Miracle-dz/UniFAR-FTUs huggingface dataset:
Guided Genetic Algorithm for Information Diffusion Problems

Information diffusion is a process that involves the propagation of an arbitrary signal (message) in an environment. In the area of social networks, it is often associated with influence maximization. Influence maximization consists in the search for an optimum set of $k$ network nodes (seed sets) that trigger the activation of a maximum total number of remaining network nodes according to a chosen propagation model. It is an attractive research topic due to its well–known difficulty and many practical applications. Influence maximization can be used in various areas spanning from social network analysis and data mining to practical applications such as viral marketing and opinion making. Formally, it can be formulated as a subset selection problem. Because of the proven hardness of the influence maximization problem, many metaheuristic and evolutionary methods have been proposed to tackle it. This paper presents and evaluates a new genetic algorithm for influence maximization. It is based on a recent genetic algorithm for fixed–length subset selection and takes advantage of the knowledge of the environment. The evolutionary algorithm is in this approach executed with respect to network properties and the probability that vertices with chosen properties are selected is increased. The experiments show that this approach improves the results of the evolutionary procedure and leads to the discovery of better seed sets.
Using Spreadsheet-defined Rules for Reasoning in Self-Adaptive Systems

Using rules to capture adaptation knowledge is a common approach for self-adaptive systems. Rule-based reasoning, i.e., using rules to analyze and plan adaptations, has several advantages: (i) it is easy to implement, (ii) it offers fast reasoning, and (iii) it works on resource-spare systems as historical knowledge is not required. Hence, the needed computational power is low and it perfectly suits systems in the pervasive IoT domain. However, the codification of rules poses a challenge to the system design. Existing approaches often require a specific syntax or programming language. Additionally, some approaches force the developer to customize the reasoning mechanism, hence, to reimplement parts of the reasoning. To address these shortcomings, we propose a reusable approach for rule-based reasoning in this paper. Rules can be defined in a spreadsheet without the need to neither learn a syntax nor implement a single line of code. We evaluate the benefits of our approach in two case studies conducted by Master students as well as a quantitative evaluation.
Design and Implementation of Flexible and Reconfigurable SDF-Based FFT Chip Architecture With Changeable-Radix Processing Elements

In this paper, we propose a flexible and reconfigurable changeable-radix fast Fourier transform (FFT) hardware architecture. It aims to support 48 different FFT sizes and up to 4096 FFT points, which are defined in current 3GPP-LTE communication system. The built-up design structure is primarily constructed on a radix-52 basis of single-path delay feedback FFT and up to 18 various changeable radixes of FFT processing. A design technique of switchable FIFO usage approach is developed to efficiently manage FIFO arrangement for 48 FFT modes. In addition, a design technique of coarse and fine rotating is designed to effectively reduce twiddle-factor circuit area. By using TSMC 40-nm CMOS technology, an FFT ASIC implementation only has a core area occupation of 0.414 mm2 and consumes 49.8 mW in average at maximal working frequency of 526.32 MHz. This innovative design work is competitive as compared to current state-of-the-art works, especially in terms of circuit area cost and power/energy performance evaluation.
Detecting Building Edges from High Spatial Resolution Remote Sensing Imagery Using Richer Convolution Features Network

As the basic feature of building, building edges play an important role in many fields such as urbanization monitoring, city planning, surveying and mapping. Building edges detection from high spatial resolution remote sensing (HSRRS) imagery has always been a long-standing problem. Inspired by the recent success of deep-learning-based edge detection, a building edge detection model using a richer convolutional features (RCF) network is employed in this paper to detect building edges. Firstly, a dataset for building edges detection is constructed by the proposed most peripheral constraint conversion algorithm. Then, based on this dataset the RCF network is retrained. Finally, the edge probability map is obtained by RCF-building model, and this paper involves a geomorphological concept to refine edge probability map according to geometric morphological analysis of topographic surface. The experimental results suggest that RCF-building model can detect building edges accurately and completely, and that this model has an edge detection F-measure that is at least 5% higher than that of other three typical building extraction methods. In addition, the ablation experiment result proves that using the most peripheral constraint conversion algorithm can generate more superior dataset, and the involved refinement algorithm shows a higher F-measure and better visual effect contrasted with the non-maximal suppression algorithm.
Ghanaian Students in TIMSS 2011: Relationship between Contextual Factors and Mathematics Performance

Educational effectiveness research has witnessed some growth in the last three decades because the current era of global competitiveness compels each country to train and equip its citizens with knowledge and skills that would make them successful. Researchers from different countries are investigating factors across various levels within the school system that affect students’ learning outcomes and social development. This study applies a multilevel modelling technique (Hierarchical Linear Modeling) on the TIMSS 2011 mathematics data of the Ghanaian Grade 8 students to examine which student-, classroom/teacher- and school-level variables contributed significantly to the performance of Ghanaian students in mathematics. The results indicated that the education system in Ghana is similar to other education systems where the students’ academic achievement is correlated with a set of students, classroom/teacher and school characteristics. However, unlike other educational systems, the findings of the current study suggested that the difference in students’ achievement in mathematics is mainly due to school factors. It was concluded that the poor performance of Ghanaian students in mathematics is at least partially attributable to inadequate preparation of teachers, emphasis on lower rather than higher thinking skills, inconsistent use of homework, failure to engage students in their learning, lack of progress of girls, lack of students’ interest and confidence in mathematics and students’ lower educational aspiration.
Assessment of MODIS, OMI, MISR and CALIOP Aerosol Products for Estimating Surface Visual Range: A Mathematical Model for Hong Kong

Estimation of atmospheric visibility (VR) using ground and satellite sensors is ineffective under Hong Kong’s complex atmosphere and climate. Therefore, the relationship between columnar Aerosol Optical Depth (AOD) from four space-borne sensors (OMI, MODIS, MISR and CALIOP) and Bext from two visibility-recording stations was evaluated, to recommend an effective satellite-based method and spatial resolution, for estimation of VR over Hong Kong. Since most column-integrated aerosol particle extinction occurs within a mixing layer height (MLH) of 1–3 km, column-based AOD from satellites is expected to give a good indication of surface-level conditions, especially when MLH is a known input. The AOD from both MODIS and MISR showed high correlations with Bext; therefore, both were subjected to rigorous statistical analysis along with climatic data to simulate visibility. The best estimate of ground visibility was obtained from MODIS AOD combined with surface-level climatic data, and this explained 84% of the variance in VR, with a low distance error of 0.27 km. Results suggest that the water vapor mixing ratio (Q) alone can explain the combined effect of Atmospheric Pressure (P), Temperature (T) and Relative Humidity (RH) on VR, and that the advection term (VT) alone is sufficient to explain the effects of T, WS and WD on dispersion of aerosols, and hence on VR.
Nonlinear equations with gradient natural growth and distributional data, with applications to a Schrödinger type equation

We obtain necessary and sufficient conditions with sharp constants on the distribution σ for the existence of a globally finite energy solution to the quasi‐linear equation with a gradient source term of natural growth of the form −Δpu=|∇u|p+σ in a bounded open set Ω⊂Rn . Here Δp , p>1 , is the standard p ‐Laplacian operator defined by Δpu= div (|∇u|p−2∇u) . The class of solutions that we are interested in consists of functions u∈W01,p(Ω) such that eμu∈W01,p(Ω) for some μ>0 and the inequality ∫Ω|φ|p|∇u|pdx⩽A∫Ω|∇φ|pdxholds for all φ∈Cc∞(Ω) with some constant A>0 . This is a natural class of solutions at least when the distribution σ is nonnegative. The study of −Δpu=|∇u|p+σ is applied to show the existence of globally finite energy solutions to the quasi‐linear equation of Schrödinger type −Δpv=σvp−1 , v⩾0 in Ω , and v=1 on ∂Ω , via the exponential transformation u↦v=eup−1 .
End-to-end Image Captioning Exploits Distributional Similarity in Multimodal Space

We hypothesize that end-to-end neural image captioning systems work seemingly well because they exploit and learn `distributional similarity' in a multimodal feature space by mapping a test image to similar training images in this space and generating a caption from the same space. To validate our hypothesis, we focus on the `image' side of image captioning, and vary the input image representation but keep the RNN text generation component of a CNN-RNN model constant. Our analysis indicates that image captioning models (i) are capable of separating structure from noisy input representations; (ii) suffer virtually no significant performance loss when a high dimensional representation is compressed to a lower dimensional space; (iii) cluster images with similar visual and linguistic information together. Our findings indicate that our distributional similarity hypothesis holds. We conclude that regardless of the image representation used image captioning systems seem to match images and generate captions in a learned joint image-text semantic subspace.
An Investigational Analysis of DSDV, AODV and DSR Routing Protocols in Mobile Ad Hoc Networks

A Mobile ad hoc network is a gathering of various data transmission node. The nodes in the MANET are movable. Due to this quality of Mobile ad hoc network, the topology is highly dynamic. There is no fixed infrastructure in this type of network. Any node that wants to transmit the data from origin node to sink node in the network, the data has to promote from one node to another node to reach the destination node. In this paper we have done a comparative investigation of three routing protocols in MANET by varying the pause time. The simulations have been made by considering the three parameters throughput, packet delivery ratio and end to end delay. The simulator used for making the comparative analysis NS-2.33. The results show that the DSR protocol outperforms the other two protocols AODV and DSDV in Throughput and packet delivery ratio and DSDV outperform the AODV and DSR in end to end delay.
Building the Semantic Similarity Model for Social Network Data Streams

This paper proposes the model for searching similar collocations in English texts in order to determine semantically connected text fragments for social network data streams analysis. The logical-linguistic model uses semantic and grammatical features of words to obtain a sequence of semantically related to each other text fragments from different actors of a social network. In order to implement the model, we leverage Universal Dependencies parser and Natural Language Toolkit with the lexical database WordNet. Based on the Blog Authorship Corpus, the experiment achieves over 0.92 precision.
Estimation of Rice Height and Biomass Using Multitemporal SAR Sentinel-1 for Camargue, Southern France

The research and improvement of methods to be used for crop monitoring are currently major challenges, especially for radar images due to their speckle noise nature. The European Space Agency’s (ESA) Sentinel-1 constellation provides synthetic aperture radar (SAR) images coverage with a 6-day revisit period at a high spatial resolution of pixel spacing of 20 m. Sentinel-1 data are considerably useful, as they provide valuable information of the vegetation cover. The objective of this work is to study the capabilities of multitemporal radar images for rice height and dry biomass retrievals using Sentinel-1 data. To do this, we train Sentinel-1 data against ground measurements with classical machine learning techniques (Multiple Linear Regression (MLR), Support Vector Regression (SVR) and Random Forest (RF)) to estimate rice height and dry biomass. The study is carried out on a multitemporal Sentinel-1 dataset acquired from May 2017 to September 2017 over the Camargue region, southern France. The ground in-situ measurements were made in the same period to collect rice height and dry biomass over 11 rice fields. The images were processed in order to produce a radar stack in C-band including dual-polarization VV (Vertical receive and Vertical transmit) and VH (Vertical receive and Horizontal transmit) data. We found that non-parametric methods (SVR and RF) had a better performance over the parametric MLR method for rice biophysical parameter retrievals. The accuracy of rice height estimation showed that rice height retrieval was strongly correlated to the in-situ rice height from dual-polarization, in which Random Forest yielded the best performance with correlation coefficient R 2 = 0.92 and the root mean square error (RMSE) 16% (7.9 cm). In addition, we demonstrated that the correlation of Sentinel-1 signal to the biomass was also very high in VH polarization with R 2 = 0.9 and RMSE = 18% (162 g·m − 2 ) (with Random Forest method). Such results indicate that the highly qualified Sentinel-1 radar data could be well exploited for rice biomass and height retrieval and they could be used for operational tasks.
Clinical effectiveness of osteoporosis treatment in older patients: A fracture liaison service-based prospective study

ABSTRACT The clinical efficacy of anti-osteoporotic treatments in old patients is discussed. The aim of this study was to assess if the use of anti-osteoporotic treatments for the secondary prevention of osteoporotic fractures could reduce the risk of refractures in patients over 75 years old in a Fracture Liaison Service. In this population of frail, elderly patients presenting with a recent osteoporotic fracture, we observed that the refracture incidence was similar in the treated group and the untreated group during the first year. However, 30 months after the index fracture, the osteoporosis medication for a year or more reduced the incidence of refractures by 70%.
Antiradical and Xanthine Oxidase Inhibitory Activity Evaluations of Averrhoa bilimbi L. Leaves and Tentative Identification of Bioactive Constituents through LC-QTOF-MS/MS and Molecular Docking Approach

The objective of the present study was to investigate the antiradical and xanthine oxidase inhibitory effects of Averrhoa bilimbi leaves. Hence, crude methanolic leaves extract and its resultant fractions, namely hexane, chloroform, and n-butanol were evaluated for 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging effect and xanthine oxidase inhibitory activity. The active constituents were tentatively identified through LC-QTOF-MS/MS and molecular docking approaches. The n-butanol fraction of A. bilimbi crude methanolic leaves extract displayed significant DPPH radical scavenging effect with IC50 (4.14 ± 0.21 μg/mL) (p < 0.05), as well as xanthine oxidase inhibitory activity with IC50 (64.84 ± 3.93 μg/mL) (p < 0.05). Afzelechin 3-O-alpha-l-rhamnopyranoside and cucumerin A were tentatively identified as possible metabolites that contribute to the antioxidant activity of the n-butanol fraction.
Procedural advantages of a novel coronary stent design with ultra-thin struts and bioabsorbable abluminal polymer coating in an all-comers registry

Introduction The implications of novel drug-eluting stent (DES) design modifications including ultra-thin struts and new concepts of polymer coating for procedural efficacy are still unknown. Aim To evaluate procedural efficacy and short-term safety of a novel DES design. Material and methods In this all-comers registry, 407 consecutive patients were enrolled upon undergoing percutaneous coronary interventions (PCI) with the thin-strut bioabsorbable abluminal polymer-coated SYNERGY stent. These patients were then compared with the previous 407 patients undergoing PCI performed by the same interventionalists using currently established second-generation DES (Promus Element plus, Xience prime, Resolute Integrity). Several clinical and procedural data were compared, and the coronary artery complexity was assessed by the American College of Cardiology/American Heart Association classification and SYNTAX Score. Results The study population consisted of 814 patients. A total of 859 Synergy stents were deployed in 480 target vessels in the Synergy group (n = 407), and 904 stents in 469 vessels in the second-generation DES group (n = 407). Coincidentally, target lesions in the Synergy group (A 2.7%, B1 13.8%, B2 38.6%, C 45.0%) were more complex (p < 0.01) than those in the second-generation DES group (A 4.9%, B1 18.7%, B2 42.3%, C 34.2%). In cases with severe lesions (B2/C), the median contrast agent amount and fluoroscopy time were significantly lower in the Synergy group, indicating improved deliverability (110 ml vs. 150 ml; p < 0.01 and 7.2 min vs. 9.1 min; p = 0.01). Rates of in-hospital major adverse cardiovascular events were comparable between the two groups. Conclusions In an all-comers, real-world PCI population, novel stent design modifications including ultra-thin struts and abluminal bioabsorbable polymer coating are associated with improved procedural performance.
Assessing Virtual Assistant Capabilities with Italian Dysarthric Speech

The usage of smartphone-based virtual assistants (e.g., Siri or Google Assistant) is growing, and their spread has generally a positive impact on device accessibility, e.g., for people with disabilities. However, people with dysarthria or other speech impairments may be unable to use these virtual assistants with proficiency. This paper investigates to which extent people with ALS-induced dysarthria can be understood and get consistent answers by three widely used smartphone-based assistants, namely Siri, Google Assistant, and Cortana. We focus on the recognition of Italian dysarthric speech, to study the behavior of the virtual assistants with this specific population for which no relevant studies are available. We collected and recorded suitable speech samples from people with dysarthria in a dedicated center of the Molinette hospital, in Turin, Italy. Starting from those recordings, the differences between such assistants, in terms of speech recognition and consistency in answer, are investigated and discussed. Results highlight different performance among the virtual assistants. For speech recognition, Google Assistant is the most promising, with around 25% of word error rate per sentence. Consistency in answer, instead, sees Siri and Google Assistant provide coherent answers around 60% of times.
Generic Model-Agnostic Convolutional Neural Network for Single Image Dehazing

Haze and smog are among the most common environmental factors impacting image quality and, therefore, image analysis. This paper proposes an end-to-end generative method for image dehazing. It is based on designing a fully convolutional neural network to recognize haze structures in input images and restore clear, haze-free images. The proposed method is agnostic in the sense that it does not explore the atmosphere scattering model. Somewhat surprisingly, it achieves superior performance relative to all existing state-of-the-art methods for image dehazing even on SOTS outdoor images, which are synthesized using the atmosphere scattering model. Project detail and code can be found here: this https URL
Overexpression of Epidermal Growth Factor Receptor (EGFR) and HER-2 in Bladder Carcinoma and Its Association with Patients’ Clinical Features

Background The aim of this study was to determine the expression of EGFR/HER-2 and investigate their association with patients’ clinical features in bladder transitional cell carcinoma (BTCC). Material/Methods Immunohistochemistry was utilized in our study to explore the expression of EGFR/HER-2 of 56 human bladder cancer samples and 10 normal bladder samples. Results EGFR and HER-2 expressions were both significantly higher in bladder transitional cell carcinoma (BTCC) than that in non-cancer bladder samples; the EGFR positivity rate was 55.4% among BTCC samples and 37.5% for HER-2a. A statistically significant correlation was also present between the increasing EGFR or HER-2 expression levels and the clinical stages, pathologic grades, and tumor recurrence. The expression level of EGFR increased along with higher clinical stages and pathologic grades of BTCC, and the obviously increased expression of HER-2 was statistically associated with clinical stages and tumor recurrence. In addition, the expression level of HER-2 increased along with the higher clinical stage of BTCC. EGFR expression and HER-2 levels were positively associated in BTCC samples. Conclusions Our findings demonstrate that high EGFR and HER-2 expressions are dramatically increased in the BTCC tissues and are closely related to the clinical stages, pathologic grades, and tumor recurrence. Therefore, the evaluation of EGFR and HER-2 expression in BTCC may contribute to identifying patients who are at increased risk of disease progression and recurrence.
Metabolic Stability and Metabolite Characterization of Capilliposide B and Capilliposide C by LC–QTRAP–MS/MS

Capilliposide B (LC-B) and Capilliposide C (LC-C), two new triterpenoid saponins extracted from Lysimachia capillipes Hemsl, exhibit potential anticancer activity both in vitro and in vivo. However, their metabolic process remains unclear. In this study, the metabolic stability of LC-B, LC-C, and Capilliposide A (LC-A, a bioactive metabolite of LC-B and LC-C) was investigated in human, rat, and mouse liver microsomes, respectively. Thereafter, their metabolites were identified and characterized after oral administration in mice. As a result, species difference was found in the metabolic stability of LC-B and LC-C. All three compounds of interest were stable in human and rat liver microsomes, but LC-B and LC-C significantly degraded in mouse liver microsomes. The metabolic instability of LC-B and LC-C was mainly caused by esterolysis. Moreover, 19 metabolites were identified and characterized in mouse biological matrices. LC-B and LC-C mainly underwent deglycosylation and esterolysis, accompanied by dehydration, dehydrogenation, and hydroxylation as minor metabolic reactions. Finally, the metabolic pathway of LC-B and LC-C in mice was proposed. Our results updated the preclinical metabolism and disposition process of LC-B and LC-C, which provided additional information for better understanding efficacy and safety.