Miracle-dz/UniFAR-FTUs huggingface dataset:
Gold Nanoparticles as Effective ion Traps in Poly(dimethylsiloxane) Cross-Linked by Metal-Ligand Coordination

At this time, the development of advanced elastic dielectric materials for use in organic devices, particularly in organic field-effect transistors, is of considerable interest to the scientific community. In the present work, flexible poly(dimethylsiloxane) (PDMS) specimens cross-linked by means of ZnCl2-bipyridine coordination with an addition of 0.001 wt. %, 0.0025 wt. %, 0.005 wt. %, 0.04 wt. %, 0.2 wt. %, and 0.4 wt. % of gold nanoparticles (AuNPs) were prepared in order to understand the effect of AuNPs on the electrical properties of the composite materials formed. The broadband dielectric spectroscopy measurements revealed one order of magnitude decrease in loss tangent, compared to the coordinated system, upon an introduction of 0.001 wt. % of AuNPs into the polymeric matrix. An introduction of AuNPs causes damping of conductivity within the low-temperature range investigated. These effects can be explained as a result of trapping the Cl− counter ions by the nanoparticles. The study has shown that even a very low concentration of AuNPs (0.001 wt. %) still brings about effective trapping of Cl− counter anions, therefore improving the dielectric properties of the investigated systems. The modification proposed reveals new perspectives for using AuNPs in polymers cross-linked by metal-ligand coordination systems.
The Human Neck is Part of the Musculoskeletal Core: Cervical Muscles Help Stabilize the Pelvis During Running and Jumping

Synopsis During locomotion, cervical muscles must be active to stabilize the head as the body accelerates and decelerates. We hypothesized that cervical muscles are also part of the linked chain of axial muscles that provide core stabilization against torques applied to the hip joint by the extrinsic muscles of the legs. To test whether specific cervical muscles play a role in postural stabilization of the head and/or core stabilization of the pelvic girdle, we used surface electromyography to measure changes in muscle activity in response to force manipulations during constant speed running and maximum effort counter-movement jumps. We found that doubling the mass of the head during both running and maximum effort jumping had little or no effect on (1) acceleration of the body and (2) cervical muscle activity. Application of horizontal forward and rearward directed forces at the pelvis during running tripled mean fore and aft accelerations, thereby increasing both the pitching moments on the head and flexion and extension torques applied to the hip. These manipulations primarily resulted in increases in cervical muscle activity that is appropriate for core stabilization of the pelvis. Additionally, when subjects jumped maximally with an applied downward directed force that reduced acceleration and therefore need for cervical muscles to stabilize the head, cervical muscle activity did not decrease. These results suggest that during locomotion, rather than acting to stabilize the head against the effects of inertia, the superficial muscles of the neck monitored in this study help to stabilize the pelvis against torques imposed by the extrinsic muscles of the legs at the hip joint. We suggest that a division of labor may exist between deep cervical muscles that presumably provide postural stabilization of the head versus superficial cervical muscles that provide core stabilization against torques applied to the pelvic and pectoral girdles by the extrinsic appendicular muscles.
Characterization and Modeling of Bond-Loss Behavior in Pretensioned Concrete I-Girders

Failures of strand-concrete bond have been widely observed in load tests of precast-pretensioned concrete I-girders. Through a review of 22 different test programs, fifteen different terminologies were identified to describe failures associated with bond loss. In many cases, previous researchers used different terms to describe the same failure behavior. In response to the wide ranging and sometimes inconsistent terminologies used in the literature, this technical note makes two contributions. First, the different types of failure involving strand-concrete bond loss are characterized and a consistent labeling scheme is proposed. The fifteen different labels given in the referenced test programs are condensed into four primary behaviors. Second, a flowchart is presented for assisting future researchers in characterizing and labeling bond-loss failures. Decision points in the flowchart are based on a synthesis of the reviewed test programs, which included 120 unique load tests having some type of bond-loss failure.
Brain tumor classification by using a novel convolutional neural network structure

Brain tumors located in the skull are among the health problems that cause serious consequences. Rapid and accurate detection of brain tumor types will ensure that the patient receives appropriate treatment in the early period, thus increasing the patient's chance of recovery and survival. In the literature, classification accuracies over 98% have been acquired automatically by using deep neural networks (DNN) for the brain tumor images such as glioma, meningioma, and pituitary. It is observed that researchers generally focused on achieving higher classification accuracy and therefore, they have used pre‐processing stages, augmentation processes, huge or hybrid DNN structures. These approaches have brought some disadvantages in terms of practical use of the developed methods: (i)The parameters of the pre‐processes should be carefully determined, otherwise the classification accuracy will decrease. (ii) In order to increase the classification performance, it is important to determine the coarse structure of the DNN correctly. If the DNN has many hyper‐parameters, the coarse structure will be determined in a long time. (iii) It is difficult to implement complex DNN structures or training algorithms in terms of practical use, because these methods need huge memory and high CPU computation. In this study, we have proposed a novel DNN model to increase the classification accuracy, and to decrease the number of weights in the structure, and to use less number of hyper‐parameters. We named this model, which uses a divergence‐based feature extractor, as DivFE‐v1 for short. 99.18% classification accuracy for the Figshare dataset is obtained by using the small‐sized DNN structure without any pre‐processing stage or augmentation process.
An Effective Approach to Detect and Identify Brain Tumors Using Transfer Learning

Brain tumors are considered one of the most serious, prominent and life-threatening diseases globally. Brain tumors cause thousands of deaths every year around the globe because of the rapid growth of tumor cells. Therefore, timely analysis and automatic detection of brain tumors are required to save the lives of thousands of people around the globe. Recently, deep transfer learning (TL) approaches are most widely used to detect and classify the three most prominent types of brain tumors, i.e., glioma, meningioma and pituitary. For this purpose, we employ state-of-the-art pre-trained TL techniques to identify and detect glioma, meningioma and pituitary brain tumors. The aim is to identify the performance of nine pre-trained TL classifiers, i.e., Inceptionresnetv2, Inceptionv3, Xception, Resnet18, Resnet50, Resnet101, Shufflenet, Densenet201 and Mobilenetv2, by automatically identifying and detecting brain tumors using a fine-grained classification approach. For this, the TL algorithms are evaluated on a baseline brain tumor classification (MRI) dataset, which is freely available on Kaggle. Additionally, all deep learning (DL) models are fine-tuned with their default values. The fine-grained classification experiment demonstrates that the inceptionresnetv2 TL algorithm performs better and achieves the highest accuracy in detecting and classifying glioma, meningioma and pituitary brain tumors, and hence it can be classified as the best classification algorithm. We achieve 98.91% accuracy, 98.28% precision, 99.75% recall and 99% F-measure values with the inceptionresnetv2 TL algorithm, which out-performs the other DL algorithms. Additionally, to ensure and validate the performance of TL classifiers, we compare the efficacy of the inceptionresnetv2 TL algorithm with hybrid approaches, in which we use convolutional neural networks (CNN) for deep feature extraction and a Support Vector Machine (SVM) for classification. Similarly, the experiment’s results show that TL algorithms, and inceptionresnetv2 in particular, out-perform the state-of-the-art DL algorithms in classifying brain MRI images into glioma, meningioma, and pituitary. The hybrid DL approaches used in the experiments are Mobilnetv2, Densenet201, Squeeznet, Alexnet, Googlenet, Inceptionv3, Resnet50, Resnet18, Resnet101, Xception, Inceptionresnetv3, VGG19 and Shufflenet.
Cyberphysical Security of Grid Battery Energy Storage Systems

This paper presents a literature review on current practices and trends on cyberphysical security of grid-connected battery energy storage systems (BESSs). Energy storage is critical to the operation of Smart Grids powered by intermittent renewable energy resources. To achieve this goal, utility-scale and consumer-scale BESS will have to be fully integrated into power systems operations, providing ancillary services and performing functions to improve grid reliability, balance power and demand, among others. This vision of the future power grid will only become a reality if BESS are able to operate in a coordinated way with other grid entities, thus requiring significant communication capabilities. The pervasive networking infrastructure necessary to fully leverage the potential of storage increases the attack surface for cyberthreats, and the unique characteristics of battery systems pose challenges for cyberphysical security. This paper discusses a number of such threats, their associated attack vectors, detection methods, protective measures, research gaps in the literature and future research trends.
COPD_A_359428 1285..1298

1College of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, People’s Republic of China; 2Henan Key Laboratory of Chinese Medicine for Respiratory Disease, Henan University of Chinese Medicine, Zhengzhou, People’s Republic of China; 3Institute for Respiratory Diseases, The First Affiliated Hospital, Henan University of Chinese Medicine, Zhengzhou, People’s Republic of China; 4Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Disease by Henan & Education Ministry of P.R. China, Henan University of Chinese Medicine, Zhengzhou, People’s Republic of China
Defuse: Training More Robust Models through Creation and Correction of Novel Model Errors

We typically compute aggregate statistics on held-out test data to assess the generalization of machine learning models. However, test data is only so comprehensive, and in practice, important cases are often missed. Thus, the performance of deployed machine learning models can be variable and untrustworthy. Motivated by these concerns, we develop methods to generate and correct novel model errors beyond those available in the data. We propose Defuse: a technique that trains a generative model on a classiﬁer’s training dataset and then uses the latent space to generate new samples which are no longer correctly predicted by the classiﬁer. For instance, given a classiﬁer trained on the MNIST dataset that correctly predicts a test image, Defuse then uses this image to generate new similar images by sampling from the latent space. Defuse then identiﬁes the images that differ from the label of the original test input. Defuse enables efﬁcient labeling of these new images, allowing users to re-train a more robust model, thus improving overall model performance. We evaluate the performance of Defuse on classiﬁers trained on real world datasets and ﬁnd it reveals novel sources of model errors.
This is in the Proceedings of the 16th USENIX Symposium on Operating Systems Design and Implementation. Demystifying and Checking Silent Semantic Violations in Large Distributed Systems

Distributed systems today offer rich features with numerous semantics that users depend on. Bugs can cause a system to silently violate its semantics without apparent anomalies. Such silent violations cause prolonged damage and are difﬁcult to address. Yet, this problem is under-investigated. In this paper, we ﬁrst study 109 real-world silent semantic failures from nine widely-used distributed systems to shed some light on this difﬁcult problem. Our study reveals more than a dozen informative ﬁndings. For example, it shows that surprisingly the majority of the studied failures were violating semantics that existed since the system’s ﬁrst stable release. Guided by insights from our study, we design Oathkeeper, a tool that automatically infers semantic rules from past failures and enforces the rules at runtime to detect new failures. Evaluation shows that the inferred rules detect newer violations, and Oathkeeper only incurs 1.27% overhead.
of a network formation game with an

This paper studies an endogenous network formation game based on well-known Con-nections Model ﬁrst introduced by Jackson and Wolinsky (1996). We assume that the cost to construct a link can be shared between two players on the both ends through a bilateral negotiation process. Although this game always has a Nash equilibrium in which the un-deriying network is eﬃcient, there also exist equilibria with ineﬃcient networks. To solve this multiplicity of equilibria, we introduce a stronger equilibrium with a special type of symmetry, that we call indiscriminate Strong Nash Equilibrium (ISNE). It is shown that the ISNE necessarily achieves eﬃcient netw orks and attains the core of the associated cooperative game.
LAWA: Locality Aware Partitioning for Efficient Query Answering Over RDF Data

The explosion of the web and the abundance of linked data, demand for effective and efficient methods for storage, management and querying. Apache Spark is one of the most active big-data approaches, with more and more systems adopting it, for efficient querying over distributed data. However, most of the Spark-based RDF query answering approaches so far, are exploiting simplistic horizontal and/or vertical partitioning of triples, resulting in poor query performance. The main reason for this is that simplistic data partitioning approaches fail to identify data locality and group together data that are usually queried together. To mitigate this problem, in this thesis, we present LAWA, a novel platform that accepts as input an RDF dataset and effectively partitions data, ensuring data locality. To achieve this, we identify the dataset’s important nodes as centroids and then we distribute the other nodes to the centroid they mostly depend on. This scheme ensures data locality, and in most cases results in a balanced data distribution, however, without offering any guarantees on it. In order to study the design choices and isolate the impact of data locality and data distribution we introduce two variants. One focusing purely only on data locality, i.e. the Locality Aware Partitioning (LAP) approach and another one enforcing balanced data distribution as well, i.e. the Bounded Locality Aware Partitioning (BLAP) approach. We show that out approach offers an optimal fine tuning between data distribution, replication and data reduction, dominating existing approaches. More specifically we evaluate our approach using both synthetic and real workloads, showing that we improve query answering orders of magnitude over existing state of the art. Κατακερματισμός δεδομένων RDF με σκοπό την αποτελεσματική απάντηση επερωτήσεων εκμεταλλευόμενοι την τοπικότητα των δεδομένων.
A Long Short-Term Memory Network for Plasma Diagnosis from Langmuir Probe Data

Electrostatic probe diagnosis is the main method of plasma diagnosis. However, the traditional diagnosis theory is affected by many factors, and it is difficult to obtain accurate diagnosis results. In this study, a long short-term memory (LSTM) approach is used for plasma probe diagnosis to derive electron density (Ne) and temperature (Te) more accurately and quickly. The LSTM network uses the data collected by Langmuir probes as input to eliminate the influence of the discharge device on the diagnosis that can be applied to a variety of discharge environments and even space ionospheric diagnosis. In the high-vacuum gas discharge environment, the Langmuir probe is used to obtain current–voltage (I–V) characteristic curves under different Ne and Te. A part of the data input network is selected for training, the other part of the data is used as the test set to test the network, and the parameters are adjusted to make the network obtain better prediction results. Two indexes, namely, mean squared error (MSE) and mean absolute percentage error (MAPE), are evaluated to calculate the prediction accuracy. The results show that using LSTM to diagnose plasma can reduce the impact of probe surface contamination on the traditional diagnosis methods and can accurately diagnose the underdense plasma. In addition, compared with Te, the Ne diagnosis result output by LSTM is more accurate.
Errata Sheet to “Physics-based Vision Meets Deep Learning”

The section 2.1. 1 is very high level. What is in OpenDR? What can it do? How is minimisation of an appearance error different from training? Correction: The paragraph of section 2.1. 1 should be rewritten as shown below: In energy minimisation approaches to inverse graphics problems, there is a need for differentiable renderers that model the physical process of image intensity formation. This allows minimisation of an appearance error using gradient descent or other first-order optimisation algorithms. Loper et al. [13] developed a differentiable renderer package called OpenDR as a framework. OpenDR could reproduce the observed images with latent variables like appearance, geometry and camera, then the error between the reproduced image intensity map and the observation can be minimised. This differentiable renderer allows us to perform image rendering with respect to per-vertex brightness, vertex-based geometry and pinhole-plus-distortion camera, and compute partial derivatives of the rendered image with respect to these image formation parameters. Hence we can estimate these image formation parameters from an observed image through an analysis-by-synthesis optimisation, which is achieved by minimising the error between the observed image and the reproduced image rendered from the estimated model parameters. Minimising the error of the rendered image from this differentiable renderer facilitates the model parameter estimation from an input image, or it could be integrated into an inverse graphics neural network to train the network parameters. Zienkiewicz et al. [21] applied a similar idea for estimating height maps in a real-time robotic system. The rendering objective isheight map rather than RGB intensities in their case.
Supplemental Material of Action-guided 3D Human Motion Prediction

A More experimental evaluation In this section, we provide more experimental evaluation on our approach. In Figure 1, we present visualization results of the predicted 3D human mesh from Human3.6M dataset [1]. We can observe that our approach can better handle tiny cues of motion dynamics. Table 1 and Table 2 provide additional ablation results for our approach on the Penn Action dataset [6] when DTW is not applied. Detailed setting of these experiments can be found in Section 4.2 of the main paper.
Optimization of multiple dynamic vibration absorbers for reduction of low frequency vibration of joist floor structures

A dynamic vibration absorber (DVA) can be used to reduce the vibration of a joist floor structure. This is achieved by tuning the natural frequency of the DVA to match the frequency of the host structure, as a large amount of structural vibration energy can be transferred to the DVA and is then dissipated by the DVA’s damping. The parameters mass, damping, stiffness, and location, need to be tuned for an optimal vibration control. In this paper, an optimization procedure for the design parameters of multiple dynamic vibration absorbers (DVAs) is proposed to reduce the low-frequency vibration of joist floor structures. First, the vibration of the joist floor structure is calculated by an analytical model combining the motion of a plate and beams. The absorbers are then modeled as SDOF mass-spring-damper systems attached to the floor structure. The optimal DVA solution is analyzed and compared with traditional improvement measures. The result shows that, with the same increase in the floor weight, the DVAs can be more effective than conventional improvement methods in reducing floor vibration in the target low-frequency bands.
ventricular myocardial is depressed in the borderzone after posterolateral myocardial infarction.

Left contractility Abstract Background— Contractility in the borderzone (BZ) after antero-apical myocardial infarction (MI) is depressed. We tested the hypothesis that BZ contractility is also decreased after posterolateral MI. Methods— Five sheep underwent postero-lateral MI. Magnetic resonance imaging (MRI) was performed 2 weeks before and 16 weeks after MI, and left ventricular (LV) volume and regional strain were measured. Finite element (FE) models were constructed and the systolic material parameter, T max , was calculated in the BZ and remote myocardium by minimizing the difference between experimentally measured and calculated LV strain and volume. Sheep were sacrificed 17 weeks after MI and myocardial muscle fibers were taken from the BZ and remote myocardium. Fibers were chemically demembranated, and isometric developed force, F max , was measured at supra-maximal [Ca 2+ ]. Routine light microscopy was also performed. Results— There was no difference in T max between pre-MI and remote myocardium 16 weeks after MI. However, there was a large decrease (63.3%, p=0.005) in T max in the BZ when compared with the remote myocardium 16 weeks after MI. In addition, there was a significant reduction of BZ F max for all samples (18.9%, p=0.023). Myocyte cross-sectional area increased by 61% (p=0.021) in the BZ, but there was no increase in fibrosis. Conclusion— Contractility in the borderzone is significantly depressed relative to the remote myocardium after postero-lateral MI. The reduction in contractility is due at least in part to a decrease in contractile protein function.