Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrated Dynamic Modeling for a Stewart Platform

This work is to study the integrated dynamic modeling for a Stewart platform from the control point of view. A general Stewart platform is composed of moving platform, fixed platform, connecting rod (actuator) and joint hinge. The kinematic inverse solution can accurately reflect the relationship between the connecting rod length and the position and orientation of the moving platform. As a highlight, this approach takes into account the moving platform's mechanical model and the actuator's electrical model respectively. We can apply the Newton-Euler method to the moving platform's force analysis to obtain the dynamic mechanical model. In this paper, the voice coil motor is chosen as the actuator due to its advantages of fast response, frequency bandwidth, and mature technology. According to the Jacobian matrix, which can describe the transmission of joints, we connect the actuator's electrical model to the mechanical model of the moving platform. In this way, the singularity of the parallel mechanism could be judged by the controllability of the integrated model. The integrated modeling not only establishes a direct connection between the control input and the task-space, but gives the way of singularity discrimination from the control point of view.
Composite Nanotools with Shape Memory Effect for Nanostructures Assembly

Samples of the composite nanotweezers with shape memory effect were prepared and tested. Proposed design of the nanotweezers made it possible to manipulate individual NbS3whiskers in the scanning electron microscopy. To study the transport properties of the charge density wave (CDW), from the array of NbS3filaments, structurally perfect crystals of tens of nanometers in width were chosen and transferred to a substrate with electrical conductors without introducing additional structural defects. On the fixed crystals of different length and width, the voltage-current (IV) curves were measured with an external high-frequency electromagnetic field. The manifestation of the Shapiro steps on IV curves is indicative of the high quality of the samples made with the help of the proposed nanotweezers with shape memory effect (SME).
A Watermark-Based In-Situ Access Control Model for Image Big Data

When large images are used for big data analysis, they impose new challenges in protecting image privacy. For example, a geographic image may consist of several sensitive areas or layers. When it is uploaded into servers, the image will be accessed by diverse subjects. Traditional access control methods regulate access privileges to a single image, and their access control strategies are stored in servers, which imposes two shortcomings: (1) fine-grained access control is not guaranteed for areas/layers in a single image that need to maintain secret for different roles; and (2) access control policies that are stored in servers suffers from multiple attacks (e.g., transferring attacks). In this paper, we propose a novel watermark-based access control model in which access control policies are associated with objects being accessed (called an in-situ model). The proposed model integrates access control policies as watermarks within images, without relying on the availability of servers or connecting networks. The access control for images is still maintained even though images are redistributed again to further subjects. Therefore, access control policies can be delivered together with the big data of images. Moreover, we propose a hierarchical key-role-area model for fine-grained encryption, especially for large size images such as geographic maps. The extensive analysis justifies the security and performance of the proposed model
Efficient Scheduling of Multiple Data Transfers in Mobile Applications

Over the last decade wireless data streaming has progressively become an important aspect of our life. However, the lack of appropriate mechanisms to handle wireless network disruptions and delays, causes mobile applications to frequently suffer from unstable wireless connectivity, which makes data streaming particularly challenging leading to unstable streaming rates, long delays and often network failures. This paper proposes a framework that utilizes performance monitoring, machine learning and scheduling techniques to effectively schedule data streaming for mobile applications. The framework monitors the current network conditions through a set of performance metrics and evaluates its availability using a machine learning approach. To meet real-time performance objectives, if the network is characterized eligible for streaming, we use a deadline-first scheduling order for the data streaming. Our experimental evaluation, using different network traffic scenarios, illustrates the performance and benefits of the approach proposed.
Phase 1 study of EGFR‐antisense DNA, cetuximab, and radiotherapy in head and neck cancer with preclinical correlatives

Cetuximab combined with radiation therapy (RT) is an evidence‐based treatment for locally advanced head and neck squamous cell carcinoma (HNSCC); however, locoregional failure remains the primary cause of cancer‐related death in this disease. Intratumoral injection of epidermal growth factor receptor (EGFR)‐antisense plasmid DNA (EGFR‐AS) is safe and has been associated with promising lesional responses in patients who have recurrent/metastatic HNSCC. For the current study, the authors investigated the antitumor effects of cetuximab and EGFR‐AS in preclinical HNSCC models and reported their phase 1 experience adding intratumoral EGFR‐AS to cetuximab RT.
Local Interpretable Model-agnostic Explanations of Bayesian Predictive Models via Kullback-Leibler Projections

We introduce a method, KL-LIME, for explaining predictions of Bayesian predictive models by projecting the information in the predictive distribution locally to a simpler, interpretable explanation model. The proposed approach combines the recent Local Interpretable Model-agnostic Explanations (LIME) method with ideas from Bayesian projection predictive variable selection methods. The information theoretic basis helps in navigating the trade-off between explanation fidelity and complexity. We demonstrate the method in explaining MNIST digit classifications made by a Bayesian deep convolutional neural network.
A First Principles Study of Blue Phosphorene as A Superior Media for Gas Sensor

By using the density functional theory, the first-principles calculations were used to research the gas sensitive properties of small gas molecules (H2S, SO2, NH3, H2O, O2) on the blue phosphorene monolayer. Parameters of adsorption energy, adsorption distance as well as charge transfer helped us find the optimum adsorption point from three adsorption sites: Center; P; Bridge. By the results of electronic density of states and electron localization function, we can make a conclusion that these five gas molecules are physically adsorbed on blue phosphorene. Besides, it is also clear that small gas molecules adsorption can tune the band gap and work function of blue phosphorene in different degrees. Our studies can provide a theoretical guidance for practical applications.
Performance Evaluation and Optimization of Multi-Dimensional Indexes in Hive

Apache Hive has been widely used for big data processing over large scale clusters by many companies. It provides a declarative query language called HiveQL. The efficiency of filtering out query-irrelevant data from HDFS closely affects the performance of query processing. This is especially true for multi-dimensional, high-selective, and few columns involving queries, which provides sufficient information to reduce the amount of bytes read. Indexing (Compact Index, Aggregate Index, Bitmap Index, DGFIndex, and the index in ORC file) and columnar storage (RCFile, ORC file, and Parquet) are powerful techniques to achieve this. However, it is not trivial to choosing a suitable index and columnar storage based on data and query features. In this paper, we compare the data filtering performance of the above indexes with different columnar storage formats by conducting comprehensive experiments using uniform and skew TPC-H data sets and various multi-dimensional queries, and suggest the best practices of improving multi-dimensional queries in Hive under different conditions.
A Novel Hole-Path and Carrier-Stored IGBT with Low Switching Loss and On State Voltage

A novel hole path and carrier stored insulated gate bipolar transistor (HP-CS-IGBT) is proposed and investigated. In our work, the hole path structure helps to reduce the turn off time in switch-state and the carrier stored structure helps to reduce the on-state voltage (VCE(sat)), respectively. When the turn-off loss (Eoff) is equal to 5 mJ/cm2, the on-state voltage of the 1200V/40A rated HP-CS-IGBT is as low as 1.46V, which has been reduced by 30% compared to the conventional IGBT. Moreover, HP-CS-IGB also overcomes the inherited weak relationship between the forward blocking capacity and on state voltage compared to the conventional carrier stored trench-gate bipolar transistor (CSTBT).
Robust $\mathbf{H}_{\infty}$ Control of Unmanned Underwater Vehicle in Vertical Plane Based on T-S Model and Parallel Distributed Compensation

Excellent depth control capability of UUV (Unmanned Underwater Vehicle) is a key factor for completing specific tasks. Considering the variance of UUV's speed and ocean current interference, depth and pitch control model of UUV are established based on T-S model. The $\mathrm{H}_{\infty}$ controller in vertical plane movement is designed applying the method of parallel distributed compensation (PDC). Aiming to meet the design requirements with the large pitch angle when UUV is submerging, the depth-pitch coordinated control method with variant depth is used to control the submergence of UUV. Simulation result shows that the control method can effectively control depth and pitch of UUV in given speed interval and has good dynamic performance. Moreover, the method can effectively decrease the pitch angel when UUV is submerging and has strong inhibitory ability on ocean current disturbance.
Broadband Millimeter-Wave Beamforming Components Augmented With AMC Packaging

For future millimeter-wave beamforming applications, the improved designs of crossover and 90° hybrid coupler are prototyped. The wideband response of 30% is achieved at the center frequency of 30 GHz. The planar-single-layered structures are developed by using microstrip line technology, which is further augmented with the separate layer of artificial magnetic conductor packaging. The aim is to minimize the spurious radiation caused by the bends and discontinuities in the beamforming networks. For simulation purpose, CST Microwave Studio is used, and designs are fabricated and measured. For the first prototype, the measured crossover insertion loss is about 0.5 dB with ±0.15 dB variation within the operating region. The isolated ports show a maximum level of −15 dB. For the second prototype of hybrid coupler, −3.7 dB is measured at transmitted (<inline-formula> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula>) and coupled ports (<inline-formula> <tex-math notation="LaTeX">$S_{31}$ </tex-math></inline-formula>) with the variation of ±0.25 dB as a magnitude imbalance between the output lines. The couplers’ isolation (<inline-formula> <tex-math notation="LaTeX">$S_{\mathbf {41}}$ </tex-math></inline-formula>) level is less than −15 dB. The through/coupled port phase difference (<inline-formula> <tex-math notation="LaTeX">$\angle S_{31}- \angle S_{21}$ </tex-math></inline-formula>) is 90° with ±3° variation in extreme cases within the frequency band of operation.
Design and Development of Real Time Bionic Hand Control Using EMG Signal

a large amount of study is carried out in the field of prosthetics to restore functionalities of lost organs. Bionic hand is one of those device which helps to replace the lost hand functionalities for the amputees. A lot of effort is put into to development of bionic hand which can able to mimic the action performed by normal hand. This paper provides an insight on development of real time control of bionic hand based on the collected Electromyography(EMG) signal from lower elbow amputee which able to perform hand opposition and re-position. This paper also provides an understanding of signal processing techniques required to classify the EMG signals for identifying required action to control bionic hand. The results indicate that root mean square and integrated absolute value for Feature extraction, k-Nearest Neighbor and Naive Bayesian Pattern Classification methods are chosen for feature classification EMG signals to control bionic hand. The developed algorithms are capable of producing accuracy up to 92-94%.
Sex Differences in Genetic Associations With Longevity

Key Points Question Are there sex differences in genetic associations with longevity? Findings In this case-control study of 2178 cases and 2299 controls who were Chinese with Han ethnicity, sex-specific genome-wide association study and sex-specific polygenic risk score analyses on longevity showed substantial and significant differences in genetic associations with longevity between men and women. Findings indicated that previously published genome-wide association studies on longevity identified some sex-independent genetic variants but missed sex-specific longevity loci and pathways. Meaning These novel findings contribute to filling the gaps in the research literature, and further investigations may substantially contribute to individualized health care and more effective and targeted health interventions for male and female elderly individuals.
Graph Analytics on Manycore Memory Systems

Graphs are ubiquitous, and graph analytics has been widely adopted in many big data applications such as social computation and natural language processing, as well as web-search and recommendation systems. Prior research focuses on processing large-scale graphs on distributed environments or a single multi-core machine with several terabytes of RAM. Increasing complex memory systems and on-chip interconnects are developed to mitigate the data movement bottlenecks in manycore processors such as Xeon Phi KNL CPU with heterogeneous memory, with up to 72 dual-core tiles. This paper presents a detailed study on the characteristics of manycore memory systems and their impact on the efficiency of graph analytics. Based on this paper, we introduce Ants, the first graph analytics platform on manycore memory systems. First, Ants differentially allocates graph data according to their access patterns and the behavior of heterogeneous memory. Second, to reduce excessive memory access and ease congestion on interconnects and memory controllers, Ants develops a fine-grained and effective task partitioning strategy for many cores. A detailed experiment on a 64 dual-core tile machine shows that Ants outperforms the state-of-the-art graph analytics platform-Ligra by up to 8.97X for real-world graphs.
Cascading State-Space Decomposition of Boolean Control Networks

In this paper, we study the cascading state-space decomposition problem for Boolean control networks by nested method. Firstly, based on the algebraic forms of BCNs, we give several algebraic criterions for the cascading state-space decomposable of BCNs. Secondly, we propose the concepts of multi-layer nested block matrix, and obtain two sufficient and necessary conditions for the cascading state-space decomposition of BCNs. Furthermore, a method is given to design controllers. Finally, an example is given to display the effectiveness of the method provided in this paper.
For Prostate MRI Segmentation: A Prior-shape-based Level Set Model Combined with Gradient and Regional Information

The contour extraction of prostate in magnetic resonance imaging (MRI) plays a significant role in clinical diagnosis and related medical research. We propose a level set model, which incorporates shape priors and the gradient information of the image into C-V model which was submitted by Chan T F and Vese L for prostate MRI segmentation, and can segment the prostate contour in MRI with high precision. In our model, prior shape information is used as the external force, and the regional information as well as gradient information is used as internal force to drive curve evolution. Then the evolution curve is modified with the information of the image itself under the constraint of prior shape. Due to the addition of local gradient information, this method can better deal with the situation when there only exists small gray-level difference between the background and target area of medical image. Meanwhile, the accuracy and robustness of segmentation are improved. Finally, the experimental results and quantitative analysis show that the level set algorithm with priori shape information can adapt to different forms of the prostate better, and the addition of gradient information avoids the situation that traditional CV model unable to process the images with intensity inhomogeneity, which demonstrate the potential of the approach proposed in the prostate MRI segmentation.
Allocation of Wireless Power Transfer System From Viewpoint of Optimal Control Problem for Autonomous Driving Electric Vehicles

This paper proposes a new approach for optimal allocation of wireless power transfer system (WPTSys) from a viewpoint of optimal control problem (OCP) for autonomous driving electric vehicles (EVs). These EVs are assumed to accurately follow a pre-determined speed profile. By transformation of the nonlinear optimization problem for optimal allocation of WPTSys to an OCP, well-known methods guaranteeing the global optimality of solution for the OCP are applied, such as Pontryagin’s maximum principle or dynamic programming. Therefore, the global optimal solution of the allocation problem of WPTSys can be obtained. In addition, we consider a practical situation of EV operation from a probability point of view, where many EVs are operated with different initial battery state-of-charge.
A Model of Bus-Metro Bi-level Weighted Network with Travel Efficiency

A model of Bus- Metro bi -level weighted network is constructed over travel efficiency of public transport, reflecting the characteristics of various traffic carriers and how they connects. The edge weight of network model will be got by computing travelling time costs between two adjacent stations. Taking the public traffic system of Nanjing city as an example, this paper empirically analyses travel efficiency under multi-modal transportation, which shows that bi-levcl spatial traffic model can provide a good choice of navigation path and better reflect travel condition under the real transportation network, compared to that of single traffic mode network. It further reveals the cooperation and independent relationship between Bus system and Metro system, reflecting spatio-temporal characteristics of urban traffic system.
Processor Free Time Forecasting Based on Convolutional Neural Network

For a complex multitask and multiple low power mode processor, to make the processor enter a suitable low power mode in free time to reduce the power consumption, we can forecast the free time of the processor by Back Propagation (BP) neural network algorithm. But as a local search method and having the disadvantage being sensitive to initial weights, BP neural network has the low forecasting accuracy. In this paper, one dimensional convolution neural network is chosen to establish the forecasting model of free time, and some improvements are made based on two-dimensional convolution neural network to improve the suitableness for forecasting one-dimensional array. The simulation experiment shows that the accuracy of the data predicted by one dimensional convolutional neural network is higher than that of the BP neural network.
Matrix Factorization for Collaborative Budget Allocation

This paper studies the collaborative budget allocation problem in which users are not isolated in the collaborative consumption of goods or services when available goods or services are limited. Different from existing methods that treat each user independently, we investigate the geometric properties of user’s consumption or preference on services, and design a matrix completion framework on the simplex. In this framework, an item’s allocation vector indicating how available services are allocated to users is estimated by the combination of user profiles as basis points on the simplex. Instead of using Euclidean distance directly, we specify a Riemannian distance on the simplex or project histogram data on simplex to Euclidean space. To intensify our model’s stability, we relax the exact recovery constraint to make a robust collaborative prediction. The resulting objective function is then efficiently optimized by a Riemannian conjugate gradient method on the simplex. Experiments on real-world data sets demonstrate our model’s competitiveness versus other collaborative budget prediction methods. Comparisons of different distance metrics for histogram data are shown and discussed. Note to Practitioners—This paper was motivated by the collaborative budget allocation (CBA) problem in which either a user has limited ratings that can be distributed among items, or a service has limited availability to all users in the sharing economy system. Existing approaches rarely consider this phenomenon in their collaborative prediction modeling, which implies the geometric constraints on a user’s rating vector. This paper suggests a matrix factorization framework to address this problem by learning a group of a user profile as basis points that can be combined to recover other users’ rating vectors. Considering the noise in observed ratings in real applications, we enhance the robustness of collaborative prediction by relaxing the recovery constraint. We then propose to specify the distance metric on the simplex from two perspectives, and give the optimization approach. In the future research, we will address the CBA in more complex settings, for example, some items can be substitutes or complementary for each other, in this way, how items can be better distributed among users is an interesting problem.
The Art and Science of Transformer Ratio Measurement

The principle which allows one to measure voltage ratio and convert this reading to transformer turns ratio sounds very simple. In practice, however, the conversion is influenced by practical issues, such as cores whose permeability and loss changes with applied voltage and causes conversion errors. This is especially troublesome when testing three-phase transformers using single-phase excitation. Three-phase transformers use cores more efficiently than do single-phase transformers, thus the loss and permeability effects are reduced, provided three-phase excitation is employed. The advantages of using three-phase excitation for ratio measurement of three-phase transformers include: * reduced dependence on test voltage. * ability to identify transformer's configuration. * ability to measure directly the ratio and phase shift of phase shifting transformers. * ability to provide additional information regarding the balance of the phases, shorted turns and excessive core loss.