Miracle-dz/UniFAR-FTUs huggingface dataset:
In Pursuit of Activity Dependent Synaptic Plasticity Rules for Cerebellar Motor Learning: A Computational Study

The cerebellar circuit is one of the stereotypical structures of the brain in which learning at the behavioral level can be directly associated with plastic changes at the synaptic level. Therefore, understanding the regulatory foundations of plasticity in the cerebellum and translating them to behavioral output is of great importance. In this paper, we proposed a computational framework to identify and investigate different synaptic plasticity rules as a function of population activity in deep cerebellar nuclei and Purkinje cell. Comparing the robustness of the rules against parameter variations, the most plausible scenario was selected and applied to model gain adaptation of optokinetic reflex (OKR), a typical cerebellar-dependent task, which promotes fixation of images on the center of vision. The model could successfully regenerate the process of OKR gain adaptation confirms to the experimental data of wild-type mice. The model could also predict the effect of flocculus shutdown, immediately after the 4th training session, on long-term learning. The results of this work illustrate the great potential of population models and activity-dependent plasticity rules in reproducing a simple whole-cerebellum network, and open a possibility to predict behavioral dynamics from neural activity.
Likelihood-based Multiple Imputation by Event Chain Methodology for Repair of Imperfect Event Logs with Missing Data

The event log recorded through an information system may be missing for various reasons, which fact may result in an imperfect event log. Performing analyses using such an imperfect event log can seriously affect the quality of the obtained results. Therefore, analyses should be performed only after processing of the missing part in the imperfect event log. In the fields of data mining and statistical analysis, various methodologies have been developed to handle data with missing values, but there are not many studies dealing with incomplete event logs that have missing data in the field of process mining. In this paper, we propose a likelihood-based Multiple Imputation by Event Chain (MIEC) method for dealing with imperfect event logs with missing data. An experiment was performed using sample event logs, and a case study was conducted using a real steel manufacturing event log to verify our method. We expect the proposed method to repair the imperfect event log to a high level and to obtain analysis result with high quality even if there are many missing data.
Multi-Target Electrolyte Sensing Front-End for Wearable Physical Monitoring

Wearable physiology is an expanding field, especially for sports applications that steadily require an accurate monitoring of the physical status of athletes. This work presents the co-design and the realization of an hardware front-end that enables multi-sensing of up to four endogenous electrolytes. A pH and temperature readout circuit are included for sensor calibration. The platform is validated with potassium and sodium ions monitoring. The hardware is remotely controlled by an user interface that configures the sensor panel and collects the biological data through a Bluetooth link. The sizes of the front-end circuit allows its integration into a headband, suitable for wearable monitoring in sweat.
Latent Question Interpretation Through Variational Adaptation

Most artificial neural network models for question-answering rely on complex attention mechanisms. These techniques demonstrate high performance on existing datasets; however, they are limited in their ability to capture natural language variability, and to generate diverse relevant answers. To address this limitation, we propose a model that learns multiple interpretations of a given question. This diversity is ensured by our interpretation policy module which automatically adapts the parameters of a question-answering model with respect to a discrete latent variable. This variable follows the distribution of interpretations learned by the interpretation policy through a semi-supervised variational inference framework. To boost the performance further, the resulting policy is fine-tuned using the rewards from the answer accuracy with a policy gradient. We demonstrate the relevance and efficiency of our model through a large panel of experiments. Qualitative results, in particular, underline the ability of the proposed architecture to discover multiple interpretations of a question. When tested using the Stanford Question Answering Dataset 1.1, our model outperforms the baseline methods in finding multiple and diverse answers. To assess our strategy from a human standpoint, we also conduct a large-scale user study. This study highlights the ability of our network to produce diverse and coherent answers compared to existing approaches. Our Pytorch implementation is available as open source.11github. com/parshakova/APIP.
Misalignment Recognition Using Markov Random Fields With Fully Connected Latent Variables for Detecting Localization Failures

Recognizing misalignment between sensor measurements and objects that exist on a map due to inaccuracies in localization estimation is challenging. This can be attributed to the fact that the sensor measurements are individually modeled for solving the localization problem, resulting in entire relations of the measurements being ignored. This letter proposes a misalignment recognition method using Markov random fields with fully connected latent variables for the detection of localization failures. The proposed method estimates the classes of each sensor measurement that are aligned, misaligned, and obtained from unknown obstacles. The full connection allows us to consider the entire relation of the measurements. A misalignment can be exactly recognized even when partial sensor measurements overlap with mapped objects. Based on the class estimation results, we are able to distinguish whether the localization has failed or not. The proposed method was compared with six alternative methods, including a convolutional neural network, using datasets composed of success and failure localization samples. Experimental results show that the classification accuracy of the localization samples using the proposed method exceeds 95% and outperforms the other examined methods.
Preprocessing Method for Performance Enhancement in CNN-Based STEMI Detection From 12-Lead ECG

ST elevation myocardial infarction (STEMI) is an acute life-threatening disease. It shows a high mortality risk when a patient is not timely treated within the golden time, prompt diagnosis with limited information such as electrocardiogram (ECG) is crucial. However, previous studies among physicians and paramedics have shown that the accuracy of STEMI diagnosis by the ECG is not sufficient. Thus, we propose a detecting algorithm based on a convolutional neural network (CNN) for detecting the STEMI on 12-lead ECG in order to support physicians, especially in an emergency room. We mostly focus on enhancing the detecting performance using a preprocessing technique. First, we reduce the noise of ECG using a notch filter and high-pass filter. We also segment pulses from ECG to focus on the ST segment. We use 96 normal and 179 STEMI records provided by Seoul National University Bundang Hospital (SNUBH) for the experiment. The sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic (ROC) curve are increased from 0.685, 0.350, and 0.526 to 0.932, 0.896, and 0.943, respectively, depending on the preprocessing technique. As our result shows, the proposed method is effective to enhance STEIM detecting performance. Also, the proposed algorithm would be expected to help timely and the accurate diagnosis of STEMI in clinical practices.
Memory Augmented Policy Optimization for Program Synthesis with Generalization

This paper presents Memory Augmented Policy Optimization (MAPO): a novel policy optimization formulation that incorporates a memory buffer of promising trajectories to reduce the variance of policy gradient estimates for deterministic environments with discrete actions. The formulation expresses the expected return objective as a weighted sum of two terms: an expectation over a memory of trajectories with high rewards, and a separate expectation over the trajectories outside the memory. We propose 3 techniques to make an efficient training algorithm for MAPO: (1) distributed sampling from inside and outside memory with an actor-learner architecture; (2) a marginal likelihood constraint over the memory to accelerate training; (3) systematic exploration to discover high reward trajectories. MAPO improves the sample efficiency and robustness of policy gradient, especially on tasks with a sparse reward. We evaluate MAPO on weakly supervised program synthesis from natural language with an emphasis on generalization. On the WikiTableQuestions benchmark we improve the state-of-the-art by 2.5%, achieving an accuracy of 46.2%, and on the WikiSQL benchmark, MAPO achieves an accuracy of 74.9% with only weak supervision, outperforming several strong baselines with full supervision. Our code is open sourced at this https URL
Edge-Boost: Enhancing Multimedia Delivery with Mobile Edge Caching in 5G-D2D Networks

By moving computation and caching to the network edge, Mobile Edge Computing (MEC) offloads core networks and shortens data access latencies, which is important for large scale mobile multimedia services. Increasing the density of edge data centers to service these multimedia requests is uneconomical. Recent research has proven the benefits of involving devices in the delivery of multimedia services. This is done by exploiting the idle computation and storage resources via device-to-device (D2D) communication, i.e., by forming a so-called Mobile Device Cloud (MDC). Despite the flexibility and cost efficiency of this MDC paradigm, the timely allocation of caching resources to satisfy the dynamic user demands is challenging. This is mainly due to the uncertainty in resource availability of mobile devices. To this end, we propose Edge-Boost, a novel MDC caching architecture for lowlatency multimedia streaming services. We develop a novel fluid-based model to capture the dynamically changing network status. Additionally, we propose a dynamic caching allocation to jointly minimize caching cost and service latency. Edge-Boost achieves over 20% higher average cache utilization and 15% shorter average access latency than the state-ofthe-art MDC approach.
Discrimination of Depression Levels Using Machine Learning Methods on EEG Signals

Depression is a mental disorder which has direct effects on electroencephalography (EEG) of patients, that made EEG analysis a beneficial way for a depression diagnosis. A precise system which can diagnose the depression levels based on the EEG signal would be useful support. This paper presents a machine learning approach to discriminate the depressed subjects to four different levels of depression, according to the Beck depression inventory (BDI-II) scores, besides the separability of different levels is investigated. In this way, we also proposed a fuzzy function based on neural network (FFNN) classifier. Our dataset contains EEG signals recorded from 60 depressed subjects with different levels of depression, under resting state, and EEG analysis was done using nonlinear features including fuzzy entropy (FuzzyEn), Katz fractal dimension (KFD) and fuzzy fractal dimension (FFD). The results indicate that KFD has a better capability in the prediction of the depression level. The proposed fuzzy classifier has demonstrated significant supremacy compared to support vector machine (SVM) in almost all experiments.
Lipid Biomarkers for Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal degenerative disease primarily characterized by the selective loss of upper and lower motor neurons. To date, there is still an unmet need for robust and practical biomarkers that could estimate the risk of the disease and its progression. Based on metabolic modifications observed at the level of the whole body, different classes of lipids have been proposed as potential biomarkers. This review summarizes investigations carried out over the last decade that focused on changes in three major lipid species, namely cholesterol, triglycerides and fatty acids. Despite some contradictory findings, it is becoming increasingly accepted that dyslipidemia, and related aberrant energy homeostasis, must be considered as essential components of the pathological process. Therefore, it is tempting to envisage dietary interventions as a means to counterbalance the metabolic disturbances and ameliorate the patient's quality of life.
Self-Representation Convolutional Neural Networks

The traditional convolutional neural networks (CNNs) learn numbers of fixed kernels(filters), which are used to obtain the representations of fixed patterns. Therefore, the knowledge representations of CNNs are limited to the number of kernels. In this paper, we present a Self-Representation Convolutional (SRC) layer to obtain richer knowledge representations of images by fully considering the self-similarity between adjacent pixels. SRC layer comprises a learnable local correlation measurement which measures the importance of adjacent pixels to the current pixel and two learnable linear parameters that perform linear projection on adjacent pixels and the weighted sum vectors, respectively. Compared with regular convolutional layers, the SRC layers can not only obtain comparable knowledge representations, but also reduce by a factor of 3× to 56× in the number of learnable parameters. Empirically, CNNs with SRC layers, called Self-Representation Convolutional Neural Networks (SRCNN), achieve strong performances on a range of visual datasets (SVHN, CIFAR-10 and CIFAR-100) while enjoying significant parameters and FLOPs savings.
Adaptive Wiener Gain to Improve Sound Quality on Nonnegative Matrix Factorization-Based Noise Reduction System

Nonnegative matrix factorization (NMF) is a useful decomposition technique for multivariate data. More recently, NMF technology was used as a noise-estimation stage for a Wiener-filtering-based noise reduction (NR) method to improve the quality of noisy speech. Previous studies showed that this method provides better sound quality performance than conventional NMF-based approaches; however, there is still scope for improving the performance under noisy listening conditions. More specifically, the performance of an NMF noise estimator for calculating the noise level is considered sensitive to diverse noise environments and signal-to-noise ratio conditions. Therefore, we proposed an adaptive algorithm that derives an adaptive factor ( $\alpha $ ) to adjust the weight between the estimated speech and noise levels on the basis of the signal-to-noise level for the gain function of the Wiener-filtering-based NR method to further improve the sound quality. Two objective evaluations and listening tests evaluated the benefits of the proposed method, and experimental results show that better output sound quality and competed for speech intelligibility performance can be achieved when compared with conventional unsupervised NR and NMF-based methods.
A double-well potential model for the ionic electrokinetic polarization in low concentration cell suspensions

In order to understand the effects of cells on the ionic electrokinetic polarization process in cell suspensions, a double-well potential electrokinetic polarization model for ions in cell suspensions under a pressure gradient field is established with reference to the ionic double-well potential model in dielectric polarization. By analyzing the ionic electrokinetic polarization process in the double-well potential, the relation between the electrokinetic polarization field of the ions in the well potential and the external pressure gradient field is derived. The “repression effect” of cell concentration on the ionic electrokinetic polarization voltage is theoretically analyzed, and the mechanisms of the effects of cells on the ionic electrokinetic polarization are obtained. The electrokinetic polarization voltages of the suspensions with different concentrations of rabbit blood cell in phosphate buffer solution are measured. The experiment results showed that the ionic electrokinetic polarization voltage decreased with increased cell concentration in the range of 105~ 107/ml, which is consistent with the theoretical models. The experiment has shown the validity of the double-well potential model for cell suspensions of low concentration. Our research deepens the understanding of the mechanism of electrokinetic polarization of cell suspensions, and provides a new model for using electrokinetic polarization to analyze the charge characteristics on cell surface.
Lighting impulse withstand performance of CF3I-N2 gas mixture for 252 kV GIL insulation

Trifluoroiodomethane (CF<sub>3</sub>I) has been broadly proposed as one of the most potential replacements for SF<sub>6</sub> owing to its outstanding dielectric strength and eco-friendly property. In this study, the basic lighting impulse withstand performance of CF<sub>3</sub>I and nitrogen mixture with a volume ratio of 20/80 (CF<sub>3</sub>I-N<sub>2</sub> (20/80)) under several high gas pressures has been investigated. The withstand voltages (U<sub>w</sub>) of two sphere-plate electrode systems were measured and the maximum withstand field strengths (Emax) were calculated to estimate its insulation ability for GIL product. At the same time, the properties of pure sulphur hexafluoride (SF<sub>6</sub>) and SF<sub>6</sub>-N<sub>2</sub> (20/80) gas mixture have been studied for comparison. As the main purpose of this study, a 252 kV gas insulated transmission line (GIL), original designed for pure SF<sub>6</sub> has been applied to verify the suitability of the CF<sub>3</sub>I-N<sub>2</sub> (20/80) mixture. With some design optimization, the CF<sub>3</sub>I-N<sub>2</sub> (20/80) gas mixture can supply sufficient withstand ability with enough safety margin for the GIL product.
Dielectric switching studies of polyvinylidene fluoride thin films with dominant planar ferroelectric domain configuration for flexible electronic devices

Electrical switching characteristics of the polymer chains of polyvinylidene fluoride (PVDF) thin films with dominant planar domain configuration were investigated in this work. PVDF a semi-crystalline piezoelectric polymer is known to undergo structural transformation when subjected to electric field. In this study, the I-V characteristics of PVDF was analyzed by varying the delay time from 0.1 to 0.001 seconds in order to understand the response of switching behavior; a promising attribute that governs the operational speed of the device. A hysteretic behavior in the I-V characteristics was observed at different drive voltages. The ferroelectric polarization domains responds at different voltages and give rise to polarization switching currents. In addition to this, at relatively larger voltages (> 10 V) the amorphous intermediate regions of PVDF is expected to undergo an amorphous to crystalline transformation. Hence, the switching characteristics of the ferroelectric domains in the form of switching currents and the dipolar re-orientation arising due to the amorphous to crystalline transformation by rotation of polymer chains are captured at different voltages. Furthermore, a longer delay time (0.1 seconds) is employed to capture the current arising from a reversible amorphous to crystalline transformation. The dielectric relaxation time for the rotation of polymer chains in PVDF was inferred from independent studies and observed to be between 100 to 500 μS.
Data Analytic Tool for Clustering Identification based on Dimensionality Reduction of Frequency Measurements

This work presents a data analytic tool for clustering analysis based on Dimensionality Reduction (DR) of power system measurements. The proposed method is applied to frequency measurements of the ENTSO-E dynamic model of continental Europe and the results are compared with other conventional DR approaches. After considerable reduction of the raw measurements, a phasor metric for identification of coherency groups of generators is proposed. The recommended measure stands for its simple implementation, interpretation and fast computation. To illustrate the effectiveness of the clustering approach and the coherency of the metrics, a particular study case following the outage of a representative generation unit in France is presented.
Inferring the Deployment of Top Domains over Public Clouds using DNS Data

Cloud technologies are becoming pervasive and available for private companies or public institutions in different flavors, mostly public cloud or private clouds. Our focus in this work is on the usage of public cloud technologies by the most popular sites in the Internet. While some studies have described the nascent landscape of public cloud computing 5 years ago, surprising little effort has been put to study the recent evolution of this domain.Using DNS data that enables us to map domains (e.g., netflix.com) and their subdomains (e.g., api.netflix.com) with the cloud providers actually used, we refresh our understanding of this ecosystem. We focus on the dominant four cloud providers, namely Amazon Web Services, Microsoft Azure, Google Cloud Computing and IBM Bluemix. We demonstrate that cloud penetration has clearly increased since 2013, reaching almost 50% of the top 1000 domains, from the Alexa list. Furthermore, a significant fraction of domains use multiple cloud providers simultaneously. Still, domain owenrs remain cautious when it comes to choose which subdomain is actually hosted in the cloud and only 17.8% on average of the subdomains are actually hosted in the cloud. In terms of performance, preliminary results indicate that hosting a subdomain in the cloud pays off as compared to private hosting with a decrease of application level latency of 28%.