Miracle-dz/UniFAR-FTUs huggingface dataset:
DCCR: Deep Collaborative Conjunctive Recommender for Rating Prediction

Recently, collaborative filtering combined with various kinds of deep learning models is appealing to recommender systems, which have shown a strong positive effect in an accuracy improvement. However, many studies related to deep learning model rely heavily on abundant information to improve prediction accuracy, which has stringent data requirements in addition to raw rating data. Furthermore, most of them ignore the interaction effect between users and items when building the recommendation model. To address these issues, we propose DCCR, a deep collaborative conjunctive recommender, for rating prediction tasks that are solely based on the raw ratings. A DCCR is a hybrid architecture that consists of two different kinds of neural network models (i.e., an autoencoder and a multilayered perceptron). The main function of the autoencoder is to extract the latent features from the perspectives of users and items in parallel, while the multilayered perceptron is used to represent the interaction between users and items based on fusing the user and item latent features. To further improve the performance of DCCR, an advanced activation function is proposed, which can be specified with input vectors. The extensive experiments conducted with two well-known real-world datasets and performances of the DCCR with varying settings are analyzed. The results demonstrate that our DCCR model outperforms other state-of-art methods. We also discuss the performance of the DCCR with additional layers to show the extensibility of our model.
Adaptive droop-based voltage control in multi-terminal dc systems

This paper presents a modified droop-based control for dc voltage restoration in a Multi-Terminal dc (MTdc) system of parallel-operated buck converters. The proposed control structure is introduced to a proportional-derivative (PD) droop controller, which realises the active power — dc voltage droop control method. The additional control structure consists of an adaptive integrator, which decreases the voltage static error of the PD droop controller. The achieved voltage restoration architecture is thus fully decentralised and therefore does not depend on communication nor relies on additional secondary controllers. The integrator gain is adaptive through a scheduling schema and a switching schema. The former decouples the dynamics of the voltage restoration controller from those of the PD droop controller. The latter facilitates the simultaneous fulfilment of the constraints on voltage and power deviations. Simulation results validate the performance of the proposed control structure.
Experimental Evaluation of Mobile Core Networks on Simultaneous Access from M2M/IoT Terminals

Handling congestion in LTE/5G cellular networks has become a critical issue due to recent, rapid increase in cellular network traffic. Control plane congestion becomes particularly serious when accommodating massive M2M/IoT terminals into cellular networks. Although various methods have been proposed for accommodation of M2M/IoT terminals, most of them are mainly based on mathematical analysis and simulation experiments. In this paper, we show the experimental evaluation results of the performance of a mobile core network to assess the impact of bursty access from M2M/IoT terminals. First, we constructed our experimental environment based on open-source implementation of mobile core networks and emulated user terminals. Then, we conducted experiments on simultaneous attach requests from multiple user terminals and measured the processing delay at each mobile core node. The results of our evaluations proved that concentrating attach requests from forty user terminals increases the processing time at a mobility management entity (MME) by up to around 510% when an MME node is operated on a virtual machine with 1 GHz / 1 core CPU.
Anatomical Heterogeneity of Memory CD4+ T Cells Due to Reversible Adaptation to the Microenvironment1

The memory T cell pool is characterized by a substantial degree of heterogeneity in phenotype and function as well as anatomical distribution, but the underlying mechanisms remain unclear. In this study we confirm that the memory CD4+ T cell pool in wild-type and TCR-transgenic mice consists of heterogeneous subsets, as defined by surface marker expression or cytokine production. Extralymphoid sites contain significant numbers of memory CD4+ T cells, which are phenotypically and functionally distinct from their lymphoid counterparts. However, we show in this study that the phenotype of lymphoid and extralymphoid memory T cells is not stable. Instead, the unique properties of extralymphoid memory T cells are acquired upon migration into extralymphoid sites and are lost when memory T cells migrate back into lymphoid organs. Thus, at least some of the extralymphoid properties may represent a transient activation state that can be adopted by T cells belonging to a single memory T cell pool. Furthermore, such intermittent activation during or after migration into extralymphoid sites could provide an important signal, promoting the survival and functional competence of memory T cells in the absence of Ag.
Increased affect-arousal in VR can be detected from faster body motion with increased heart rate

We instrumented an immersive VR platform with physiological (heart rate and electrodermal activity) sensors to investigate the use of movement data and physiological data to automatically detect changes in affect (emotional state). 12 users were asked to complete four blocks of tasks requiring them to hit moving targets while standing and moving about. One of the four blocks (in counterbalanced order) was designed to be stressful (S), while the other blocks were designed to be calm (C). The motions required of the users were the same in both conditions; only the visual and audio feedback were different across the S and C conditions. Users' self-scored arousal in the S condition was significantly higher. We analyzed the recorded motions by segmenting out 2747 "fast motions", i.e., intervals of time where the sum of the speed of the hands was above a threshold. A simple machine learning algorithm (a decision tree) could learn to classify these fast motions as either calm or stressed, with ≈80% accuracy, using only two features: the maximum speed achieved during the motion, and the heart rate at the moment of maximum speed, where both features were normalized. If only the maximum speed feature is used (i.e., with no physiological data), ≈70% accuracy is achieved.
Variational Bayesian Inference-Based Multiple Target Localization in WSNs With Quantized Received Signal Strength

The received signal strength (RSS)-based target localization is an important field of research with numerous applications in wireless sensor networks. By exploiting the sparsity of localization, the compressive sensing (CS) can be applied to develop an effective localization framework for multiple targets. However, the existing CS-based method considers localization directly from real-valued sensor measurements, which implicitly assume that the measurements have infinite bit precision. When the assumption is violated, the localization performance will deteriorate dramatically, especially at low quantization bit rates. In this paper, we first design a quantizer for efficiently processing the raw RSS measurements and then develop a novel Bayesian CS framework for estimating target locations from the quantized measurements. By modeling the quantization errors as independent random variables, the non-linear localization problem is formulated in a linear CS observation model. Following this idea, we solve the problem from a Bayesian perspective and design some sophisticated prior distributions to guarantee the performance. To address this, we resort to the variational Bayesian inference methodology and propose a novel iterative algorithm for jointly estimating target locations and dealing with quantization errors. The extensive simulation results demonstrate the superiority of the proposed algorithm in comparison with the state-of-the-art methods.
Virtualized Network Function Consolidation Based on Multiple Status Characteristics

In network function virtualization (NFV), network function implemented in software on the generally shared servers is called virtualized network function (VNF). The characteristics of virtualization provide service function chain (SFC) provisioning with the required flexibility by allowing for placing VNFs anywhere and anytime. However, there may be a large number of servers with low utilization across the network at the low-load time, leading to enormous loss of energy. VNF consolidation, as an effective way to address this issue, allows for energy saving by consolidating VNFs into as fewer servers as possible. Unfortunately, it involves multiple conflicting goals, such as energy saving, bandwidth usage minimization, and migration cost reduction, which makes it hard to implement in practical environments. In order to achieve the tradeoff among these goals, this paper proposes a VNF consolidation method, VCMM, which determines appropriate servers to be turned off by leveraging neural network with multiple network status characteristics as input. The neural network is trained using the particle swarm optimization algorithm (PSO). VCMM migrates VNFs on servers to be turned off by adopting a greedy mechanism. Through extensive simulations, we demonstrate that the proposed VCMM outperforms prior approaches in terms of energy, bandwidth, and migration costs.
Comparison of the pharmacological properties of GK11 and MK801, two NMDA receptor antagonists: towards an explanation for the lack of intrinsic neurotoxicity of GK11

Over‐stimulation of NMDA receptors (NMDARs) is involved in many neurodegenerative disorders. Thus, developing safe NMDAR antagonists is of high therapeutic interest. GK11 is a high affinity uncompetitive NMDAR antagonist with low intrinsic neurotoxicity, shown to be promising for treating CNS trauma. In the present study, we investigated the molecular basis of its interaction with NMDARs and compared this with the reference molecule MK801. We show, on primary cultures of hippocampal neurons, that GK11 exhibits neuroprotection properties similar to those of MK801, but in contrast with MK801, GK11 is not toxic to neurons. Using patch‐clamp techniques, we also show that on NR1a/NR2B receptors, GK11 totally blocks the NMDA‐mediated currents but has a six‐fold lower IC50 than MK801. On NR1a/NR2A receptors, it displays similar affinity but fails to totally prevent the currents. As NR2A is preferentially localized at synapses and NR2B at extrasynaptic sites, we investigated, using calcium imaging and patch‐clamp approaches, the effects of GK11 on either synaptic or extrasynaptic NMDA‐mediated responses. Here we demonstrate that in contrast with MK801, GK11 better preserve the synaptic NMDA‐mediated currents. Our study supports that the selectivity of GK11 for NR2B containing receptors accounts contributes, at least partially, for its safer pharmacological profile.
[Polarized populations of T helper cells in patients with chronic hepatitis B virus infection].

OBJECTIVES To investigate the composition of Th1/Th2/Th3 cells in chronic HBV-infected individuals by determining the expression of IL-4/IFN-gamma/TGF-beta in single CD4+ T cell isolated from the peripheral blood mononuclear cells (PBMCs), and analyze the role of Th cells polarized population in chronic HBV-infection. METHODS PBMCs from chronic HBV infected individuals were separated routinely, stimulated by PMA/Ionomycin/Monensin, and the production of IL-4/IFN-gamma/TGF-beta by CD4+ T cells in PBMCs was determined by FACS analysis. RESULTS The percentage of IFN-gamma-producing T cells, IL-4-producing T cells and TGF-beta producing T cells ranged from 2.3%-18.6%, 1.1%-8.7% and 0.7%-7.1%, respectively, in CD4+ cells from non-infected individuals. The majority of CD4+ T cells from PBMCs in chronic HBV-infected individuals were Th0 cells. The proportion of Th1 cells increased significantly with the hepatic inflammation activity, and the proportion in active period of chronic hepatitis B was higher than that in nonactive period (P < 0.05). The percentage of Th2 cells in CD4+ T cells from HBV-infected individuals did not differ significantly (P > 0.05), but it was higher than controls (P < 0.05). The percentage of Th3 cells in CD4+ T cells from AsC group was higher than that in CHB group and control (P < 0.05). CONCLUSIONS Th1 phenotype cytokine are correlated with hepatic inflammation activity of chronic hepatitis B. Th2 cells may be associated with the persistence of HBV infection. Th3 cells cooperating with Th2 cells exert negative immunoregulating action and may be associated with the immune tolerance state of chronic HBV infection.
A Comparison of Time Delay Estimation Methods and Interpolation Methods in Signal-Averaged ECG: Preliminary Results

Signal-Averaged Electrocardiogram (SAECG) is a technique used to analyze ECG records for the detection of subtle changes in the electrical activity of the heart. The alignment (or time delay estimation) of PQRST complexes is the core process in creating an SAECG. In this work, we employed several time delay estimation and interpolation methods to produce SAECG using 60 normal ECG records with added synthesized -30 dB white noise. Performance evaluation was carried out by calculating the ratio of signal to noise ratio (SNR) before and after signal averaging. Spline interpolation method consistently resulted in better performance compared to the linear and cosine methods. Additionally, the results of this study indicate that the sum of square differences (SSD) and the sum of absolute differences (SAD) methods have comparable performances to that of the computationally expensive cross-correlation method.
Enhancement of MAC Protocol for Power Reduction in LoRa WAn

With the increasing use of the Internet of Things (IoT), various LPWA standards are being introduced. The authors focused on the LoRaWAnstandard with low power consumption and transmission distance of several kilometers. As ALOHA is adopted for the LoRaWAN MAC protocol, its communication collision increases as the number of nodes increases. Therefore, a back-off process becomes necessary, and the power consumption is increased by the back-off process during its operation. In this paper, we propose an enhancement to the LoRaWAN MAC protocol, which enables communication collision avoidance and sleep control of the end nodes.
A note on Pierre Marie (1853–1940)

Objectives: To determine the prevalence of cognitive delay and possible associated dysmorphic features in children exposed to antiepileptic drugs (AEDs) in utero. Design: Retrospective study of children born to mothers with epilepsy. Setting: Regional epilepsy clinics in Liverpool and Manchester, UK. Participants: Children aged between 6 months and 16 years born to mothers with epilepsy. Main outcome measures: Structured interviews, hospital records, clinical examination, and psychometric tests (Wechsler) were used to assess exposure and intelligence quotient (IQ). Blinded assessment of photographs was used to score children with characteristic dysmorphic features. Results: A total of 249 children aged 6 and over were studied: 41 were exposed to sodium valproate, 52 to carbamazepine, 21 to phenytoin, 49 to polytherapy, and 80 were unexposed. Mean verbal IQ was significantly lower in the valproate group compared to unexposed and other monotherapy groups. Multiple regression analysis showed that both valproate exposure and frequent tonic-clonic seizures in pregnancy were significantly associated with a lower verbal IQ despite adjusting for other confounding factors. There was a significant negative correlation between dysmorphic features and verbal IQ in children exposed to valproate. Conclusions: This study identifies valproate as a drug carrying potential risks for developmental delay and cognitive impairment and is the first to suggest that frequent tonic-clonic seizures have a similar effect. Our results need to be interpreted with caution given their retrospective nature. Women with epilepsy need careful counselling about individual risk benefit of AED treatment before pregnancy.
Recursive Maximum Margin Active Learning

Active learning is an important technique to alleviate the problem when there is abundant unlabeled data but scarce labeled data. It aims to choose the most valuable samples to label in order to build powerful predictive models with minimal supervision. However, under the setup of active learning, when the data are characterized by high-dimensional features, it will be difficult to get a reliable estimate on the model parameters, as the labeled data are limited. Most existing works tackle this problem by learning a low-dimensional representation of data before active learning, but it cannot guarantee to obtain promising results, as traditional feature extraction techniques and active learning algorithms are designed independently. In this paper, we propose an efficient hybrid active learning algorithm, called Recursive Maximum Margin Active Learning. We optimize the active learning and semi-supervised feature extraction under a unified framework to tackle the high-dimensional features’ problems and select the most representative samples in the low-dimensional space. By introducing the semi-supervised maximum margin criterion into active learning, we can conduct sample selection and feature extraction recursively at each iteration of active learning to learn more accurate models. The extensive experimental results show that the proposed method outperforms several state-of-the-art active learning methods on publicly available datasets.
Using Twitter to Infer User Satisfaction With Public Transport: The Case of Santiago, Chile

User satisfaction is an important aspect to consider in any public transport system, and as such, regular and sound measurements of its levels are fundamental. However, typical evaluation schemes involve costly and time-consuming surveys. As a consequence, their frequency is not enough to properly and timely characterize the satisfaction of the users. In this paper, we propose a methodology, based on Twitter data, to capture the satisfaction of a large mass of users of public transport, allowing us to improve the characterization and location of their satisfaction level. We analyzed a massive volume of tweets referring to the public transport system in Santiago, Chile (Transantiago) using text mining techniques, such as sentiment analysis and topic modeling, in order to capture and group bus users’ expressions. Results show that, although the level of detail and variety of answers obtained from surveys are higher than the ones obtained by our method, the amount of bus stops and bus services covered by the proposed scheme is larger. Moreover, the proposed methodology can be effectively used to diagnose problems in a timely manner, as it is able to identify and locate trends, and issues related to bus operating firms, whereas surveys tend to produce average answers. Based on the consistency and logic of the results, we argue that the proposed methodology can be used as a valuable complement to surveys, as both present different, but compatible characteristics.
A Robust Sparse System Identification via Integrating an Individualized Time-Varying Stepsize Adjustments

In this paper, we present a new adaptive filter solution that mitigates the impact of highly correlated input signals to a system with sparse and long impulse response, reduces negative effect of sparsity measures on the performance of sparse related algorithms, and improves the trading-off between achieving a fast convergence rate and attaining a small steady-state MSE. We based this new solution on a recently proposed technique that innovatively employs a tap-individualized time adjusting stepsize. Based on the total input power and input signal associated with the underlying tap, each tap of the adaptive filter has its own variable stepsize adjustment. This adjustment is based on a logarithmic formulation designed to deliver a consistent reduction in stepsize in each iteration by leveraging the low input excitation and, at the same time, compressing the high input excitation into a relatively moderate input range. Simulation results show that the proposed algorithm delivers a superior performance, at various system sparsity levels and input types, compared with known sparse-aware algorithms.
High-performance of the Multiplication over the Quadratic Extension in Montgomery Domain for the Pairing Cryptosystems

In our paper we propose an architecture to calculate the operation of multiplication over $\mathbb{F}_{p^{2}}$ in the tower extension field arithmetic for pairing. This tower used in pairings based cryptography. Pairing is becoming the most popular for Public Key Cryptosystems. For the same security level the cryptosystem pairing use much smaller key length than other cryptosystems like RSA. We propose an efficient and optimized architectures that considerably accelerate the calculation of Miller algorithm in pairing-based cryptography. In our architecture we use the Montgomery multiplication based on the systolic architecture published in [17]. We propose an efficient design to implement multiplication over $\mathbb{F}_{p^{2}}$. In this work our architecture is designed with VHDL in order to use the component flexible DSP of Xilinx FPGAs. This design of multiplication is scalable and depends on the size of words.
A Deep Reinforcement Learning based Mechanism for Cell Outage Compensation in 5G UDN

Ultra Dense Networks (UDN) have become one of the key technologies for 5G wireless communications, which can meet the requirements of high-traffic, high-density wireless terminal access. Compared with LTE, there are a large number of heterogeneous cells in UDN. If a failure occurs and can't be alleviated its effect in time, it will lead to a significant drop in network performance. Therefore, the cell outage compensation (COC) problem in the UDN is very important. Although deep reinforcement learning (DRL) has been applied to many scenarios related to the self-organizing network (SON), there are fewer applications for cell outage compensation. In this paper, aiming at the cell outage scenario in the UDN with the goal of maximizing the sum of the throughput of all users while meeting service quality demands of each mobile user, and present a framework based on DRL to solve it. Specifically, we first allocate compensation users to adjacent BSs by using the K-means clustering algorithm, then apply a deep neural network (DNN) to approximate action-value function. The simulation results show that the algorithm converges quickly and tends to be stable, and reach 99.53% of the maximum throughput. It verifies the efficiency of the DRL-based framework and its effectiveness in meeting the requirement of user rates and handling cell interrupt compensation.
tACS generator as method for evaluating EEG electrodes: Initial validation using pig skin

Electroencephalogram (EEG) systems commonly used in laboratory environments are rapidly moving towards real-world applications. In this paradigm shift, new methods for recording and classifying EEG signals are necessary. However, it is challenging to validate the efficacy of new electrodes or other data acquisition components when the target signal cannot be controlled. Here, we propose and validate a method for using an attenuated tACS unit to generate a ground-truth signal in conjunction with porcine skin to determine electrode effectiveness. We highlight the utility of this approach by objectively comparing the signal quality of a chirp signal though porcine skin as measured by four different EEG electrodes, three "dry" and a standard hydrogel. We believe a similar approach could be used with transdermal signals on live humans to observe effects of motion, electrode type, or environment on EEG in order to improve design for use in noisy, non-laboratory settings.