Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial analyses of multi‐trophic terrestrial vertebrate assemblages in Europe

1Laboratoire d’Écologie Alpine (LECA), University Grenoble Alpes, CNRS, University Savoie Mont Blanc, Grenoble, France 2Department of Forest Resources Management, Faculty of Forestry, UBC Forest Sciences Centre, Vancouver, British Columbia, Canada 3Pacific Forestry Centre, Canadian Forest Service – Natural Resources Canada, Victoria, British Columbia, Canada 4Theoretical and Experimental Ecology Station, Moulis, France 5Département de biologie, Université de Sherbrooke, Sherbrooke, Quebec, Canada 6Department of Biology and Biotechnologies “Charles Darwin”, Università di Roma “La Sapienza”, Roma, Italy 7Department of Environmental Sciences and Policy, Università degli Studi di Milano, Milano, Italy 8Laboratoire de Biométrie et Biologie Evolutive, Univ Lyon, Université Claude Bernard Lyon 1, CNRS, Villeurbanne, France
Set-conditional set generation for particle physics

The simulation of particle physics data is a fundamental but computationally intensive ingredient for physics analysis at the large Hadron collider, where observational set-valued data is generated conditional on a set of incoming particles. To accelerate this task, we present a novel generative model based on a graph neural network and slot-attention components, which exceeds the performance of pre-existing baselines.
Active Damping Method for Mitigation of Resonance Propagation in Grid-connected and Islanding Microgrid by using FLC

The shunt capacitor banks and underground links application can present force circulation framework resonances. At that point the effects of current controlled and voltage controlled circulated era (DG) units to microgrid reverberation spread is researched. A straight voltage controlled DG unit with a LC channel has a short out component at the chose symphonious frequencies. While a current controlled DG unit exhibits an open circuit qualities. Because of various practices at consonant frequencies, particular symphonious moderation systems are created for voltage controlled and current controlled DG units individually. A voltage controlled DG unit-based dynamic symphonious damping strategy for both framework associated framework and islanding microgrid framework is talked about in this paper. An enhanced virtual impedance control technique with a virtual damping resistor and a nonlinear virtual capacitor is prescribed. The adequacy of the proposed damping procedure is investigated by utilizing both a solitary DG unit and twofold or various parallel DG units. Here fuzzy logic is used for controlling the simpered systems tool has proved that the combined system will at the same time inject maximum power and provide dynamic frequency support to the grid.
ANALYSIS OF UNEVENLY SPACED TIME SERIES DATA IN HIGHLY DISTRIBUTED AND UNRELIABLE NETWORKS

Missing values may appear in collected time series for a number of reasons such as: network failure, power failures and other unexpected conditions during data transfer or their collection what creates a challenge when trying to analyze such data. Accordingly, there is need for reconstructing these missing values and gaps, i.e. to do transformation from unevenly spaced into evenly spaced time series, but based on the best fitted forecast method. Several types of time series are generally distinguished, including stationarity time series (e.g. white noise) and different patterns in non-stationary time series such as: trend, seasonal and a combination of them. By selecting a forecasting method that has the best accuracy (the smallest error) for adequate and recognized type of time series, an automated recursive predicting function is presented. An important factor in determining the best forecasting method involves the different forecast accuracy measures.
Support vector machine based spam SMS detection

Short Message Service (SMS) is the most important communication tool in recent decades. With the increased popularity of mobile devices, the usage rate of SMS will increase more and more in years. SMS is a practical method used to reach individuals directly. But this practical and easy method can cause SMS to be misused. The advertising or promotional SMS of the companies are an examples of this misuse. In this study, a spam SMS detection technique is proposed using Data Mining (DM) methods. In the proposed study, data mining algorithms such as Naive Bayes (NB), K-Nearest Neighborhood (KNN), Support Vector Machine (SVM), Random Forest (RF) and Random Tree (RT) is selected. SMSSpamCollection dataset, which is contain 747 spam SMS and 4827 ham SMS, is used. 10 fold cross-validation technique is used to evaluate prediction of Spam SMS in the dataset. Therefore, proposed study achieved 98.33 % success rate and 0,087 false positive rate for SVM algorithm.
Non-Neural Network Applications for Spiking Neuromorphic Hardware

—Increasing power costs for large-scale computing in a post-Moores Law system have forced the high-performance computing community to explore heterogeneous systems. Neuromorphic architectures, inspired by biological neural systems, have so far been relegated to auxiliary machine learning applications. Here, we discuss growing research showing the viability of ultra-low-power neural accelerators as co-processors for classic compute algorithms, such as random walk simulations and graph analytics.
Soft-sensing nitrite in a urine nitrification system for resource recovery

The unscented Kalman filter (UKF) has been proposed as general-purpose modelbased soft-sensor (i.e. observer) for nonlinear dynamic processes. Prior work focused on use of the UKF with a dynamic model involving all reactions, including chemical speciation and acidbase reactions, as dynamic phenomena in a set of ordinary differential equations (ODEs). This results in an observer that is sensitive to noise. In this work, we present recent results obtained with a reformulated model within which the fast chemical reactions are represented and solved as algebraic equilibria. This leads to an accurate and reliable estimate of the nitrite concentration, which is the key variable for process supervision and control of the urine nitrification process under study.
An assessment of eyewitness accuracy: The integration of suggestibility and misidentification

An Assessment of Eyewitness Accuracy: The Integration Of Suggestibility And Misidentification Karri S. Bonner The current study was an examination of eyewitness accuracy and how it relates to misidentification. A new scale to measure suggestibility in adults (The Video Suggestibility Scale for Adults, VSSA) was developed. This scale is modeled after the Gudjonsson Suggestibility Scales, which define suggestibility as a combination of Yield and Shift. Participants (N = 160) were administered the VSSA. Participants watched a video of two crimes and were subsequently interviewed either immediately or after a 5-8 day delay. The interview consisted of free recall, open-ended questions, and closed-ended questions suggestive questions. Participants also viewed several lineups to assess accuracy of identification of perpetrators. Results indicated that participants yielded to the suggestions made by the interviewer and shifted their answers in response to negative feedback. Timing of interview had an effect on Yield 1, Shift, and Total Suggestibility, with individuals who were interviewed immediately having lower Yield 1, Shift, and Total Suggestibility scores. Individuals who were interviewed after a delay were more likely to decline in the accuracy of their responses to probe questions than those who were interviewed immediately. Participants who were more suggestible were more likely to incorrectly select a perpetrator from a target-absent lineup, as were those who scored high on the Yield 2 subscale. These findings have important implications for how witnesses should be interviewed.
Using Q-XAS to Determine Zinc Speciation in a Contaminated Environment

Metal smelting operations have introduced large concentrations of metals in aquatic systems [1]. In order to understand the fate of these metals, one needs to assess the mobility, the reactivity, and the toxicity of these contaminants in the impacted environment. Hence, the determination of the chemical speciation of metals becomes an essential issue. Often, this is achieved by means of selective extraction procedures or by performing equilibrium calculations coupled to appropriate surface complexation models [2]. However, these approaches can be misleading because wet chemical methods are operationally defined and natural systems are quite frequently out of equilibrium [3]. In addition, biological systems have evolved a variety of mechanisms to respond to metal stress, some of which involve a re-speciation of the metals [4-6], and that are not yet well understood. Therefore, there is a need to develop speciation schemes that allow ones to determine directly the various chemical and physical forms of the metals in the environment. In a previous paper [7] we investigated the speciation of zinc in contaminated sediments using analytical electron microscopy (AEM). Zinc bearing particles ranged from near spherical colloids to small grains intimately associated either with biological templates or as separate amorphous entities. Elemental analyses showed that zinc was present in structures composed of iron and phosphorus close to the source of contamination, whereas further away zinc was primarily associated with sulfur. We present here an approach to quantifying the chemical speciation of Zn by Q-XAS.
SOURCE: SOURce-Conditional Elmo-style Model for Machine Translation Quality Estimation

Quality estimation (QE) of machine translation (MT) systems is a task of growing importance. It reduces the cost of post-editing, allowing machine-translated text to be used in formal occasions. In this work, we describe our submission system in WMT 2019 sentence-level QE task. We mainly explore the utilization of pre-trained translation models in QE and adopt a bi-directional translation-like strategy. The strategy is similar to ELMo, but additionally conditions on source sentences. Experiments on WMT QE dataset show that our strategy, which makes the pre-training slightly harder, can bring improvements for QE. In WMT-2019 QE task, our system ranked in the second place on En-De NMT dataset and the third place on En-Ru NMT dataset.
Chapter 2 Genotyping DNA Variants with High-Resolution Melting Analysis

High-resolution melting analysis (HRMA) is a simple, quick, and effective method to scan and screen PCR amplicons for sequence variants. HRMA is a nondestructive closed tube assay; after PCR, DNA melting can directly be performed on the amplified samples without any purification or separation steps. For single SNP genotyping, HRMA is an attractive alternative to Sanger sequencing, restriction enzyme analysis, and hydrolysis probes.
CMOS FUZZY CONTROLLERS IMPLEMENTED AS MIXED-SIGNAL

This paper describes a programmable fuzzy controller chip designed with mixed-signal IC techniques. Its input and output signals are analog to directly interact with the information from the real world. The programmability interface is digital and the output signal is also given in digital format to allow easy embedding into digital processing environments. Experimental results from a prototype integrated in a 2.4-μm CMOS process are included.
Assessment of Physicochemical and Bioactive Properties of Fresh and Dried Sweet Cherry Fruit

Bu calismada Giresun’da yetisen kirazin ( Prunus avium ) laboratuvar tipi etuvde, uc farkli ortam sicakliginda (40, 50, 60 °C) kurutulmasi sonucunda pH, suda cozunur kuru madde (SCKM), renk, toplam fenolik madde ve antioksidan aktivite ozelliklerinde meydana gelen degisimler arastirilmistir. Toplam fenolik madde icerigi Folin-Ciocalteu spektrofotometrik yontemle belirlenmis ve sonuclar Gallik Asit Esdegeri (GAE) olarak ifade edilmistir. Antioksidan aktivite ise ABTS+ radikal supurucu aktivite ile belirlenmis ve sonuclar Trolox Esdeger Antioksidan Kapasite (TEAC) olarak ifade edilmistir. Kurutma sicakligi arttikca toplam kurutma suresinin azaldigi, ayrica kurutmada yarim kiraz orneklerinin tam kiraz orneklerine gore daha kisa surede kurudugu tespit edilmistir. Farkli kurutma sicakliklarinda kurutulan yarim ve tam kiraz orneklerinin pH degerinin 4.08-4.45 araliginda degistigi belirlenmistir. Kurutma suresince kiraz orneklerinin renk degisimleri incelendiginde kurutma sicakliginin artmasiyla renkte L* degerlerinin onemli olcude etkilenmedigi fakat a* degerinin arttigi ve b* degerlerinin azaldigi tespit edilmistir. Kurutma sonrasi yarim kiraz ve tam kiraz ekstraktlarinda toplam fenolik madde miktarlari sirasiyla 362.58-347.26 mg GAE/100g ve 372.49-355.17 mg GAE/100g araliginda bulunmustur. Antioksidan aktivitesi degerleri ise yarim ve tam kiraz ekstraktlarinda sirasiyla 14.70-20.59 µM trolox/mg kuru ornek ve 15.51-27.46 µM trolox/mg kuru ornek olarak belirlenmistir. Kiraz orneklerinin kurutulmasi sirasinda yuksek sicaklik ve oksijene maruz kalmasindan dolayi tam ve yarim olarak kurutulmus kiraz orneklerinde fenolik madde icerigi ve buna bagli olarak daha antioksidan aktivitesinde azalma tespit edilmistir.
ERRORS OF SPATIAL GRIDS ESTIMATORS OF VOLUME AND SURFACE

Errors of the volume and surface area estimators that use IUR spatial grids of points, lines and planes are calculated using geometric characteristics of the objects under study. Coefficients in trend terms of asymptotic formulae for variances of volume estimators that use simple orthogonal grids are calculated. The variance of surface area estimation using spatial grids of lines has two components, the variance due to orientation and the residual variance. Upper bounds of the component of the error of surface area estimator due to the orientation are calculated for various sets of directions of grid lines. Coefficients in trend terms of asymptotic formulae for residual variances of the surface area estimators using triple orthogonal spatial grids of lines are calculated.
Towards efficient mapping of BNNs onto embedded targets using Tensorflow / XLA

Copyright remains with the authors. Abstract Binarised Neural Networks hold significant promise for efficient inference on embedded devices and have received significant attention recently. We investigate if Tensorflow and its XLA compiler infrastructure are suited for generating efficient embedded code given a trained BNN. We detail the Tensorflow/XLA/LLVM work flow, building on Tensorflow’s XLA high-level optimiser and following LLVM infrastructure. We explore XLA generated code for dot products as well as supported data types. We build a binary dot product in LLVM IR and show the efficiency of the resulting code. We identify shortcomings of the current Tensorflow/XLA/LLVM tool chain and suggest future work to yield efficient autogenerated code for binary dot products.
An Approach to Tuning Hyperparameters in Parallel: A Performance Study Using Climate Data CyberTraining: Big Data + High-Performance Computing + Atmospheric Sciences

The ability to predict violent storms and bad weather conditions with current models can be difficult due to the immense complexity associated with weather simulation. For example when predicting a tornado caution must be used when attempting to quickly classify a weather pattern as tornadic or not tornadic. Thus one can use machine learning to quickly classify these weather patterns but great care must be taken to obtain the maximal amount of accuracy while maintaining prediction wall time. We then create a general framework for determining hyperparameters with tensorflow and keras and use it for training a convolutional neural network that specializes in classifying storms as tornadic or not tornadic based on important factors like vorticity. We demonstrate our framework’s ability to determine optimal hyperparameter values for batch size, epochs, and learning rate by examining accuracy and training time with regards to a small amount of application data. In the context of training time we leverage both CPUs and GPUs and found the performance of GPUs to be vastly superior in time taken to train the various networks than CPUs.
Corruption, Institutional Quality and Growth: a Panel Smooth Transition Regression Approach∗

We analyze the impact of corruption and institutional quality on economic growth using a sample of 128 developed and developing countries on the period 1984-2012. We show, by employing a Panel Smooth Transition Regression framework à la Gonzalez et al. (2005), that there is a non-linear relation between corruption and growth modulated by the institutional development. Specifically, we find that corruption can positively (negatively) affect growth in case of a low (high) level of institutional development. This result suggests therefore a reconcialition of the theories asserting that corruption can ”sand the wheels” or ”grease the wheels” of economic growth.
Performance Analysis of Different Transmission Methods for Broadcast Channels in MU-MIMO S

Multi-user MIMO system is affected by Multiple Access Interference (MAI) caused by the simultaneous transmission of multiple users which limits capacity and performance of CDMA system. Since the data recovery at the receiver side is not straightforward, these interferences cause error bit while reconstructing the signal. So, different transmission methods are adopted to cancel out these interferences where the signals are precoded before transmission. Channel inversion, Dirty paper coding and Tomlinson Harashima Precoding are some of these methods which cancel outs the effects of Inter-symbol interference by means of Channel state information feedback. This paper discusses about these transmission methods for broadcast channels in multi-user MIMO. First we discuss the mathematical model of Multi-user MIMO system. Then we analyse the performance of different transmission methods for broadcast channels in terms of bit error rate (BER). Finally, we make a comparison among these methods by their performance.
eview hysical constraints for pathogen movement lrich

In this pedagogical review, we discuss the physical constraints that pathogens experience when they move in their host environment. Due to their small size, pathogens are living in a low Reynolds number world dominated by viscosity. For swimming pathogens, the so-called scallop theorem determines which kinds of shape changes can lead to productive motility. For crawling or gliding cells, the main resistance to movement comes from protein friction at the cell–environment interface. Viruses and pathogenic bacteria can also exploit intracellular host processes such as actin polymerization and motor-based transport, if ell shape ell adhesion
Business cycles in presence of exogenous financial exclusion with productivity shocks

Recent literature on macroeconomic fluctuations in emerging and developed countries has focussed on the role of various frictions and shocks rather than the differences in the ability of the households in these two types of countries to absorb these shocks. Emerging and developed countries differ in proportion of agents who have access to financial services. The paper highlights the contribution of such heterogeneity to the volatility of aggregate consumption. I compute the correlation generated by the model between financial inclusion and ratio of volatility of aggregate consumption to volatility of aggregate income for emerging and developed countries and compare with the data counterpart to justify the model.
A review of 3 D reconstruction techniques in civil engineering and their applications

Three-dimensional (3D) reconstruction techniques have been used to obtain the 3D representations of objects in civil engineering in the form of point cloud models, mesh models and geometric models more often than ever, among which, point cloud models are the basis. In order to clarify the status quo of the research and application of the techniques in civil engineering, literature retrieval is implemented by using major literature databases in the world and the result is summarized by analyzing the abstracts or the full papers when required. First, the research methodology is introduced, and the framework of 3D reconstruction techniques is established. Second, 3D reconstruction techniques for generating point clouds and processing point clouds along with the corresponding algorithms and methods are reviewed respectively. Third, their applications in reconstructing and managing construction sites and reconstructing pipelines of Mechanical, Electrical and Plumbing (MEP) systems, are presented as typical examples, and the achievements are highlighted. Finally, the challenges are discussed and the key research directions to be addressed in the future are proposed. This paper contributes to the knowledge body of 3D reconstruction in two aspects, i.e. summarizing systematically the up-to-date achievements and challenges for the applications of 3D reconstruction techniques in civil engineering, and proposing key future research directions to be addressed in the field.
Normality in Medicine : A critical review Systematic Review

s were read, and the word “normality” was searched in the body of text. The result was 31 articles that were studied thoroughly and their importance for the topic at hands was evaluated. The rationale that we have followed is according to the PRISMA statement, and the PRISMA flow-chart is presented in the Appendix (6.1). 12 The results were organized in a table presented in the Appendix (6.2) that shows the articles selected, the year and place of publication, and the viewpoint on normality they express. In the body of the review, from the analysis of the articles, resulted the section headed Normality Models, where the viewpoints on normality were grouped into models and their advantages and disadvantages exposed. To explore the limitations of these models, the authors have then formulated three Case Studies where the models were applied. Following is the section regarding the Discussion, where the models were criticized globally, and the authors point of view expressed openly.
Association of leukoaraiosis and the risk of recurrent stroke : a meta-analysis

Stroke is a severe disability with high recurrence rate. Inconsistent results about the association between leukoaraiosis (LA) and risk of recurrent stroke exists. The meta-analysis was aimed to comprehensively evaluate the predicted role of LA for risk of stroke recurrence. Several databases were retrieved from their inception to June, 2015, using the strategies of Leukoaraiosis AND (stroke OR “Cerebral infarction”) AND (recurrence OR recurrent OR relapse). Eligible studies were identified based on predefined inclusion criteria. Quality of the selected studies was assessed by the Newcastle-Ottawa Scale. Sensitive analysis and publication bias were also performed. Additionally, subgroup analysis stratified by different follow-up times (<5 years and ≥5 years) was conducted. All the statistical analyses were completed using Stata 11.0. The Relative Risk (RR) with corresponding 95% confidence interval (CI) was used as a measure of the effect size. Consequently, we included five studies in our meta-analysis, most of which were high-quality. No reverse results were detected via sensitive analysis, indicating the reliability of this meta-analysis. Moreover, no publication bias was observed (Egger’s test: t = -1.71, P = 0.185). LA was significantly associated with the risk of stroke recurrence in two subgroups (<5 year: RR = 1.47, 95% CI, 1.27 to 1.70, P<0.001; ≥5 years: RR = 1.26, 95% CI, 1.12 to 1.43, P<0.001). LA could be used as a predictor for the risk of recurrent stroke. However, more studies with large sample size were required to confirm this finding.