Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on High-speed Railway Wheel/rail Non-steady-state Rolling Contact

Based on the Kalker three-dimensional rolling contact exact theory and the study of creep force characteristics under single spin creepage harmonic vibration, the corresponding non-steady state transfer function was fitted, and the non-steady state rolling contact problems with both spin and longitudinal creepages were studied considering the effects of time-dependent wheel/rail contact elastic displacement gradient and complex wheel/rail creepage conditions. The results show that the creep force phase lag compared with creepage is found in the calculation result by non-steady-state rolling contact method. With the decrease of creepage harmonic wave ratio Lᶓ/a the greater the decrease of the creep force amplitude gain and the more phase lag. Using the non-steady state transfer function method to calculate the creep force magnitude and phase consistent with the Kalker three-dimensional rolling contact exact theory method. And the longitudinal creepage also has effect on transfer function of spin creepage/lateral creep force.
Unsupervised End-to-End Learning of Discrete Linguistic Units for Voice Conversion

We present an unsupervised end-to-end training scheme where we discover discrete subword units from speech without using any labels. The discrete subword units are learned under an ASR-TTS autoencoder reconstruction setting, where an ASR-Encoder is trained to discover a set of common linguistic units given a variety of speakers, and a TTS-Decoder trained to project the discovered units back to the designated speech. We propose a discrete encoding method, Multilabel-Binary Vectors (MBV), to make the ASR-TTS autoencoder differentiable. We found that the proposed encoding method offers automatic extraction of speech content from speaker style, and is sufficient to cover full linguistic content in a given language. Therefore, the TTS-Decoder can synthesize speech with the same content as the input of ASR-Encoder but with different speaker characteristics, which achieves voice conversion (VC). We further improve the quality of VC using adversarial training, where we train a TTS-Patcher that augments the output of TTS-Decoder. Objective and subjective evaluations show that the proposed approach offers strong VC results as it eliminates speaker identity while preserving content within speech. In the ZeroSpeech 2019 Challenge, we achieved outstanding performance in terms of low bitrate.
Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles

Radar-based road user classification is an important yet still challenging task towards autonomous driving applications. The resolution of conventional automotive radar sensors results in a sparse data representation which is tough to recover by subsequent signal processing. In this article, classifier ensembles originating from a one-vs-one binarization paradigm are enriched by one-vs-all correction classifiers. They are utilized to efficiently classify individual traffic participants and also identify hidden object classes which have not been presented to the classifiers during training. For each classifier of the ensemble an individual feature set is determined from a total set of 98 features. Thereby, the overall classification performance can be improved when compared to previous methods and, additionally, novel classes can be identified much more accurately. Furthermore, the proposed structure allows to give new insights in the importance of features for the recognition of individual classes which is crucial for the development of new algorithms and sensor requirements.
Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline

This article has been removed from the ACM Digital Library because it was found to plagiarize an earlier work written by Zhedong Zheng, Liang Zheng and Yi Yang, published by IEEE Xplore and entitled "Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro" 2017 IEEE International Conference on Computer Vision https://doi.org/10.1109/ICCV.2017. 405. For further information, contact the ACM Director of Publications.
Shredder: Learning Noise to Protect Privacy with Partial DNN Inference on the Edge

A wide variety of DNN applications increasingly rely on the cloud to perform their huge computation. This heavy trend toward cloud-hosted inference services raises serious privacy concerns. This model requires the sending of private and privileged data over the network to remote servers, exposing it to the service provider. Even if the provider is trusted, the data can still be vulnerable over communication channels or via side-channel attacks [1,2] at the provider. To that end, this paper aims to reduce the information content of the communicated data without compromising the cloud service's ability to provide a DNN inference with acceptably high accuracy. This paper presents an end-to-end framework, called Shredder, that, without altering the topology or the weights of a pre-trained network, learns an additive noise distribution that significantly reduces the information content of communicated data while maintaining the inference accuracy. Shredder learns the additive noise by casting it as a tensor of trainable parameters enabling us to devise a loss functions that strikes a balance between accuracy and information degradation. The loss function exposes a knob for a disciplined and controlled asymmetric trade-off between privacy and accuracy. While keeping the DNN intact, Shredder enables inference on noisy data without the need to update the model or the cloud. Experimentation with real-world DNNs shows that Shredder reduces the mutual information between the input and the communicated data to the cloud by 70.2% compared to the original execution while only sacrificing 1.46% loss in accuracy.
A general framework of smart Open Linked Government Data: Application in E-health

The exploitation of information is deeply rooted in major Government functions such as service provisioning, inspection, and policy development. Open Government Data (OGD) initiatives provide mean for stakeholders to obtain government information about a locality or country, in order to reuse them and create a source of enrichment in several ways: new user services, internal lever of modernization, economic development and increased transparency. Various actors around the world are focusing on the availability of open public data in data portals, by applying legal guidelines and beneficiating from the technical competence of public organizations. While these open data government portals are offering tools to present, search, download and visualize the government information, critical voices start addressing some issues of availability of a large amount of replicated datasets, therefore, a difficulty of finding relevant datasets and accessibility of datasets without connection between them. In this paper a framework for generating smart open linked government data (smart OLGD) is proposed, this framework profits from several technologies, Linked data, Natural language processing to aggregate in a semantic level similar datasets and Ratings-Based Recommender Systems to pro-vide suggestions of datasets that may represent a potential interest for citizens.
Rapamycin Rescues TDP-43 Mislocalization and the Associated Low Molecular Mass Neurofilament Instability

TDP-43 is a nuclear protein involved in exon skipping and alternative splicing. Recently, TDP-43 has been identified as the pathological signature protein in frontotemporal lobar degeneration with ubiquitin-positive inclusions and in amyotrophic lateral sclerosis. In addition, TDP-43-positive inclusions are present in Parkinson disease, dementia with Lewy bodies, and 30% of Alzheimer disease cases. Pathological TDP-43 is redistributed from the nucleus to the cytoplasm, where it accumulates. An ∼25-kDa C-terminal fragment of TDP-43 accumulates in affected brain regions, suggesting that it may be involved in the disease pathogenesis. Here, we show that overexpression of the 25-kDa C-terminal fragment is sufficient to cause the mislocalization and cytoplasmic accumulation of endogenous full-length TDP-43 in two different cell lines, thus recapitulating a key biochemical characteristic of TDP-43 proteinopathies. We also found that TDP-43 mislocalization is associated with a reduction in the low molecular mass neurofilament mRNA levels. Notably, we show that the autophagic system plays a role in TDP-43 metabolism. Specifically, we found that autophagy inhibition increases the accumulation of the C-terminal fragments of TDP-43, whereas inhibition of mTOR, a key protein kinase involved in autophagy regulation, reduces the 25-kDa C-terminal fragment accumulation and restores TDP-43 localization. Our results suggest that autophagy induction may be a valid therapeutic target for TDP-43 proteinopathies.
Rogue waves and self-similar intermediate asymptotics

We explore extreme event emergence in statistical nonlinear wave systems with self-similar intermediate asymptotics. We show, within the framework of a generic (1+1)D nonlinear Schrodinger equation with linear gain, that the rogue waves in weakly nonlinear, statistical open systems emerge as parabolic-shape giant fluctuations in the self-similar asymptotic propagation regime. We analytically derive the non-Gaussian statistics of such rogue waves, validate our results with numerical simulations, and demonstrate the self-similar structure of the emergent rogue wave statistics. Our results shed new light on generic statistical features of rogue waves in nonlinear open systems with self-similar intermediate asymptotics.
Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting

Graph convolution network-based approaches have been recently used to model region-wise relationships in region-level prediction problems in urban computing. Each relationship represents a kind of spatial dependency, such as region-wise distance or functional similarity. To incorporate multiple relationships into a spatial feature extraction, we define the problem as a multi-modal machine learning problem on multi-graph convolution networks. Leveraging the advantage of multi-modal machine learning, we propose to develop modality interaction mechanisms for this problem in order to reduce the generalization error by reinforcing the learning of multi-modal coordinated representations. In this work, we propose two interaction techniques for handling features in lower layers and higher layers, respectively. In lower layers, we propose grouped GCN to combine the graph connectivity from different modalities for a more complete spatial feature extraction. In higher layers, we adapt multi-linear relationship networks to GCN by exploring the dimension transformation and freezing part of the covariance structure. The adapted approach, called multi-linear relationship GCN, learns more generalized features to overcome the train–test divergence induced by time shifting. We evaluated our model on a ride-hailing demand forecasting problem using two real-world datasets. The proposed technique outperforms state-of-the art baselines in terms of prediction accuracy, training efficiency, interpretability and model robustness.
Beyond Exponentially Discounted Sum: Automatic Learning of Return Function

In reinforcement learning, Return, which is the weighted accumulated future rewards, and Value, which is the expected return, serve as the objective that guides the learning of the policy. In classic RL, return is defined as the exponentially discounted sum of future rewards. One key insight is that there could be many feasible ways to define the form of the return function (and thus the value), from which the same optimal policy can be derived, yet these different forms might render dramatically different speeds of learning this policy. In this paper, we research how to modify the form of the return function to enhance the learning towards the optimal policy. We propose to use a general mathematical form for return function, and employ meta-learning to learn the optimal return function in an end-to-end manner. We test our methods on a specially designed maze environment and several Atari games, and our experimental results clearly indicate the advantages of automatically learning optimal return functions in reinforcement learning.
Structure Learning for Neural Module Networks

Neural Module Networks, originally proposed for the task of visual question answering, are a class of neural network architectures that involve human-specified neural modules, each designed for a specific form of reasoning. In current formulations of such networks only the parameters of the neural modules and/or the order of their execution is learned. In this work, we further expand this approach and also learn the underlying internal structure of modules in terms of the ordering and combination of simple and elementary arithmetic operators. We utilize a minimum amount of prior knowledge from the human-specified neural modules in the form of different input types and arithmetic operators used in these modules. Our results show that one is indeed able to simultaneously learn both internal module structure and module sequencing without extra supervisory signals for module execution sequencing. With this approach, we report performance comparable to models using hand-designed modules. In addition, we do a analysis of sensitivity of the learned modules w.r. t. the arithmetic operations and infer the analytical expressions of the learned modules.
Case study on the effects of partial solar eclipse on distributed PV systems and management areas

Photovoltaic (PV) systems depend on irradiance, ambient temperature and module temperature. A solar eclipse causes significant changes in these parameters, thereby impacting PV generation profile, performance, and power quality of larger grid where they connect to. This paper presents a case study to evaluate the impacts of the solar eclipse of August 21, 2017 on two real-world grid-tied PV systems (1.4MW and 355kW) in Miami and Daytona, Florida, the feeders they are connected to, and the management areas they belong to. Four types of analyses are conducted to obtain a comprehensive picture of the impacts using 1-minute PV generation data, hourly weather data, real feeder parameters, and daily reliability data. These analyses include: individual PV system performance measurement using power performance index; power quality analysis at the point of interconnection; a study on the operation of voltage regulating devices on the feeders during eclipse peak using an IEEE 8500 test case distribution feeder; and reliability study involving a multilayer perceptron framework for forecasting system reliability of the management areas. Results from this study provide a unique insight into how solar eclipses impact the behavior of PV systems and the grid, which would be of concern to electric utilities in future high penetration scenarios.
Empowering Smart Cities with Strong Cryptography for Data Privacy

The transformative promise of Smart Cities relies largely on the collection and innovative analysis of data produced by and about its citizens. As more insights are gained from such analysis, its impacts are difficult to predict. Care must be taken to ensure data is transmitted and stored using privacy-protecting methods. Traditional strategies of access control are necessary but insufficient as major security breaches are becoming more commonplace. Attempts to strip data sets of personally identifiable information may appear to protect privacy, but de-anonymization techniques using statistical analysis can uniquely identify a person from surprisingly little information. Strong cryptography, when implemented correctly, can provide these protections. Appropriately applied cryptography can ensure that breaches reveal nothing about the data it protects. And beyond the confidentiality guarantees from typical cryptographic applications, end-to-end cryptography can also help to ensure privacy is maintained. Sophisticated mechanisms for sharing and revoking access become approachable and inherent to the system. Integrating cryptography at a foundational level allows projects to adapt to new insights without sacrificing privacy. Impacts from breaches are drastically mitigated and unforeseen statistical correlations are next to impossible. Our paper details our experiences, in collaboration with NIST, developing an end-to-end encrypted platform that empowers users with fine-grained control over their own data privacy.
Deep Feature Extraction for Pap-Smear Image Classification: A Comparative Study

Cervical cancer is one of the major public health problems in the world. Even if it is one of the most preventable cancers, the early screening with reliable Pap-smear test is the key to curing. Based on the morphology and the texture of the cervix cells, Cytopathologists rely on hand-crafted features to determine whether a cellule is normal or abnormal. In medical imaging, deep learning can generate features that are more sophisticated and difficult to elaborate in descriptive means. These relevant features allow improving the classification's accuracy. In this study, we use deep neural networks to extract features from Pap-smear images and provide these extracted features as inputs for optimized MLP classifier. We study the ability of the four different pre-trained models as feature extractors toward classifying Pap-smear images. Experiments performed on the DTU/HERLEV Database and comparison show that for the Pap-smear image classification, the ResNet50 exceeds the VGGs and the InceptionV3 by 15% of accuracy.
XMM-Newton spectrum of the magnetar CXOU J171405.7–381031

We observe the magnetar CXOU J171405.7−381031 with XMM-Newton and obtain the most reliable X-ray spectral parameters for this magnetar. After removing the flux from the surrounding supernova remnant CTB 37B, the radiation of CXOU J171405.7−381031 is best described by a two-component model, consisting of a blackbody and power law. We obtain a blackbody temperature of $0.58^{+0.03}_{-0.03}$ keV, a photon index of $2.15^{+0.62}_{-0.68}$, and an unabsorbed 2–10 keV band flux of $2.33^{+0.02}_{-0.02} \times 10^{-12}$ erg cm−2 s−1. These new parameters enable us to compare CXOU J171405.7−381031 with other magnetars, and it is found that the luminosity, temperature, and photon index of CXOU J171405.7−381031 are aligned with the known trend among the magnetar population with a slightly higher temperature, which could be caused by its young age. All magnetars with a spin-down age of less than 1000 yr show time variation or bursts except for CXOU J171405.7−381031. We explore the time variability for six observations between 2006 and 2015, but there is no variation larger than ∼10%.
The structure of mood: a comparison between sleep deprivation and normal wakefulness conditions.

Summary The stability of the factor structure of mood was examined under two different experimental conditions, sleep deprivation and normal wakefulness In the first phase of the study, two different forms of a mood scale were administered to several college classes, and the ratings for each scale were intercorrelated and factored Eight interpretable factors emerged from each analysis, and the two sets of factors were highly congruent. In the second phase of the study subjects completed the mood ratings after staying awake all night or after getting a good night's rest Comparisons between the means for the two groups showed significant differences on 26 of the 44 variables included in the scale The scores for the sleep-deprived group were then intercorrelated and factored, and the factor structure was compared with the relevant factor structure obtained in the first phase of the study The results of the comparison showed that six of the eight factors appeared under both conditions of sleep deprivation and normal wakefulness Additional analysis indicated that a seventh factor appeared, but in the guise of different variables The only factor which failed to appear under sleep deprivation was elation. It was concluded that although variable ratings showed high sensitivity to the experimental operations, the basic structure of the underlying mood states remained remarkably stable The results indicate that the search for a list of basic mood states may be quite fruitful An important methodological implication was that a variable may be selected to measure a mood under various experimental operations with the assurance that the nature of the mood to be measured does not change as a result of those operations
AIS Electronic Library (AISeL)

Recommender systems are widely used for suggesting books, education materials, and products to users by exploring their behaviors. In reality, users’ preferences often change over time, which leads to the studies on time-dependent recommender systems. However, most existing approaches to deal with time information remain primitive. In this paper, we extend existing methods and propose a hidden semi-Markov model to track the change of users’ interests. Particularly, this model allows for users to stay in different (latent) interest states for different time periods, which is beneficial to model the heterogeneous length of users’ interest and focuses. We derive an EM algorithm to estimate the parameter of the framework, and predict users’ actions. Experiments on a real-world dataset show that our model significantly outperforms the state-of-the-art benchmark methods. Further analyses show that the performance depends on the allowed heterogeneity of latent states and the existence of user interest heterogeneity in the dataset.
Reasoning about Quality and Fuzziness of Strategic Behaviours

We introduce and study SL[F], a quantitative extension of SL (Strategy Logic), one of the most natural and expressive logics describing strategic behaviours. The satisfaction value of an SL[F] formula is a real value in [0,1], reflecting ``how much'' or ``how well'' the strategic on-going objectives of the underlying agents are satisfied. We demonstrate the applications of SL[F] in quantitative reasoning about multi-agent systems, by showing how it can express concepts of stability in multi-agent systems, and how it generalises some fuzzy temporal logics. We also provide a model-checking algorithm for ourlogic, based on a quantitative extension of Quantified CTL*.
Rare Failure Prediction via Event Matching for Aerospace Applications

In this paper, we consider a problem of failure prediction in the context of predictive maintenance applications. We present a new approach for rare failures prediction, based on a general methodology, which takes into account peculiar properties of technical systems. We illustrate the applicability of the method on the real-world test cases from aircraft operations.