Miracle-dz/UniFAR-FTUs huggingface dataset:
Association patterns in a high-elevation chimpanzee community in Rwanda

Investment in social interaction and affiliative behaviour is often related to variation in sex-specific dispersal patterns among species but can also vary within species in response to local environmental conditions and feeding competition. Chimpanzee (Pan troglodytes) association patterns have been studied primarily in low and mid-elevation tropical forests. This study adds to our understanding of association patterns in chimpanzees by presenting the first data on association patterns in a high-elevation population at Nyungwe in equatorial Rwanda. Using data on co-occurrence in social parties (party association) and calculating gregariousness indices, we quantified levels of associations among age-sex classes relative to food (fruit) distribution and the presence of oestrous females. Overall, adult females in Nyungwe are less gregarious than males. Female but not male gregariousness increased when feeding on clumped foods, indicating that resource characteristics impact female association tendencies more intensely than males. Despite high elevation forests representing a habitat with relatively fewer fruit resources for chimpanzees, at an edge of range location, we found that gregariousness patterns were driven by similar factors as those in mid and low elevation chimpanzee populations. HIGHLIGHTS – We quantified association patterns in a high elevation population of chimpanzees at Nyungwe (Rwanda) – Adult females were less gregarious than males – Female but not male gregariousness increased when feeding on clumped foods, indicating that resource characteristics impact female association tendencies more intensely than males
A method for solving bilevel linear programming problems

This paper presents an approach for solving bilevel linear programming problems (BLPP). It is based on the result that an optimal solution to the BLPP is reachable at an extreme point of the underlying region. Consequently, we develop a pivot technique to find the global optimal solution on an expanded tableau that represents the data of the BLPP. The pivot technique allows to rank in increasing order the outer level objective function value until
High-resolution observations of a complete sample of 27 FR II radio galaxies and quasars with 0.3 < z < 0.6

We present high-resolution Very Large Array and Multi-Element Radio Linked Interferometer Network observations of the 27 FR II radio galaxies and quasars with 0.3 < z < 0.6 from the sample of Laing, Riley & Longair (1983). The jet detection rate is similar to that found in the lower-power, lower-redshift studies carried out using similar observing conditions. A wide range of lobe and hotspot structures is observed in both the radio galaxies and quasars in the sample. Interestingly, a high proportion of the quasars exhibit structures and asymmetries markedly different from those of the radio galaxies; this is difficult to explain using arguments based upon projection effects alone.
Formation control of multiple unmanned vehicles based on graph theory: A Comprehensive Review

In recent years, formation control for multiple unmanned vehicles becomes an active research topic that has received a lot of attention from scientists due to its superior advantages compared with other conventional systems. Algebraic graph and graph rigidity theories are the two main mathematical backgrounds of the formation control theory. The graph theory is used to describe the interconnections among vehicles in formation while rigid graph theory - an important subset of graph theory - ensured that the inter-vehicle distance constraints of the desired formation are enforced via the graph rigidity. This paper provides a comprehensive review of graph theory supporting formation control for groups of unmanned aerial vehicles (UAV) or swarm UAVs. The background of the theory and the recent developments of graph-theory-based formation control are reviewed. We provide a cohesive overview of the formation control and coordination of multiple vehicles. Finally, some challenges and future potential directions in formation control are discussed.
Overconfidence Bias dalam Pengembilan Keputusan Investasi: Peran Perbedaan Gender

Tujuan dari penelitian ini adalah untuk melihat bagaimana overconfidence bias mempengaruhi keputusan investasi investor individu di Indonesia. Dengan 298 investor saham yang sesuai dengan kebutuhan sampel penelitian, teknik analisis yang digunakan dalam penelitian ini adalah PLS-MGA dengan menggunakan tool WarpPLS 7.0. Uji validitas dan reliabilitas dilakukan sebelum hipotesis penelitian divalidasi (validitas konvergen, validitas diskriminan, reliabilitas komposit, pengaruh langsung, dan analisis multikelompok). Bias terlalu percaya diri memiliki pengaruh yang menguntungkan pada pengambilan keputusan investasi, menurut temuan pengujian hipotesis penelitian, dan fungsi moderasi gender divalidasi. Temuan ini menyiratkan bahwa perilaku bias terlalu percaya diri investor mengarah pada keputusan investasi yang tidak rasional, dan bahwa investor pria lebih rentan daripada investor wanita untuk terlalu percaya diri dalam keputusan investasi mereka.
Self-normalized Cramér-type large deviations for independent random variables

Let X 1 , X 2 ,. be independent random variables with zero means and finite variances. It is well known that a finite exponential moment assumption is necessary for a Cramer-type large deviation result for the standardized partial sums. In this paper, we show that a Cramer-type large deviation theorem holds for self-normalized sums only under a finite (2 + δ)th moment, 0 < δ ≤ 1. In particular, we show P(S n /V n ≥ x) = (1 - Φ(x))(1 + O(1)(1 + x) 2+δ /d 2+δ n,δ ) for 0 ≤ x ≤ d n,δ , where d n,δ = (Σ n i=1 EX 2 i ) 1/2 /(Σ n i=1 E|X i | 2+δ ) 1/(2+δ) and V n = (Σ n i=1 X 2 i ) 1/2 . Applications to the Studentized bootstrap and to the self-normalized law of the iterated logarithm are discussed.
Evaluation of Putative Hybrid Hatchlings between Hawksbill Sea Turtle (Eretmochelys imbricata) and Green Sea Turtle (Chelonia mydas) in Tortuguero, Costa Rica

Abstract. Although hybridization processes in the Cheloniidae family have been documented since the 19th century, detailed reports of these occurrences are scarce. Therefore, the record of a hybridization between Green Sea Turtles (Chelonia mydas) and Hawksbill Sea Turtles (Eretmochelys imbricata) signifies an important event. In this study, we report the third known record of hybridization between C. mydas and E. imbricata in the Caribbean Sea. In Tortuguero, Costa Rica, we marked and monitored several nests from both species during the 2020 nesting season. Offspring from two E. imbricata clutches showed morphological characteristics corresponding to both species. We compared 20 individuals from each of these nests to large groups of pure C. mydas and E. imbricata individuals. We measured carapace length and mass of each hatchling and documented other morphological properties such as scale patterns on the head to better identify the species. Because these hatchlings presented different combinations of representative characteristics of each species, we concluded that they are hybrid individuals. Our findings provide a new record of a C. mydas · E. imbricata hybridization event.
In Vitro Structure–Activity Relationships Between Dammarane-Type Saponins Isolated From Panax notoginseng and Their Anti-inflammatory Properties

Objectives: Total saponin extracts from the roots of Panax notoginseng have been proven to have anti-inflammatory properties. Dammarane-type saponins isolated from the roots of P notoginseng have been shown to be active inhibitors of nitric oxide (NO) and tumor necrosis factor-α (TNF-α). To clarify the structural requirements for inhibition, some structure–activity relationships (SARs) were determined. Methods: RAW264.7 macrophages were maintained in vitro under standard cell culture conditions. The release of NO and TNF-α from these cells was induced by lipopolysaccharides (LPS) and was measured. The primary SAR inhibitory activity of LPS-induced NO and TNF-α and production in RAW264.7 macrophage cells were evaluated by cytotoxicity assays. Results: SAR studies of dammarane-type saponins revealed that the glycosides substituent at C-3, C-6, and C-20 were important features for the NO and TNF-α production in RAW264.7 cells induced LPS. Moreover, the 5(6)-double bond and the OH group at C-7 improved the TNF-α inhibitory activity. In the case of ginsenoside Rh2, the C-20 configuration was needed for potent TNF-α inhibition. Conclusions: These results provide an approach for understanding the structural requirements of dammarane-type saponins isolated from P notoginseng for optimum anti-inflammatory properties of these compounds.
Imaging of cooperative motion on a simulated energy landscape

The transport through a modulated energy landscape is a classical problem that, in different forms, arises in different biological systems, such as protein folding and the motion of molecular motors. The calculations become increasingly difficult if a realistic potential landscape is taken into account and in many experimental settings the control of parameters is not possible. We propose and experimentally demonstrate an experimental apparatus that allows direct simulation and imaging of stochastic molecular motion on a time-dependent energy landscape.
Coherence measurements of a Nd-YAG laser by image processing of Michelson interferometer fringe patterns

To determine the coherence of a laser device, the concept of visibility is used and applied to the measured interference patterns produced by this laser light in a specific interferometer. In this case, to determine the coherence of a Diode Pumped Solid State Nd -YAG Laser, a digital image processing procedure was developed and applied to the fringe patterns obtained with a rigid arm Michelson interferometer array in order to obtain the coherence function. A spatial filtered laser beam was used to illuminate the interferometer and to produce a circular fringe pattern. The interference pattern was captured on a spatial calibrated translucent screen and photographed by a digital camera. Afterwards, the images were digitally processed to calibrate the pixel distribution and to produce an intensity function from this calibrated image. Data processing of the intensity function values allows to produce the coherence function and to evaluate the coherence degree of the radiation. The interference patterns, intensity distributions and the coherence calculations of the Nd - YAG laser are presented.
Symmetric Part Preconditioning of the CG Method for Stokes Type Saddle-Point Systems

A nonsymmetric formulation of saddle-point systems is considered, and symmetric part preconditioning of the CG method is applied. Linear and superlinear convergence estimates are derived for the finite element solution of the Stokes problem and of Navier's equations of elasticity. Mesh independence of the estimates is obtained from proper Hilbert space operator background.
Characterization of Climatic Drought Sequences in the Upper Moulouya Watershed, Morocco

Like most of the countries of the African continent and the MENA, Morocco has experienced alternating wet and dry periods for several decades and is still confronted with the effects of unstable climate change due to the speciﬁcities conferred by its geographical position and the diversity of its ecosystems. It is one of the countries most affected by desertiﬁcation, with an arid and semi-arid climate covering more than 93% of its territory. Indeed, the Upper Moulouya watershed has been exposed to severe droughts several times in recent decades. The spatial and temporal distribution of drought episodes in this watershed is studied over a 91-year period between 1931 and 2022. In order to characterize and evaluate the severity and sustainability of drought in this watershed, four indices were used and applied in this study, as they have advantages in terms of statistical consistency and have the capac - ity to describe, through different time scales (short, medium and long) the impacts of the climatic drought in ques - tion. These are the Standardized Precipitation Index SPI, RDI, RI and DI. The annual rainfall series at the eight meteorological stations of the said watershed show irregularities and very marked spatial and temporal variability with a generally decreasing trend. The SPI calculation results obtained show a heterogeneous distribution of SPI values throughout the watershed area. The analysis of the graphical illustrations of this index allowed to highlight an important fluctuation of the dry and wet periods with a strong dominance and tendency to drought with the order of 51% in the stations of Midelt, and Ansegmir, 52% in the station of Zaida, 59% in the stations of Tabouazant, Barrage (Dam) Enjil and El Aouia, 58% in the station of Louggagh, 47% in the station of Anzar Oufounes. The analysis of the results of the of the drought indices RDI, RI and DI at the level of this watershed also made it possible to high - light the existence of numerous drought sequences alternating with other wet sequences and indicates a dominance of dry years, perfectly remarkable during the period 1976-93. The most important dry episode, in number of succes - sive years, was recorded at the Ansegmir station from 1976-89 and the most important rainy episode was recorded at the Midelt station from 1966-76. The years of the 2015-2022 series show an overall persistent decrease in rainfall, thus allowing the installation of a severe drought episode. The trend in the entire watershed is a decrease in rainfall and the installation of mild, moderate and severe drought episodes of varying length and duration.
Dijitalleşme Sürecinde Değişen Eğitim Pratikleri ve Bu Değişimin Taraflara Etkisi

Bu çalışmada, eğitim alanında yaşanan dijital dönüşüm süreçlerinin, eğitimcilerin ve ailelerin rollerini nasıl ve ne yönde etkilediği sorusuna cevap aranmak istenmiş; eğitimcilerin ve ailelerin bu süreçte karşılaştıkları problemler ile uzaktan eğitim pratiklerinin beğenilen ve eleştirilen yönlerinin ortaya konulması amaçlamıştır. Bu temel amaç çerçevesinde araştırma, uzaktan eğitim ve eğitim teknolojileri konularında ilköğretim öğrencilerinin ailelerinden daha fazla destek alabileceği hipotezinden hareketle ilköğretimde eğitim gören öğrencilerin velileri ile ilköğretimde hizmet veren öğretmenler ile sınırlandırılmıştır. Araştırmanın çalışma grubu, grubu amaçlı örnekleme yöntemleri arasında yer alan ölçüte dayalı örnekleme tekniği dikkate alınarak belirlenmiştir. Araştırmanın çalışma grubundaki katılımcıların her biri bir coğrafi bölgede görevli ilköğretim düzeyinde eğitimci ve ilköğretim öğrenci velilerinden oluşmaktadır. Araştırmada, nitel araştırma metodolojisi uygulanmış ve tek turlu yarı yapılandırılmış görüşme tekniği kullanılmıştır. Görüşme formu olarak, araştırmacılar tarafından geliştirilen yarı yapılandırılmış görüşme formu kullanılmıştır. Yaşları ve eğitim durumları farklı toplam 7 ilköğretim düzeyinde eğitimci olarak çalışan ve 7 ilköğretim düzeyi öğrenci velisi olmak üzere 14 kişi ile görüşme yapılmıştır.
Data Augmentation Considering Distribution Discrepancy for Fault Diagnosis of Drilling Process With Limited Samples

The fault diagnosis during drilling is necessary to prevent the accidents develop to more serious status. Data-driven diagnosis methods have great advantages in nonlinear industrial process, however, the problem of limited samples restricts its further application. This article proposes a data augmentation method based on synthetic data generation and updating for drilling fault diagnosis with limited samples. First, the generator is trained with generative adversarial nets (GAN), and the GAN is improved by the design of parameter selection module, and loss function in generative model. Then, sufficient samples are obtained, and a balanced dataset is constructed for modeling. Meanwhile, by considering the distribution discrepancy, the self-organizing incremental neural network-based synthetic data updating is realized to track the changes of data distribution when the data drift appears. Finally, the actual data acquired from two wells are employed for the method validation. The experimental results illustrate that the proposed method is helpful for improving the performance of diagnosis model with limited samples, and the negative impact to diagnosis model due to the distribution discrepancy also can be overcome.
A Least Squares Estimator for Lévy-driven Moving Averages Based on Discrete Time Observations

This article is concerned with a least squares estimator (LSE) of the kernel function parameter θ for a Lévy-driven moving average of the form X(t) = ∫t− ∞K(θ(t − s)) dL(s), where is a Lévy process without the Brownian motion part, K is a kernel function and θ > 0 is a parameter. Let h be the time span between two consecutive observations and let n be the size of sample. As h → 0 and nh → ∞, consistency and asymptotic normality of the LSE are studied. The small-sample performance of the LSE is evaluated by means of a simulation experiment. Finally, two real-data applications show that the Lévy-driven moving average gives a good approximation to the autocorrelation of the process.
The structure factor in small‐angle scattering and the effect of deviation from spherical symmetry

The effect of deviation from spherical symmetry is studied for the structure factor. This is done by combining the analytical expression for the excluded volume of an ellipsoid of revolution with the expression for the excluded volume correlation function for a sphere. This approach makes it possible to estimate the effect of small deviations from spherical symmetry as a function of axial ratio and volume fraction for relatively low volume fractions. The calculations are relevant for the case of short-range potentials where the Percus–Yevick formula is frequently applied, and indicate that even minor deviations from spherical symmetry may lead to significant effects on the structure factor at low scattering angles.
Modèles neuronaux pré-appris par auto-supervision sur des enregistrements de parole en français

Self-supervised pre-trained neural networks with French speech recordings Self-Supervised Learning (SSL) has yielded remarkable improvements in many different domains including computer vision, natural language processing and speech processing. Models pre-trained on a large amount of unlabeled data can be further fine-tuned with very small manually-labeled datasets. Wav2Vec2.0 models showed very remarkable results for automatic speech recognition. The very first models shared to the community had been trained on English or multiple languages. We offer, in this paper, seven wav2vec2.0 models, trained on 1 000, 3 000 and 7 000 hours of French speech. Considering that the training of such models requires large computing capacities, and in a spirit of open science, our French models are freely available. Experimental results on ASR are also displayed, confirming the benefit of using such pre-trained models. MOTS-CLÉS : Modèles Autosupervisés, Reconnaissance Automatique de la Parole. *. note aux relecteurs : cette soumission est une sous-partie ré-adaptée d’un article publié à Neurips fin 2021. Nous avons tenu à faire cet effort afin de présenter ces travaux sur des modèles pour le traitement du français à la communauté francophone. Il ne s’agit pas d’une simple traduction : certains détails et informations sont précisés ici qui étaient absents de l’article original. XXXIVe Journées d’Études sur la Parole – JEP 2022 13-17 June 2022, Île de Noirmoutier, France 424 10.21437/JEP.2022-45
The variance constant for the actual waiting time of the $PH/PH/1$ queue

In this paper we consider the asymptotic time average variance of the actual waiting time of the PH/PH/1 queue. To this end Poisson’s equation plays a major role, since the solution to Poisson’s equation is shown to be intimately connected with the desired variance. We derive explicit solutions to Poisson’s equation using phase-type methodology, and present some numerical examples.
High performance optical absorber based on a plasmonic metamaterial

High absorption efficiency is particularly desirable at present for various microtechnological applications including microbolometers, photodectors, coherent thermal emitters, and solar cells. Here we report the design, characterization, and experimental demonstration of an ultrathin, wide-angle, subwavelength high performance metamaterial absorber for optical frequencies. Experimental results show that an absorption peak of 88% is achieved at the wavelength of ∼1.58 μm, though theoretical results give near perfect absorption.
COVID-19 classification using hybrid deep learning and standard feature extraction techniques

There is no doubt that COVID-19 disease rapidly spread all over the world, and effected the daily lives of all of the people. Nowadays, the reverse transcription polymerase chain reaction is the most way used to detect COVID-19 infection. Due to time consumed in this method and material limitation in the hospitals, there is a need for developing a robust decision support system depending on artificial intelligence (AI) techniques to recognize the infection at an early stage from a medical images. The main contribution in this research is to develop a robust hybrid feature extraction method for recognizing the COVID-19 infection. Firstly, we train the Alexnet on the images database and extract the first feature matrix. Then we used discrete wavelet transform (DWT) and principal component analysis (PCA) to extract the second feature matrix from the same images. After that, the desired feature matrices were merged. Finally, support vector machine (SVM) was used to classify the images. Training, validating, and testing of the proposed method were performed. Experimental results gave (97.6%, 98.5%) average accuracy rate on both chest X-ray and computed tomography (CT) images databases. The proposed hybrid method outperform a lot of standard methods and deep learning neural networks like Alexnet, Googlenet and other related methods.
Variable step-size multichannel frequency-domain LMS algorithm for blind identification of finite impulse response systems

The choice of step size is a critical factor in blind identification of single-input multi-output systems using adaptive multichannel frequency-domain least-mean-squares (MCFLMS) algorithm. The proper step size is dependent on the signal power and it influences the speed of convergence and the final misalignment error. Applying normalised MCFLMS (NMCFLMS) algorithm, the dependency of the step-size parameter on signal power can be eliminated; however, it is shown that even for a moderate signal-to-noise ratio, the algorithm fails to converge to the eigenvector corresponding to the minimum eigenvalue of the data correlation matrix and hence misconverges to a fictitious solution. A self-adaptive variable step-size MCFLMS (VSS-MCFLMS) algorithm that optimises the performance of the algorithm in each iteration in order to achieve minimum misalignment with the true channel impulse response is proposed. The convergence analysis of the proposed algorithm is performed and it is shown that the VSS-MCFLMS algorithm converges, both in noise-free and noisy conditions, to the eigenvector corresponding to the minimum eigenvalue and is therefore more noise robust when compared with the NMCFLMS. The algorithm also shows the optimal speed of convergence in a noise-free case and the proposed variable step size guarantees the stability of the algorithm. Simulation results are presented to demonstrate that the proposed VSS-MCFLMS algorithm gives lower final misalignment when compared with the NMCFLMS algorithm without sacrificing the speed of convergence and computational efficiency.