Miracle-dz/UniFAR-FTUs huggingface dataset:
Causal analysis of the temperature impact on deep-sea biodiversity

The deep sea comprises more than 90% of the ocean; therefore, understanding the controlling factors of biodiversity in the deep sea is of great importance for predicting future changes in the functioning of the ocean system. Consensus has recently been increasing on two plausible factors that have often been discussed as the drivers of deep‐sea species richness in the contexts of the species‐energy and physiological tolerance hypotheses: (i) seafloor particulate organic carbon (POC) derived from primary production in the euphotic zone and (ii) temperature. Nonetheless, factors that drive deep-sea biodiversity are still actively debated potentially owing to a mirage of correlations (sign and magnitude are generally time dependent), which are often found in nonlinear, complex ecological systems, making the characterization of causalities difficult. Here, we tested the causal influences of POC flux and temperature on species richness using long-term palaeoecological datasets derived from sediment core samples and convergent cross mapping, a numerical method for characterizing causal relationships in complex systems. The results showed that temperature, but not POC flux, influenced species richness over 103–104-year time scales. The temperature–richness relationship in the deep sea suggests that human-induced future climate change may, under some conditions, affect deep-sea ecosystems through deep-water circulation changes rather than surface productivity changes.
Long Oak Tree-Ring Chronologies from Central Russia and Their Potential for Dating

ABSTRACT New oak tree-ring chronologies for European Russia built with subfossil oak wood excavated from the alluvial deposits of the Zapadnaya Dvina (Daugava) River and archaeological samples from Novgorod and Vyazma are presented. They have been matched with the nearest absolutely dated tree-ring chronology constructed in Polotsk (Republic of Belarus) and dated to the periods AD 649–1382 (Zapadnaya Dvina), AD 1059–1386 (Novgorod), and AD 1074–1306 (Vyazma). Dates have been further confirmed via comparison with subfossil oaks from Smarhon (Republic of Belarus) and Baltic 1 chronology as well as by radiocarbon dating. Newly built medieval chronologies can be used for dating and provenancing of oak wood originating from archaeological sites and natural archives.
TREMoLo : un corpus multi-étiquettes de tweets en français pour la caractérisation des registres de langue (TREMoLo : a Multi-Label Corpus of French Tweets for Language Register Characterization)

Des registres tels que familier, courant et soutenu sont un phénomène immédiatement perceptible par tout locuteur d’une langue. Ils restent encore peu étudiés en traitement des langues (TAL), en particulier en dehors de l’anglais. Cet article présente un large corpus de tweets en français annotés en registres de langue. L’annotation intègre des marqueurs propres à ce type de textes (tels que les émoticônes ou les hashtags) et habituellement évincés dans les travaux en TAL. À partir d’une graine annotée manuellement en proportion d’appartenance aux registres, un classifieur de type CamemBERT est appris et appliqué sur un large ensemble de tweets. Le corpus annoté en résultant compte 228 505 tweets pour un total de 6 millions de mots. Des premières analyses statistiques sont menées et permettent de conclure à la qualité du corpus présenté. Le corpus ainsi que son guide d’annotation sont mis à la disposition de la communauté scientifique.
Machine Translation Through Language Understanding

In this paper is described a general framework of a next generation machine translation system which translates a text not sentence by sentence but by considering inter-sentential discourse. The method is a step closer to human translation than the present-day machine translation systems. Particularly important are a detailed discourse analysis and a flexible text generation by using information obtained from the discourse analysis.
Optimal sensor placements using modified Fisher information matrix and effective information algorithm

This article presents an optimal sensor placement algorithm for modifying the Fisher information matrix and effective information. The modified Fisher information matrix and effective information are expressed using a dynamic equation constrained by the condensed relationship of the incomplete mode shape matrix. The mode shape matrix row corresponding to the master degree of freedom of the lowest-contribution Fisher information matrix and effective information indices is moved to the slave degree of freedom during each iteration to obtain an updated shape matrix, which is then used in subsequent calculations. The iteration is repeated until the target sensors attain the targeted number of modes. The numerical simulations are then applied to compare the optimal sensor placement results obtained using the number of installed sensors, and the contribution matrices using the Fisher information matrix and effective information approaches are compared based on the proposed parameter matrix. The mode-shape-based optimal sensor placement approach selects the optimal sensor layout at the positions to uniformly allocate the entire degree of freedom. The numerical results reveal that the proposed F-based and effective information–based approaches lead to slightly different results, depending on the number of parameter matrix modes; however, the resulting final optimal sensor placement is included in a group of common candidate sensor locations. However, the resulting final optimal sensor placement is included in a group of common candidate sensor locations.
Advances in computerized MRI‐based biomarkers in Alzheimer’s disease

The use of neuroimaging examinations is crucial in Alzheimer’s disease (AD), in both research and clinical settings. Over the years, magnetic resonance imaging (MRI)–based computer‐aided diagnosis has been shown to be helpful for early screening and predicting cognitive decline. Meanwhile, an increasing number of studies have adopted machine learning for the classification of AD, with promising results. In this review article, we focus on computerized MRI‐based biomarkers of AD by reviewing representative studies that used computerized techniques to identify AD patients and predict cognitive progression. We categorized these studies based on the following applications: (1) identifying AD from normal control; (2) identifying AD from other dementia types, including vascular dementia, dementia with Lewy bodies, and frontotemporal dementia; and (3) predicting conversion from NC to mild cognitive impairment (MCI) and from MCI to AD. This systematic review could act as a state‐of‐the‐art overview of this emerging field as well as a basis for designing future studies.
Incentive mechanism based on Stackelberg game under reputation constraint for mobile crowdsensing

Encouraging a certain number of users to participate in a sensing task continuously for collecting high-quality sensing data under a certain budget is a new challenge in the mobile crowdsensing. The users’ historical reputation reflects their past performance in completing sensing tasks, and users with high historical reputation have outstanding performance in historical tasks. Therefore, this study proposes a reputation constraint incentive mechanism algorithm based on the Stackelberg game to solve the abovementioned problem. First, the user’s historical reputation is applied to select some trusted users for collecting high-quality sensing data. Then, the two-stage Stackelberg game is used to analyze the user’s resource contribution level in the sensing task and the optimal incentive mechanism of the server platform. The existence and uniqueness of Stackelberg equilibrium are verified by determining the user’s optimal response strategy. Finally, two conversion methods of the user’s total payoff are proposed to ensure flexible application of the user’s payoff in the mobile crowdsensing network. Simulation experiments show that the historical reputation of selected trusted users is higher than that of randomly selected users, and the server platform and users have good utility.
An Analysis of Ratings and Interrater Reliability at the American String Teachers Association’s National Orchestra Festival

The purpose of this study was to examine the assigned ratings, interrater reliability, and possible influences of school level and instrumentation on adjudicators’ evaluations of orchestra performances at a national-level adjudicated music festival. Data consisted of the overall ratings assigned to orchestra performances (N = 55) at the 2017, 2018, and 2019 American String Teachers Association’s National Orchestra Festival (NOF). Analysis revealed that 83.64% of all participating orchestras earned a I/Superior or II/Excellent overall rating. A logistic regression model revealed that both school level and instrumentation were not significant predictors of earning a I/Superior overall rating. Individual members of each three-judge adjudication panel assigned identical ratings to 61.82% of orchestra performances and possessed a mean internal consistency of . 90. Orchestra teachers can use the results of this study to weigh the educational benefits of participating in the NOF.
Throughput optimization of multi-hop and multi-path cooperation in WPSNs with hardware noises

This paper proposes a novel multi-path and multi-hop wireless powered sensor network in case of hardware impairment, constituting an energy node, one source node, single sink node, and a series of distributed relay sensor nodes, where the energy node transmits wireless energy to all terminals in the first stage, and the relay sensor nodes relay the information of the source node to the sink node in the second stage. There exists M available paths between the source node and sink node, one of which is chosen for serving source-sink communication. To enhance the minimum achievable data rate, we propose a multi-hop communication protocol based on time-division-multiple-access and an optimal throughput path algorithm. We formulate the time allocation optimization problem about energy and information transmission of the proposed multi-hop cooperation, and confirm through abundant simulation experiments that the proposed scheme can availably improve user unfairness and spectral efficiency, and thus enhance its throughput performance.
Can pulse wave velocity (PWV) alone express arterial stiffness? A neglected tool for vascular function assessment

Abstract Arterial stiffness, defined as the rigidity of the arterial wall, is the consequence of vascular aging and is associated with the full spectrum of cardiovascular diseases. Carotid-femoral pulse wave velocity (cf-PWV) is the gold standard method for arterial stiffness evaluation: it measures the velocity of the arterial pulse along the thoracic and abdominal aorta alongside arterial distensibility. Its value rises as stiffness progresses. Cf-PWV is helpful to assess residual cardiovascular risk (CVR) in hypertension (HT). In fact, an increase in pulsatility and arterial stiffness predicts CVR in patients affected by arterial HT, independently of other risk factors. Arterial stiffness can predict cardiovascular events in several other clinical conditions such as heart failure, diabetes, and pulmonary HT. However, cf-PWV has not been yet included in routine clinical practice so far. A possible reason might be its methodological and theoretical limitations (inaccuracy in the traveled distance, intra and interindividual variability, lack of well-defined references values, and age- and blood pressure-independent cutoff). To exceed these limits a strict adherence to guidelines, use of analytical approaches, and possibility of integrating the results with other stiffness examinations are essential approaches.
Optical current generation in graphene: CEP control vs. ω + 2ω control

Abstract The injection of directional currents in solids with strong optical fields has attracted tremendous attention as a route to realize ultrafast electronics based on the quantum-mechanical nature of electrons at femto- to attosecond timescales. Such currents are usually the result of an asymmetric population distribution imprinted by the temporal symmetry of the driving field. Here we compare two experimental schemes that allow control over the amplitude and direction of light-field-driven currents excited in graphene. Both schemes rely on shaping the incident laser field with one parameter only: either the carrier-envelope phase (CEP) of a single laser pulse or the relative phase between pulses oscillating at angular frequencies ω and 2ω, both for comparable laser parameters. We observe that the efficiency in generating a current via two-color-control exceeds that of CEP control by more than two orders of magnitude (7 nA vs. 18 pA), as the ω + 2ω field exhibits significantly more asymmetry in its temporal shape. We support this finding with numerical simulations that clearly show that two-color current control in graphene is superior, even down to single-cycle pulse durations. We expect our results to be relevant to experimentally access fundamental properties of any solid at ultrafast timescales, as well as for the emerging field of petahertz electronics.
Economic and social security in EU: Reforming Slovenian law on social enterpreneurship

Abstract Europe 2020 strategy of smart, sustainable and inclusive growth also implies policies for boosting social innovations. In this context, it is relevant to encourage solutions such as social entrepreneurship as it is addressing social and environmental issues while employing entrepreneurship. Social entrepreneurship depends on the context of its emergence and implementation. In this regard, the context-specific factors play an important role. Following the Cultural Political Economy approach, the article examines on different factors that have influenced on the adoption of amendments of the Slovenian Social Entrepreneurship Act in 2018. Selection of the case study of Slovenia bases on the findings implying that Slovenia has still not overcome the initial phase of the development of the social entrepreneurship. The article reveals that there are still issues relevant for running social enterprises that are not sufficiently regulated by the law. One of them is a training of the vulnerable groups in terms of their technological skills in order to ensure their efficiency and productiveness in Work Integration Social Enterprises.
A Simulation Study to Locate Cervical Abnormality based on Electrical Impedance Tomography (EIT), using a Planar Nine-Electrode Probe

Cervical cancer is one of the most common causes of cancer death. Early detection of cancer can reduce mortality and there are many screening methods nowadays e.g. Pap smear, liquid-based cytology, and visual inspection by colposcopy. Screening by measuring bioimpedance is also another method that can detect the histological change of cells. In this paper, instead of using only bioimpedance to classify tissues for cancer, we propose to reconstruct the data of bioimpedance obtained from a measurement pattern as the image of bioimpedance distribution, based on Electrical Impedance Tomography (EIT). A nine-electrode planar array probe was investigated. The simulation result shows that simulated grade1 and 2/3 cervical intraepithelial neoplasia (CIN) objects can be located for most of the cases with the localization error of 40-48% of the object's diameter. When the objects were situated in the low sensitivity region, the object could not be located. Grade 1 and small-sized CIN were more difficult to locate. It is also noticeable that the amplitude of conductivity change in the images was smaller than the true amplitude by 1.1-9.4 times. This indicates that these images are more suitable to use as qualitative images than quantitative images.
Investigation of SIFT and ORB descriptors for Indoor Maps Fusion for the Multi-agent mobile robots

There are many applications for creating an indoor map by a single robot already. When using a single robot in a large working space like a factory, the performance and robustness are needed to be increased. Multi-Agent Robot System (MAR) is introduced to meet this requirement. MAR could increase productivity and flexibility while works in a dynamic environment because it is modular and can work simultaneously. When MAR combines with Simultaneous Localization and Mapping (SLAM) technology, it can explore and discover the indoor environment cooperatively and simultaneously. Each robot creates its map with different initial poses and path planning. The main issue of a Multi-Robot SLAM (MRSLAM) is how to combine maps from different robots correctly. In this research, we will focus on algorithms of map merging. SIFT and ORB descriptors are selected along with some image processing techniques, and a proposed approach including the algorithms is verified by general benchmark map data. The results will be shown and discussed. Then, the proposed approach will be deployed into a real robot platform based on Robot Operating System (ROS). Experiments will be conducted to prove the feasibility and the limitation of the proposed approach in the real-world scenario.
The Language of Engineering: Training a Domain-Specific Word Embedding Model for Engineering

Since the introduction of Word2Vec in 2013, so-called word embeddings, dense vector representation of words that are supposed to capture their semantic meaning, have become a universally applied technique in a wide range of Natural Language Processing (NLP) tasks and domains. The vector representations they provide are learned on huge corpora of unlabeled text data. Due to the large amount of data and computing power that is necessary to train such embedding models, very often, pre-trained models are applied which have been trained on domain unspecific data like newspaper articles or Wikipedia entries. In this paper, we present a domain-specific embedding model that is trained exclusively on texts from the domain of engineering. We will show that such a domain-specific embeddings model performs better in different NLP tasks and can therefore help to improve NLP-based AI in the domain of Engineering.
Risk Assessment for Agricultural Supply Chain Financial Sustainable Development

Rapid agricultural industrialization means that many Chinese small or medium-sized enterprises (SMEs), agricultural industry financial businesses, and core enterprises are seeking agricultural supply chain financial innovation. However, while there are significant benefits to be gained from innovation, agricultural supply chain finance (ASCF) has certain risks, and as the number of nodes in the agricultural supply chain increases, the potential financial risks increase. To scientifically identify the key factors affecting ASCF and to determine the relationships between these factors, this paper first constructs an agricultural supply chain financial risk evaluation index system made up of 16 aspects across five dimensions. Then, to quantitatively analyze the interactions between the factors, a model is developed that combines the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method and the Criteria Importance through Inter-criteria Correlation (CRITIC) method. Finally, a causal classification and importance analysis is conducted based on the quantitative results. It was found that pledge risk, enterprise moral risk, farmer credit risk, core enterprise credit status, operational risk and other factors were the key factors affecting ASCF. These results provide a scientific decision-making basis for ASCF risk assessments and a comprehensive guide for commercial bank risk prevention and agricultural supply chain financial business credit control, which can assist in the achievement of sustainable ASCF development.
Intelligent Set Speed Estimation for Vehicle Longitudinal Control with Deep Reinforcement Learning

Besides the goal of reducing driving tasks, modern longitudinal control systems also aim to improve fuel efficiency and driver comfort. Most of the vehicles use Adaptive Cruise Control (ACC) systems that track constant set speeds and set headways which makes the trajectory of the vehicle in headway mode highly dependent on the trajectory of a preceding vehicle. Hence, this might lead to increased consumptions in dense traffic situations or when the leader has a less careful driving style. In this work, a method based on Deep Reinforcement Learning (DRL) is presented that finds a control strategy by estimating an intelligent variable set speed based on the system state. Additional control objectives, such as minimizing consumption, are considered explicitly through the feedback in a reward function. A DRL framework is set up that enables the training of a neural set speed estimator for vehicle longitudinal control in a simulative environment. The Deep Deterministic Policy Gradient algorithm is used for the training of the agent. Training is carried out on a simple test track to teach the basic concepts of the control objective to the DRL agent. The learned behavior is then examined in a more complex, stochastic microscopic traffic simulation of the city center of Darmstadt and is compared to a conventional ACC algorithm. The analysis shows that the DRL controller is capable of finding fuel efficient trajectories which are less dependent on the preceding vehicle and is able to generalize to more complex traffic environments, but still shows some unexpected behavior in certain situations. The combination of DRL and conventional models to build up on the existing engineering knowledge is therefore expected to yield promising results in the future.
Syntheses, Crystal Structures and Antibacterial Activities of Dithiocarbamate Complexes([M(MeBnNCS_2)_3], M=Sb(III), Bi(III))

Two novel complexes [Sb (Ⅲ)(MeBnNCS2)3](1) and [Bi (Ⅲ)(MeBnNCS2)3](2) were synthesized and characterized by elemental analysis, IR,1H NMR, TG-DTG and X-ray single-crystal diffraction. Both complexes belong to monoclinic system, P21/c space group, for 1: a=0.955 1(7) nm, b=1.357 5(10) nm, c=2.468 1(17) nm, β=104.01(2)°, Z=4, V=3.105(4) nm3, Dc=1.520 g·cm-3, F(000)=1 440, μ=1.314 mm-1, the final R1=0.033 9, wR2= 0.083 2, S=1.010, for 2: a=1.339 0(6) nm, b=0.997 5(5) nm, c=2.426 1(5) nm, β=98.433(7)°, Z=4, V=3.205(2) nm3, Dc=1.653 g·cm-3, F(000)=1 568, μ=5.912 mm-1, the final R1=0.039 8, wR2=0.086 4, S=1.089. The central M(Ⅲ) are coordinated by six sulfur atoms from three legends, completing distorted octahedral coordination environment in 1 and pentagonal pyramid configuration in 2. For 2, the adjacent molecules are formed the dimmer structure by the weak interactions of Bi…S. The complexes were valued for their antimicrobial activities by agar diffusion method. The results indicate that the complex 1 to be active against the four test bacterial organisms. CCDC: 919077, 1; 919078, 2.
LSTM Based Scene Detection with Smartphones

With rapid adoption of smartphones, context detection is becoming increasingly important to enable new and sophisticated context-aware mobile apps and provide better communication services. In this paper, we propose an Long Short Term Memory (LSTM) based indoor/outdoor/underground detection system for smartphone scene detection with low energy consumption. The proposed system is first compared with other deep learning methods including fully connected network (FC), standard LSTM network and Gated Recurrent Unit (GRU) based models. and then with traditional machine learning methods including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR) and Random Forest (RF). Experimental results show that our proposed system is superiors in identifying indoor/outdoor/underground scene using only ultra-low power sensors. We collect real data at different periods and locations using multiple mobile devices. The required sensors are common in all types of smartphones, implying high compatibility and availability of the system.