigated to determine the feasibility of parylene F as an alternative to the commonly employed parylene C. The results show that parylene F has a hydrophobic surface with a water contact angle of 109.63 degrees. We found that 3.5 ul probe liquid is an optimal value for the contact angle measurement of parylene F. Moreover, we found that the Owens-Wendt-Kaelble and the Lifshitz-van der Waals/acid-base approaches are unsuitable for determining the surface energy of parylene F, whereas an approach based on the limitless liquid-solid interface wetting system is compatible. Furthermore, the results show that parylene F has a surface energy of 39.05 mJ/m2. Considering the improved resistance, relatively low cost, and the desirable properties, parylene F can replace parylene C for applications under harsh conditions.

physics.chem-ph↗

arXiv2019

About how to make novel Visible by using Newly Translated Tale of Genji as an example

This paper aims to make Tales of Genji visible by using natural language processing, mathematic analysis, emiton analysis. Based on novel, mining data from content of this novel at respect of information abstracting. Summing up the fundamental method of novel visualization, our work are as follows: Based on frequency analysis, we use tf-did to abstract keyword of Newly Translated Tale of Genji, which means the most important word in each chapter. We recognize the emotion of word to analysis the emotion of each chapter of Newly Translated Tale of Genji. Next, we think about the connection between the result of emotion analysis and literature analysis, showing we can get same result by natural language processing. We build a network of all the word apperanced in Newly Translated Tale of Genji. Make a study of relationships between words. Further, we search the writer of Uji Chapters.

cs.HC↗

arXiv2018

A Co-evolution Model of Network Structure and User Behavior in Online Social Networks: The Case of Network-Driven Content Generation

With the rapid growth of online social network sites (SNS), it has become imperative for platform owners and online marketers to investigate what drives content production on these platforms. However, previous research has found it difficult to statistically model these factors from observational data due to the inability to separately assess the effects of network formation and network influence. In this paper, we adopt and enhance an actor-oriented continuous-time model to jointly estimate the co-evolution of the users' social network structure and their content production behavior using a Markov Chain Monte Carlo (MCMC)- based simulation approach. Specifically, we offer a method to analyze non-stationary and continuous behavior with network effects in the presence of observable and unobservable covariates, similar to what is observed in social media ecosystems. Leveraging a unique dataset from a large social network site, we apply our model to data on university students across six months to find that: 1) users tend to connect with others that have similar posting behavior, 2) however, after doing so, users tend to diverge in posting behavior, and 3) peer influences are sensitive to the strength of the posting behavior. Further, our method provides researchers and practitioners with a statistically rigorous approach to analyze network effects in observational data. These results provide insights and recommendations for SNS platforms to sustain an active and viable community.

cs.SI↗

arXiv2018

A multifeature fusion approach for power system transient stability assessment using PMU data

Taking full advantage of synchrophasors provided by GPS-based wide-area measurement system (WAMS), a novel VBpMKL-based transient stability assessment (TSA) method through multifeature fusion is proposed in this paper. First, a group of classification features reflecting the transient stability characteristics of power systems are extracted from synchrophasors, and according to the different stages of the disturbance process they are broken into three nonoverlapped subsets; then a VBpMKL-based TSA model is built using multifeature fusion through combining feature spaces corresponding to each feature subset; and finally application of the proposed model to the IEEE 39-bus system and a real-world power system is demonstrated. The novelty of the proposed approach is that it improves the classification accuracy and reliability of TSA using multifeature fusion with synchrophasors. The application results on the test systems verify the effectiveness of the proposal.

eess.SP↗

arXiv2018

Measuring the thermal conductivity and interfacial thermal resistance of suspended MoS2 using electron beam self-heating technique

Establishment of a new technique or extension of an existing technique for thermal and thermoelectric measurements to a more challenging system is an important task to explore the thermal and thermoelectric properties of various materials and systems. The bottleneck lies in the challenges in measuring the thermal contact resistance. In this work, we applied electron beam self-heating technique to derive the intrinsic thermal conductivity of suspended Molybdenum Disulfide (MoS2) ribbons and the thermal contact resistance, with which the interfacial thermal resistance between few-layer MoS2 and Pt electrodes was calculated. The measured room temperature thermal conductivity of MoS2 is around 30 W/mK, while the estimated interfacial thermal resistance is around 2*10-6 m2K/W. Our experiments extend a useful branch in application of this technique for studying thermal properties of suspended layered ribbons and have potential application in investigating the interfacial thermal resistance of different 2D heterojunctions.

cond-mat.mes-hall↗

arXiv2016

Utility-based Link Recommendation for Online Social Networks

Link recommendation, which suggests links to connect currently unlinked users, is a key functionality offered by major online social networks. Salient examples of link recommendation include "People You May Know" on Facebook and LinkedIn as well as "You May Know" on Google+. The main stakeholders of an online social network include users (e.g., Facebook users) who use the network to socialize with other users and an operator (e.g., Facebook Inc.) that establishes and operates the network for its own benefit (e.g., revenue). Existing link recommendation methods recommend links that are likely to be established by users but overlook the benefit a recommended link could bring to an operator. To address this gap, we define the utility of recommending a link and formulate a new research problem - the utility-based link recommendation problem. We then propose a novel utility-based link recommendation method that recommends links based on the value, cost, and linkage likelihood of a link, in contrast to existing link recommendation methods which focus solely on linkage likelihood. Specifically, our method models the dependency relationship between value, cost, linkage likelihood and utility-based link recommendation decision using a Bayesian network, predicts the probability of recommending a link with the Bayesian network, and recommends links with the highest probabilities. Using data obtained from a major U.S. online social network, we demonstrate significant performance improvement achieved by our method compared to prevalent link recommendation methods from representative prior research.

cs.SI↗

arXiv2015

Invisible sensor: Simultaneous sensing and camouflaging in multiphysical fields

To manipulate various types of physical signals in one single device has long captivated the attention of scientists and engineers. This however is very challenging, if not impossible, even for emerging metamaterials. Up to date, many artificial materials have been proposed, theoretically and (or) experimentally, for manipulating various waves/signals on a one-function-one-device basis. In this work, for the very first time, we employ undecorated natural materials to experimentally demonstrate a simultaneous camouflage for thermal current and electric dc current on the same device. It demonstrates how ingenuity can overcome the limitations of natural material systems without the need for complex decoration to impart inhomogeneous and (or) anisotropic properties, which was previously considered impossible to accomplish except by using metamaterials.

physics.optics↗

arXiv2014

Manipulating Steady Heat Conduction by Sensu-shaped Thermal Metamaterials

The ability to design the control of heat flow has innumerable benefits in the design of electronic systems such as thermoelectric energy harvesters, solid-state lighting, and thermal imagers, where the thermal design plays a key role in performance and device reliability. However, to realize one advanced control function of thermal flux, one needs to design one sophisticated, multilayered and inhomogeneous thermal structure with different composition/shape at different regions of one device. In this work, we employ one identical sensu-unit with facile natural composition to experimentally realize a new class of thermal metamaterials for controlling thermal conduction (e.g., thermal concentrator, focusing/resolving, uniform heating), only resorting to positioning and locating the same unit element of sensu-shape structure. The thermal metamaterial unit and the proper arrangement of multiple identical units are capable of transferring, redistributing and managing thermal energy in a versatile fashion. It is also shown that our sensu-shape unit elements can be used in manipulating dc currents without any change in the layout for the thermal counterpart. The proposed scheme can also be applied to control dc electric currents and dc magnetic fields that governed by Laplace equation. These could markedly enhance the capabilities in thermal sensing, thermal imaging, thermal-energy storage, thermal packaging, thermal therapy, and more domains beyond.

physics.optics↗

arXiv2014

Invisibility's Flicker: Detecting Thermal Cloaks via Transient Effects

Recent research on the development of a thermal cloak has concentrated on engineering an inhomogeneous thermal conductivity and homogeneous volumetric heat capacity. While the perfect cloak of inhomogeneous $κ$ and $ρc_p$ is known to be exact (no signals scattering or penetrating to the cloak's interior), no such analysis has been considered for this case. Using analytic, computational, and experimental techniques, we demonstrate that these approximate cloaks are detectable. Although they work as perfect cloaks in the steady-state, their transient (time-dependent) response is imperfect and a detectable amount of heat is scattered. This is sufficient to determine the presence of a cloak and any heat source it contains, but the material composition hidden within the cloak is not detectable in practice.

cond-mat.mtrl-sci↗

arXiv2014

Robust Linear Regression: A Review and Comparison

Ordinary least-squares (OLS) estimators for a linear model are very sensitive to unusual values in the design space or outliers among y values. Even one single atypical value may have a large effect on the parameter estimates. This article aims to review and describe some available and popular robust techniques, including some recent developed ones, and compare them in terms of breakdown point and efficiency. In addition, we also use a simulation study and a real data application to compare the performance of existing robust methods under different scenarios.

stat.ME↗

arXiv2013

Bilayer Isotropic Thermal Cloak

Invisibility has attracted intensive research in various communities, e.g., optics, electromagnetics, acoustics, thermodynamics, etc. However, the most of them have only been experimentally achieved by virtue of simplified approaches due to their inhomogeneous and extreme parameters imposed by transformation-optic method, and usually require challenging realization with metamaterials. In this paper, we demonstrate an advanced bilayer thermal cloak with naturally available materials first time. This scheme, directly from thermal conduction equation, has been validated as an exact cloak rather than a reduced one, and we experimentally confirmed its perfect performance (heat-front maintenance and heat protection) in an actual setup. The proposed scheme may open a new avenue to control the diffusive heat flow in ways inconceivable with phonons.

cond-mat.mtrl-sci↗