Miracle-dz/UniFAR-FTUs huggingface dataset:
Fusing Recommendations for Social Bookmarking Websites

Social bookmarking websites, which allow their users to store and access their bookmarks online through a Web interface, are rapidly growing in popularity. Recommender systems are a successful remedy to the information overload accompanying the popularity-driven explosive growth in content. They are designed to automatically identify which unseen content might be of interest to a particular user, based on his or her past preferences. In this article, we focus on the task of item recommendation: using recommender systems to locate interesting content for users of social bookmarking websites. Item recommendation for social bookmarking has only recently begun to attract more attention from researchers, and much of the previous work suffers from a lack of comparisons between the different available approaches. This means that it is difﬁcult to determine exactly what the best practices are for item recommendation on social bookmarking websites. In this article, we address this issue by comparing and evaluating eight different recommendation approaches on four different data sets from two different domains. We ﬁnd that approaches that use tag overlap and metadata are competitive with each other and provide better results for social bookmarking data sets than the transaction patterns that are used traditionally in recommender systems research. In addition, we investigate how to fuse together different recommendation approaches to further improve recommendation accuracy. We ﬁnd that fusing recommendations can indeed produce signiﬁcant improvements in recommendation accuracy. We also ﬁnd that it is often better to combine approaches that use different data representations, such as tags and metadata, than it is to combine approaches that only vary in the algorithms they use. The best results are obtained when both of these aspects of the recommendation task are varied in the fusion process. Our ﬁndings can be used to improve the quality of recommendations not only on social bookmarking websites, but conceivably also on websites that offer annotated commercial content.
K-Alpha Calculator–Krippendorﬀ’s Alpha Calculator: A user-friendly tool for computing Krippendorﬀ’s Alpha inter-rater reliability coeﬃcient

Krippendorﬀ’s Alpha is a measure for assessing inter-rater reliability, facilitating triangulated evaluations among multiple raters. Indeed, Krippendorﬀ’s Alpha is key in validating the dependability of human assessments, thereby reinforcing the robustness of human-based choices in contexts where interpretive variability could otherwise undermine the research outcomes. Despite its versatility across various data types, the procedure for computing this coeﬃcient might limit its applicability for researchers unfamiliar with specialised statistical software. Addressing this issue, this paper introduces the “K-Alpha Calculator ”—Krippendorﬀ’s Alpha Calculator —a freely accessible, user-friendly, web application available at https://www.k-alpha.org designed for easy computation of Krippendorﬀ’s Alpha. By oﬀering a web interface without any software dependency, the K-Alpha Calculator seeks to promote the broader integration of such a reliability metric in research processes. We envisage that the tool’s contribution could support researchers in enhancing methodological rigour, fostering conclusions are rooted in triangulated interpretations and assessments. The K-Alpha Calculator is both a computational tool and an educational resource,
Eﬀicient saliency detection using regional color and spatial information

In this paper, we propose an efficient saliency model using regional color and spatial information. The original image is first segmented into a set of regions using a superpixel segmentation algorithm. For each region, its color saliency is evaluated based on the color similarity measures with other regions, and its spatial saliency is evaluated based on its color distribution and spatial position. The final saliency map is generated by combining color saliency measures and spatial saliency measures of regions. Experimental results on a public dataset containing 1000 images demonstrate that our computationally efficient saliency model outperforms the other six state-of-the-art models on saliency detection performance.
Sample Algorithms in Multi-start Heuristics for the Switch Allocation Problem

We study the problem of allocating switches in electrical distribution networks to improve their reliability. We present a sample construction algorithm and a sample local search for this problem. We compare these approaches with other construction and local search strategies within heuristics that combine them. We present and analyze experimental results, showing that sample approaches are inexpensive and ﬁnd solutions of good quality.
Shrub expansion raises both aboveground and underground multifunctionality on a subtropical plateau grassland: coupling multitrophic community assembly to multifunctionality and functional trade-off

Introduction Shrubs have expanded into grasslands globally. However, the relative importance of aboveground and underground diversity and the relative importance of underground community assembly and diversity in shaping multifunctionality and functional trade-offs over shrub expansion remains unknown. Methods In this study, aboveground and underground multitrophic communities (abundant and rare archaea, bacteria, fungi, nematodes, and protists) and 208 aboveground and underground ecosystem properties or indicators were measured at three stages (Grass, Mosaic, Shrub) of shrub expansion on the Guizhou subtropical plateau grassland to study multifunctionality and functional trade-offs. Results The results showed that shrub expansion significantly enhanced aboveground, underground, and entire ecosystem multifunctionality. The functional trade-off intensities of the aboveground, underground, and entire ecosystems showed significant V-shaped changes with shrub expansion. Shrub expansion improved plant species richness and changed the assembly process and species richness of soil abundant and rare subcommunities. Plant species diversity had a greater impact on multifunctionality than soil microbial diversity by more than 16%. The effect of plant species diversity on functional trade-offs was only one-fifth of the effect of soil microbial diversity. The soil microbial species richness did not affect multifunctionality, however, the assembly process of soil microbial communities did. Rather than the assembly process of soil microbial communities, the soil microbial species richness affected functional trade-offs. Discussion Our study is the first to couple multitrophic community assemblies to multifunctionality and functional trade-offs. Our results would boost the understanding of the role of aboveground and underground diversity in multifunctionality and functional trade-offs.
The Resolution Dependence of Contiguous U.S. Precipitation Extremes in Response to CO2 Forcing

AbstractPrecipitation extremes have a widespread impact on societies and ecosystems; it is therefore important to understand current and future patterns of extreme precipitation. Here, a set of new global coupled climate models with varying atmospheric resolution has been used to investigate the ability of these models to reproduce observed patterns of precipitation extremes and to investigate changes in these extremes in response to increased atmospheric CO2 concentrations. The atmospheric resolution was increased from 2° × 2° grid cells (typical resolution in the CMIP5 archive) to 0.25° × 0.25° (tropical cyclone permitting). Analysis has been confined to the contiguous United States (CONUS). It is shown that, for these models, integrating at higher atmospheric resolution improves all aspects of simulated extreme precipitation: spatial patterns, intensities, and seasonal timing. In response to 2 × CO2 concentrations, all models show a mean intensification of precipitation rates during extreme events of a.
Evaluation of submerged microfiltration process to harvest freshwater and marine microalgae

Although microalgae receive much interest as a potential source of biofuel, proteins, and for wastewater treatment, cost-efficient harvesting is still a challenge. This study was performed to investigate the applicability of submerged microfiltration for harvesting both a freshwater green algae species Chlorella vulgaris and a marine diatom Phaeodactylum tricornutum using three lab-made membranes with different porosity. The filtration performance was assessed by assessing their critical flux using the improved flux step method (IFM) and by conducting batch up-concentration filtrations. The cost analysis was in a first approximation estimated based on the data of a related full-scale submerged membrane bioreactor (MBR). Overall results suggest that submerged microfiltration for algal harvesting is economically competitive. The IFM results indicate a low degree of fouling within the range of applied parameters, comparable to the one obtained for a submerged MBR. The membrane with 0.037 μm pore size was found to perform best. The energy consumption was estimated to vary from 0.25 to 0.48 kWh/m 3 , strongly depending on membrane fouling and up-concentration levels. Results suggest a promising prospect of this technique to be applied as a low cost technology for algal harvesting. Further study on long-term and continued algal harvesting at higher up-concentration levels is still necessary.
IMAGE ANNOTATION USING VISUAL CONTENT AND OPTIMIZING OPEN KNOWLEDGE BASE

Open knowledge bases such as the Cyc and DBPedia contain rich knowledge sources for various applications. In the case of image annotation and retrieval applications, such knowledge can be tapped and exploited to semantically annotate poorly annotated images or even images which have not been annotated. We proposed an approach to annotate images by using the visual content of images and subsequently used such content to assign suitable seeds annotation to target images. These seeds annotation are then mapped to the open knowledge base in order to further enrich its semantic information.
The Effect of Post-Activation Potentiation Enhancement Alone or in Combination with Caffeine on Anaerobic Performance in Boxers: A Double-Blind, Randomized Crossover Study

Post-activation performance enhancement (PAPE) is a physiological phenomenon that refers to an acute excitation of the neuromuscular system following intense exercise that ends in enhanced physical performance in a subsequent bout of exercise. The scientific literature has primarily examined the effectiveness of PAPE alone or combined with caffeine (CAF) intake in all-out tests lasting ≤10 s, as the effect of PAPE is transitory. The aim of the present study was to determine the effect of a protocol to induce PAPE alone or in combination with caffeine intake on the 30 s Wingate Anaerobic Test in highly trained boxers. Twenty-five male and highly trained boxers (mean age: 20 ± 1 years) participated in a double-blind, randomized crossover study consisting of three different experimental conditions: (i) control (CON), with no substance intake and no PAPE protocol before the Wingate Anaerobic Test; (ii) PAPE + PLA, involving the intake of a placebo 60 min before and a PAPE protocol comprising a 10 s cycling sprint overloaded with 8.5% of the participants’ body weight 10 min before the Wingate Anaerobic Test; and (iii) PAPE + CAF, involving the intake of 3 mg/kg of caffeine 60 min before and the same PAPE protocol used in the (ii) protocol before the Wingate Anaerobic Test. In all conditions, the participants performed the 30 s version of the Wingate Anaerobic Test with a load equivalent to 7.5% of their body weight, while the cycle ergometer setting was replicated. Immediately following the Wingate test, heart rate (HR), the rating of perceived exertion (RPE), and blood lactate concentration (Bla) were measured. In comparison to CON, PAPE + PLA enhanced mean power (p = 0.024; Effect size [ES] = 0.37) and total work (p = 0.022; ES = 0.38) during the Wingate test, accompanied by an increase in post-test blood lactate concentration (p < 0.01; ES = 0.83). In comparison to CON, PAPE + CAF enhanced mean power (p = 0.001; ES = 0.57), peak power (p = 0.013; ES = 0.57), total work (p = 0.001; ES = 0.53), post-test blood lactate concentration (p < 0.001; ES = 1.43) and participants’ subjective perception of power (p = 0.041). There were no differences in any variable between PAPE + PLA and PAPE + CAF. In summary, a PAPE protocol that involves a 10 s all-out sprint 10 min before the Wingate Anaerobic Test was effective in enhancing Wingate mean power in highly trained boxers. The addition of 3 mg/kg of caffeine to the PAPE protocol produced an effect on mean power of a higher magnitude than PAPE alone, and it enhanced peak power along with participants’ subjective perception of power. From a practical point of view, PAPE before exercise seems to be an effective approach for increasing Wingate performance in highly trained boxers, while the addition of caffeine can increase some benefits, especially peak power.
Atmospheric, radiative, and hydrologic effects of future land use and land cover changes: A global and multimodel climate picture

Land use and land cover changes (LULCC) modulate land surface energy, heat, moisture, and momentum fluxes. Using simulations performed with and without LULCC for five earth system models, averaged over the 2071–2100 period, we quantify the biophysical effects in response to a future realistic LULCC scenario (Representative Concentration Pathway RCP8.5) on 15 climate variables (i.e., atmospheric, radiative, wind, hydrologic variables, and heat fluxes). We find that climate models are able to simulate some robust and strong climate perturbations in response to LULCC. In tropical regions with substantial LULCC, significantly higher skin temperatures, less precipitation and soil moisture, less evaporation and clouds, more incoming radiation and stronger winds, more anticyclonic conditions and subsidence, are simulated in response to future LULCC. In midlatitude and high latitude, LULCC result in autumn cooling and higher tropospheric pressures, while East Asia is drier, warmer, with higher sensible heat flux and lower evaporation. The tropical wind strengthening and weakening of the hydrological cycle are comparable in magnitude to their future regional changes induced by greenhouse gases under RCP8.5, which make LULCC an indispensable forcing to take into account in future climatic assessments. Finally, our study reveals significant indirect atmospheric processes triggered by LULCC, implying substantial changes in incoming radiation, which dominate climatic responses over the direct effects, particularly in boreal regions.
Numerical Simulation of Atmospheric Boundary Layer Flow Over Battlefield-scale Complex Terrain: Surface Fluxes From Resolved and Subgrid Scales

Abstract : The large-eddy simulation (LES) tool has been used to study the transient characteristics of turbulent mixing in atmospheric boundary layer(ABL) flows over canonical topographies representative of complex, urban environments. Specifically, distributions of uniform height, staggered cubes are considered with heights equal to one quarter and one eighth of the ABL depth. An immersed boundary method has been implemented within the LES, to represent the presence of the cubes; this enables the computational domain to capture the classical roughness sublayer and aloft inertial layer. With this, time series of fluctuating streamwise and vertical velocity is recorded over the ABL depth at various positions. Thus, the code has been used to record datasets that would more typically be recorded with micrometeorological instrumentation in field campaigns. However the unique spatial and temporal accessibility offered via simulations facilitates novel analysis that has led to new physical insights regarding the transient characteristics of roughness sublayer dynamics. It has been shown periods of momentum excess(deficit) in the inertial layer precede excitation(subdual) of cube-scale coherent vortices in the roughness sublayer, and a physics-based predictive model for the observed advective lag has been developed.
Artificial design of organic emitters via a genetic algorithm enhanced by a deep neural network

The design of molecules requires multi-objective optimizations in high-dimensional chemical space with often conflicting target properties. To navigate this space, classical workflows rely on the domain knowledge and creativity of human experts, which can be the bottleneck in high-throughput approaches. Herein, we present an artificial molecular design workflow relying on a genetic algorithm and a deep neural network to find a new family of organic emitters with inverted singlet-triplet gaps and appreciable fluorescence rates. We combine high-throughput virtual screening and inverse design infused with domain knowledge and artificial intelligence to accelerate molecular generation significantly. This enabled us to explore more than 800 000 potential emitter molecules and find more than 10 000 candidates estimated to have inverted singlet-triplet gaps (INVEST) and appreciable fluorescence rates, many of which likely emit blue light. This class of molecules has the potential to realize a new generation of organic light-emitting diodes.
Toxicological evaluation of Sargassum plagiophyllum extract in male mice

Purpose: To provide valid scientific evidence for the safety of Sargassum plagiophyllum extract in an animal model. Methods: Sargassum plagiophyllum extract (SPE) was obtained via water extraction using an autoclave at 121°C for 20 min. The SPE was administered to 4 groups of adult male mice via gavage once a day for 21 days. The treatment groups received different doses of SPE, i.e., 100, 500, 1000, and 2000 mg/kg. Control mice were given distilled water. Body weight, and feed and water intakes were recorded. The toxicity of SPE was assessed by determining blood, biochemical, and histopathological indices. Results: Intake of SPE for 21 days did not produce any impact on body mass, feed intake and water intake, even at 2000 mg/kg. Hematological parameters were also unaffected. Biochemical analysis of blood/serum revealed normal levels of blood urea nitrogen (BUN), creatinine, alanine transaminase (ALT), aspartate transaminase (AST), and alkaline phosphatase (ALP) in all treatment groups, when compared to control group. Moreover, histopathological studies confirmed healthy conditions of the liver, kidney, colon, and other organs in all treatment groups. Conclusion: These results from a mouse model provide basic scientific evidence of the safety of consuming Sargassum plagiophyllum extract, even at high doses thus expanding its potential use as a medication for improving health.
Encouraging the Mutual Interact Between Dataset-Level and Image-Level Context for Semantic Segmentation of Remote Sensing Image

Recently, semantic segmentation of remote sensing images has witnessed rapid advancement with the adoption of deep neural networks. Contextual cues, referring to the long-range correlation between pixels, are crucial for achieving accurate segmentation results, particularly for objects with less discriminative characteristics in these images. Currently, most studies are centered on incorporating contextual cues by aggregating context information at the dataset level or image level. However, current research often treats contextual cue modeling at the dataset-level and image level as independent procedures, neglecting the intrinsic correlation between these two feature levels. Consequently, the obtained contextual cues are suboptimal. This issue is particularly critical in the semantic segmentation of remote sensing images. To address this, we propose to encourage mutual interaction between dataset-level and image-level contextual cues. Firstly, we propose an interactive dataset–image context aggregation scheme to obtain complementary and consistent multilevel contextual cues. Additionally, we introduce a parallel feature interaction network (PFI-Net) that progressively extracts and fuses features across multiple layers, enabling effective integration of multilevel contexts. Furthermore, we introduce an enhanced shifted window-based cross-attention mechanism to augment model efficiency. Extensive experimental results on the widely used Vaihingen dataset, GaoFen-2 dataset, and instance segmentation in aerial images dataset (iSAID) effectively demonstrate the superiority of our proposed method over the other state-of-the-art methods.
A Fast Instantaneous Power Calculation Algorithm for Single-Phase Rectifiers Based on Arbitrary Phase-Delay Method

The instantaneous power for a three-phase rectifier can be calculated directly in the orthogonal coordinate system via coordinate transformation. However, it is difficult to calculate the instantaneous power directly for a single-phase rectifier due to the lack of another phase. Therefore, an orthogonal signal generator (OSG) is required to create an orthogonal coordinate system. Currently, conventional OSG algorithms suffer from long response time. To remedy this problem, in this paper, an arbitrary phase-delay OSG algorithm is developed and applied to the direct power control (DPC) system in the single-phase rectifiers. First, the instantaneous power theory and DPC are presented. Then, the principle of the arbitrary phase-delay OSG method is introduced and the anti-interference ability of the proposed method is analyzed. Through the dynamic response analysis, the developed algorithm brings faster response, easier implementation, and less computational burden compared with other common OSG algorithms. Finally, the validity and advancement of the proposed algorithm are verified by simulations and hardware-in-the-loop experimental tests. A comparison with conventional OSG algorithms also proves the advantages of the developed method.
Convolutional neural network hyperparameters for face emotion recognition using genetic algorithm

The development of artificial intelligence in facial emotion recognition (FER) is rapidly growing and has been widely applied in various fields. Deep learning (DL) techniques with evolutionary algorithms have become the preferred choice for solving various security, health, gaming, and other related problems. This research proposes the use of a genetic algorithm (GA) as the main method to optimize hyperparameters in the convolutional neural network (CNN) model for FER. The required computation time is approximately 37 hours 57 minutes 55 seconds, with generation 3 taking the longest time at around 16 hours 45 minutes 4 seconds. However, generation 3 achieved an accuracy of 76.11%, which is the highest compared to other generations. The results indicate that the more generations are involved, the higher the achievable accuracy. Furthermore, the proposed CNN-GA model in this study outperforms previous models that have been examined. Thus, this study makes a significant contribution to improving the understanding of using GAs to optimize the performance of CNN models for FER.
Working Paper Series

Did the decline in inﬂation rates from 2012 to 2015 and the low levels of market-based inﬂation expectations lead to de-anchored inﬂation dynamics in the euro area? This paper is the ﬁrst time-varying event study to investigate the reaction of inﬂation-linked swap (ILS) rates – a market-based measure of inﬂation expectations – to macroeconomic surprises in the euro area. Compared to the pre-crisis period, surprises have a much stronger eﬀect on spot ILS rates during the crisis. Medium-term forward ILS rates remain insensitive to news most of the time, which implies inﬂation anchoring. Only short periods of sensitivity on the part of medium-term forward ILS rates are identiﬁed at times of low inﬂation or recession. The sensitivity is lower over more distant forecast horizons such that medium-term sensitivity represents an inﬂation adjustment process and provides no evidence for a de-anchoring of inﬂation expectations or a loss of credibility for the Eurosystem’s policy target.