Miracle-dz/UniFAR-FTUs huggingface dataset:
Trends and Challenges in Lifelong Machine Learning Topic Models

Lifelong learning is the newly introduced approach to machine learning. It follows the true essence of developing learning based intelligent agents. The idea is to build models that can learn with experience in order to become more knowledgeable, following a continuous learning mechanism. It has similarities with other approaches including online learning, transfer learning, reinforcement learning and cumulative learning but has maintained its differences. Lifelong topic modeling refers to the use of lifelong learning approach with topic modeling. The objective is to develop topic models that incorporate past knowledge to generate topics that easily represent the underlying concepts discussed in the dataset. The past knowledge is in the form of a collective sense that helps to give meaningful topics even to domains that have limited or noisy sample data. This review paper puts emphasis on the effectiveness of lifelong learning approach in comparison to the traditional machine learning approaches. It discusses the strengths of lifelong learning and the progress that it has made with topic models as a specific case. In addition the challenges faced by lifelong learning topic models are discussed and the new avenues are highlighted as decisive factors towards the future of lifelong learning approach.
A Real Approach on Web Systems Packaging and Delivery

Web Engineering has been a subtopic of Software Engineering which concerns the software development of web applications. In spite of the fact that Software Engineering has covered main sub-processes for many kind of systems, the packaging and delivery sub-process for web applications has not received enough attention. This paper presents two research topics, first, we conducted a systematic mapping review in order to elicit current contributions on packaging and delivery for sub-processes and, secondly, we conducted an elicitation process from a real setting of the current packaging and delivery sub-process, getting a refined map of activities as a contribution to this topic.
Alzheimer’s disease Classification from Brain MRI based on transfer learning from CNN

Various Convolutional Neural Network (CNN) architecture has been proposed for image classification and Object recognition. For the image based classification, it is a complex task for CNN to deal with hundreds of MRI Image slices, each of almost identical nature in a single patient. So, classifying a number of patients as an AD, MCI or NC based on 3D MRI becomes vague technique using 2D CNN architecture. Hence, to address this issue, we have simplified the idea of classifying patients on basis of 3D MRI but acknowledging the 2D features generated from the CNN framework. We present our idea regarding how to obtain 2D features from MRI and transform it to be applicable to classify using machine learning algorithm. Our experiment shows the result of classifying 3 class subjects patients. We employed scratched trained CNN or pretrained Alexnet CNN as generic feature extractor of 2D image which dimensions were reduced using PCA+TSNE, and finally classifying using simple Machine learning algorithm like KNN, Navies Bayes Classifier. Although the result is not so impressive but it definitely shows that this can be better than scratch trained CNN softmax classification based on probability score. The generated feature can be well manipulated and refined for better accuracy, sensitivity, and specificity.
Impact of Isolation between Solar Panel Elements on RIME Performance aboard JUICE

The independent ESA mission JUICE (JUpiter ICy moon Explorer) has the goal to explore Jupiter and three of its Galilean moons. The spacecraft (S/C) will launch in the year 2022 and arrives the Jovian system in the year 2030. RIME (Radar for Icy Moons Exploration) and other experiments aboard JUICE will analyse the celestial bodies. Some selected scientific objectives will be the characterization of Ganymede, Europa and Callisto as planetary objects as well as the exploration of potential habitats. Moreover, the Jovian system shall be investigated as an archetype for gas giants.
PIMSim: A Flexible and Detailed Processing-in-Memory Simulator

With the advent of big data applications and new process technologies, Process-in-Memory (PIM) attracts much attention in memory research as the architecture studies gradually shift from processors to heterogeneous aspects. How to achieve reliable and efficient PIM architecture modeling becomes increasingly urgent for the researchers, who want to experiment on critical issues from detailed implementations of their proposed PIM designs. This paper proposes PIMSim, a full-system and highly-configurable PIM simulator to facilitate circuit-, architecture- and system-level researches. PIMSim enables architectural simulation of PIM and implements three simulation modes to provide a wide range of speed/accuracy tradeoffs. It offers detailed performance and energy models to simulate PIM-enabled instructions, compiler, in-memory processing logic, various memory devices, and PIM coherence. PIMSim is open source and available at https://github.com/vineodd/PIMSim.
Wireless channel simulation using geometrical models extrated from point clouds

In recent years, point clouds obtained from laser scanning devices have been used in the simulation of the wireless channel; these point clouds represent with high accuracy the environment leading to an improvement in the channel prediction. However, the estimation of specular replicas using point clouds is a difficult task. In this work, flat rectangular surfaces of the environment elements are extracted from a point cloud producing a complete geometrical model. This model is used in a ray tracing tool that easily estimates the specular components, whereas diffuse components are estimated using points sampled from the geometrical model.
Establishment of loop-mediated isothermal amplification for rapid detection of Pseudomonas aeruginosa

Pseudomonas aeruginosa is one of the three most pathogenic bacteria that frequently cause life-threatening opportunistic human infections, pneumonia, and lower respiratory tract infections in immunocompromised hosts, particularly in the burns ward. The present study aimed to establish a loop-mediated isothermal amplification (LAMP) method for the rapid and sensitive detection of P. aeruginosa-specific gene hypothetical protein (GenBank ID: 882161). The gene was obtained through local and online BLAST, and specific primers were designed for this gene. Reaction conditions were optimized at 65°C for 30 min and 80°C for 2 min, whereas the reaction system contained 5.2 mM Mg2+, 8 U Bst 2.0 DNA polymerase, 1.4 mM deoxyribonucleotide and 0.2 and 1.6 µM of the outer and inner primers, respectively. The LAMP method was evaluated using 150 P. aeruginosa and 170 non-P. aeruginosa strains. Positive reactions were observed on 150 P. aeruginosa strains, whereas all non-P. aeruginosa strains exhibited negative results. Plasmids with the specific gene and mouse blood with P. aeruginosa were used for sensitivity assay. The detection limit of LAMP was 1 bacterium/reaction. Results indicated that the LAMP method targeted to hypothetical protein is a fast, specific, sensitive, inexpensive and suitable method for detection of P. aeruginosa.
Rigorous Modal Analysis of Electromagnetic Coupling Between Circular Horns

This paper describes a rigorous modal analysis of electromagnetic coupling between circular horns. The Generalized Scattering Matrix (GSM) of each horn is obtained by the hybrid boundary contour mode-matching (BCMM) technique, and then connected by free-space spherical wave transmission lines, leading to a global GSM description. Two numerical examples, with respect to horns of the same shape and different gains, respectively, are proposed to demonstrate the accuracy and efficiency of the hybrid method.
Focusing High-Resolution High Forward-Looking Bistatic SAR With Nonequal Platform Velocities Based on Keystone Transform and Modified Nonlinear Chirp Scaling Algorithm

This paper presents a focusing solution for high-resolution high forward-looking bistatic synthetic aperture radar with nonequal platform velocities (NPV-BFSAR). In NPV-BFSAR, due to the high-resolution SAR and high forward-looking angle, range cell migration, and two-dimensional spatial variation are more serious. To solve this problem, a modified imaging algorithm based on second-order keystone transform (KT) and modified nonlinear chirp scaling (NLCS) method are proposed. The second-order KT method can correct the linear range cell migration and reduce the strong coupling between azimuth and range. The proposed modified azimuth NLCS algorithm is more accurate than traditional NLCS algorithm on eliminating the variation of azimuth reference function that can improve the focus quality of edge targets. Finally, some simulation results confirm the efficiency of the proposed algorithm.
A Composite Structure of In Situ Cross-Linked Poly(Ionic Liquid)s and Paper for Humidity-Monitoring Applications

This paper describes a humidity sensor fabricated by cross-linking poly(ionic liquid)s (PILs) in the cellulose fibers of printing paper via an in situ photoinitiated “thiol-ene” click reaction. The resultant sensors inherit the strong hydrophilicity and high ionic conductivity of the cellulose and PILs, respectively, which are the most important factors for a high-performance impedance-type humidity sensor. The introduction of PILs improved the sensitivity, hysteresis, response speed, and stability of the paper-based devices. The optimal paper-based device sensitivity increased by a factor of 1.4 after adding PILs, and the response time decreased to 25 s, which is comparable to that of the best reported paper-based humidity sensors.
The high-sensitivity modified Glasgow prognostic score is superior to the modified Glasgow prognostic score as a prognostic predictor for head and neck cancer

Background There is increasing evidence that the inflammatory indices of modified Glasgow prognostic score (mGPS) and high-sensitivity mGPS (HS-mGPS) play important roles in predicting the survival in many cancer; however, evidence supporting such an association in head and neck cancer (HNC) is scarce. Materials and Methods We evaluated the impact of the mGPS and HS-mGPS on the overall survival (OS) in 129 patients with HNC treated at Aichi Cancer Center Central Hospital from 2012-2013. The mGPS was calculated as follows: mGPS of 0, C-reactive protein (CRP) ≤1.0 mg/dl; 1, CRP >1.0 mg/dl; 2, CRP>1.0 mg/dl and albumin <3.5 mg/dl. Regarding the HS-mGPS, the CRP threshold level was set as 0.3 mg/dl. Hazard ratios (HRs) and 95% confidence intervals (95% CIs) were estimated by Cox proportional hazard models after adjusting for potential confounders. Results The prognosis of HNC worsened significantly as both the mGPS and HS-mGPS increased in a univariate analysis. After adjusting for covariates, the HS-mGPS was significantly associated with the OS (adjusted HR for HS-mGPS of 2 compared to an HS-mGPS of 0 [HRscore2-0] 3.14 [95% CI: 1.23-8.07], Ptrend < 0.001), while the mGPS was suggested to be associated with the survival (HRscore2-0 2.37 [95% CI:0.89-6.33], Ptrend = 0.145). Even after stratification by clinical covariates, these associations persisted. Conclusion We conclude that the HS-mGPS is useful as an independent prognostic factor in HNC.
Machine Learning for Wide Area Surveillance from Aerial Platforms

Legacy detection approaches in persistent wide area surveillance (WAS) rely on tractable stochastic models to predict clutter and target behaviour to allow the generation of detector structures. Unfortunately, actual clutter properties encountered during radar and optical WAS are frequently observed to diverge significantly from the proposed statistical models. This results in degraded detection performance. machine learning (ML) is proposed as a potential technique to help capture complex clutter behaviour so as to improve detection performance. In this paper some prior motivating applications of ML for WAS are discussed. Ongoing challenges in the application of ML techniques to WAS are identified along with recommendations for future implementations.
A comprehensive review on tyrosinase inhibitors

Abstract Tyrosinase is a multi-copper enzyme which is widely distributed in different organisms and plays an important role in the melanogenesis and enzymatic browning. Therefore, its inhibitors can be attractive in cosmetics and medicinal industries as depigmentation agents and also in food and agriculture industries as antibrowning compounds. For this purpose, many natural, semi-synthetic and synthetic inhibitors have been developed by different screening methods to date. This review has focused on the tyrosinase inhibitors discovered from all sources and biochemically characterised in the last four decades.
Reliability Assessment of Power Converters with Partial Redundancy and Variable Power Distribution

Conventional reliability assessment method mainly consists of three steps: building component-level reliability model, establishing system-level reliability model and assessing the reliability by reliability metrics. However, this traditional method have some limitations for power converters with partial redundancy and variable power distribution: for the component-level reliability analysis, it is very difficult to determine the importance of the component failure rate; For the system-level reliability analysis, the existing models do not work well for the power converters with partly redundant components; For the conventional reliability metrics, the converters reliability cannot be fully described when the power distribution of the converters varies. In order to solve these problems, three improvements are proposed by this paper. Firstly, failure rates of all components are compared and the important ones are chosen. Secondly, a system-level reliability hybrid model, which considering partial redundancy, is proposed. Thirdly, a new reliability metric MTTFr is presented to evaluate the reliability with variable power distribution. In this paper, a compact motor drive of the electric vehicle is utilized to validate the superiority of the proposed improvements.
Dentition status and treatment needs and its correlation with oral health-related quality of life among men having sex with men and transgenders in Pune city: A cross-sectional study

Introduction: Dental diseases are universal in nature. Their presence and severity may vary from one individual to another and is also affected by multiple factors such as gender, knowledge, attitude and the affordability of dental/oral care. Gay, bisexual and transgender community is one among the socially disadvantaged community in our country. The present study was carried out in the city of Pune, India, to evaluate the prevalence of dentition status and the treatment needs among the men who have sex with men (MSMs) and transgenders (TGs) and its correlation with the oral health-related quality of life. Materials and Methods: The sample size was 270. Convenience sampling was followed. The WHO oral health assessment for 1997 was used to record dentition status and treatment needs. Oral Health Impact Profile-14 was used to record the oral health-related quality of life. SPSS 22.0 (Chicago, IL, USA) was used for statistical analysis. All P < 0.05 were considered to be statistically significant. Results: There was no statistically significant difference seen across the various domains in the overall impact of the oral quality of life. There was a significant correlation between the decayed teeth, missing teeth and overall decayed, missing and filled teeth scores with respect to the seven domains. Problems arising due to past dental experience (dental caries), lost natural tooth structures replaced with restorative material and a cumulative impact significantly affected the outcome of the oral health-related quality of life of the participants. Conclusion: There was an alteration in quality of life of the MSMs and TGS with respect to dental caries. This calls for an intervention that is tailor-made to meet the needs of this group.
The effect of antimicrobial additives on the properties of dental glass-ionomer cements: a review

Abstract Aim: The aim of this article is to review the literature on the use of antimicrobial additives in glass-ionomer dental cements. Method: An electronic search between 1987 and the end of 2017 was performed using PubMed, Web of Science and Google search engines with the terms glass-ionomer, glass polyalkenoate, antibacterial and antimicrobial as the key words. The search was refined by excluding the majority of references concerned with cement antimicrobial properties only. Extra papers already known to the authors were added to those considered. Results: A total of 92 relevant articles have been cited in the review of which 55 are specifically concerned with the enhancement of antibacterial properties of glass-ionomers, both conventional and resin-modified, with additives. In addition, information is included on the uses of glass-ionomers and the biological properties of the antibacterial additives employed. There are several reports that show that additives are typically released by diffusion, and that a high proportion is usually left behind, trapped in the cement. Additives generally increase setting times of cements, and reduce mechanical properties. However, smaller amounts of additive have only slight effects and the longer-term durability of cements appears unaffected. Conclusion: Modified glass-ionomer cements seem to be acceptable for clinical use, especially in the Atraumatic Restorative Treatment (ART) technique.
Robust Voltage Resonance Feedforward Control Strategy for LCL-type Grid-connected Inverters under Weak Grid Condition

The quasi-proportional resonance feedforward (QPRF) control strategy was proposed to enhanced the robust ability of LCL-type grid connected in weak grid condition. The equivalent circuit model was developed and impedance analysis method(IAM) was adopted to analyze the stability of the control system. The gain at low frequency and the phase margin at cutoff frequency were detailed analyzed compared with the traditional proportional feedforward (PF) strategy in this paper. It was obtained that the robust ability of QPRF was enhanced than PF. And the gain of low frequency was ensured. Finally, results of simulation were given to verify the validity and feasibility of the proposed strategy.
A Circularly Polarized Magneto-Electric Dipole Leaky Wave Antenna with Broadside Radiation Based on SIW Technology

This paper presents a circularly polarized magnetoelectric (ME) dipole leaky wave antenna (L W A). The proposed antenna characterizes its broadside radiation and contains circular polarization when beam scans from backward to forward as frequency increases. Substrate Integrated Waveguide (SIW) is employed as the basic transmission line to guide electromagnetic waves. Impedance matching technique is adopted to eliminate the open-stopband (OSB) effect, which produces a discontinuity for a seamless L W A. A ME dipole layer is added to realize circular polarization (CP). Simulations demonstrate that the scanning range of the proposed antenna is from −12° to +12° and the axial ratio (AR) is below 3dB between 21GHz and 26GHz.
A Dual-Circularly Polarized Compact Feed Horn at 5G Millimeter-Frequency for Reflector Application

A high-performance feed horn with dual circular polarization is proposed at 5G millimeter-Wave (mm Wave) band for reflector antenna applications. The feed consists of two rectangular ports, a cylindrical waveguide, a TEd11polarizer and a conical horn. This feed horn is designed to operate within a frequency band of 50GHz to 53GHz. Simulations shows that the feed has inter-port isolation below −20dB, port reflection coefficients below −15dB, axial ratios below 0.2dB at the center of the beam and polarization isolations over 24dB. Furthermore, the feed is easy to fabricate. This feed is being manufactured currently and experimental results will be presented during the symposium.
Application of Convolutional Neural Network in Target Detection of Millimeter Wave Imaging

This article focuses on the establishment of convolutional neural network model to achieve the detection of human concealment in millimeter wave images. The convolutional neural network is applied to the image data set for detection training, pictures are randomly selected for identification, and the target location is marked. This paper proves that convolutional neural network can be applied as a feasible general image detection technology to different detection problems.
Stimulation of tolerogenic dendritic cells using dexamethasone and 1,25-dihydroxyvitamin D3 represses autologous T cell activation and chondrocyte inflammation

Human osteoarthritis (OA) has been reclassified as a systemic musculoskeletal disorder involving activation of the innate and adaptive immune systems. Elevated pro-inflammatory cytokines may serve a key function in the development of the disease. 1,25-Dihydroxyvitamin D3 and dexamethasone (vitD3/Dex) may inhibit inflammation by acting on tolerogenic dendritic cells (tolDCs) in chronic inflammatory conditions. In the present study, DCs were isolated from peripheral blood mononuclear cells of patients with OA. DCs expressing high levels of co-stimulatory molecules maintain a tolerogenic phenotype under stimulation with LPS, which promotes DC maturation to generate tolDCs. When vitD3/Dex were added in the current study, the tolDCs produced pro-inflammatory cytokines, including low levels of tumor necrosis factor-α, interleukin (IL)-1β, IL-6 and IL-10, and high levels of transforming growth factor-β. However, when vitD3/Dex were added to DCs without LPS stimulation, the levels of IL-10 were high. DCs with LPS stimulation increased the percentage of T-cells that produced IFN-γ and IL-17 and DCs with vitD3/Dex treatment alone increased the percentage of T-cells that produced IL-10 and FoxP3. However, those cytokines decrease in DCs co-processed with LPS and vitD3/Dex. The IL-10 release by the stimulated T cells was indicated to repress autologous T cell proliferation via soluble IL-10 and cell-cell contact. Furthermore, tolDCs and regulatory T cells suppressed matrix metalloproteinase (MMP)-1 and MMP-13 secretion by chondrocytes. Additionally, Akt and p38 mitogen-activated protein kinase signaling were demonstrated to be involved in the regulatory effects of Dec and vitD3 in DCs. The present findings suggest a novel mechanism underlying the beneficial effects of tolDCs, particularly in association with the pathogenesis of OA.
Towards the development of new DC railway system architectures with the use of superconducting devices

The new railway network envisioned by the French company SNCF must consider ultra-urban requirements and consequently review its architecture. Discovered in 1911, superconductivity is today used in a large range of high power devices and integrated in many domains of the society. Superconducting materials are able to carry high current without dissipating any joules losses during its operation. These materials can transmit current density 100 times higher than copper and they are interesting for applications requiring high transmitted currents. For example, low voltage DC power superconducting cables are well suited where the place for installation is limited such as urban railways grids. Taking the benefit from its transition from the superconducting to the resistive state, an innovative equipment to protect against faults is possible. In this article, two main devices are presented to upgrade the existing grid i.e. high power superconducting cables and fault current limiters. After a short presentation of the concept of superconductivity, these two apparatuses are detailed and their advantages discussed. Two envisioned applications in the railway grid are then presented and their benefits detailed.