Miracle-dz/UniFAR-FTUs huggingface dataset:
MINISTERIO DE CULTURA Y EDUCACION FUNDACION MIGUEL LILLO NEW MATERIALS OF LAGOSUCHUS TALAMPAYENSIS ROMER (THECODONTIA-PSEUDOSUCHIA) AND ITS SIGNIFICANCE ON THE ORIGIN J. F. BONAPARTE OF THE SAURISCHIA. LOWER CHANARIAN, MIDDLE TRIASSIC OF ARGENTINA ACTA GEOLOGICA LILLOANA

On the basis of new remains of Lagosuchus that are thoroughly described, including most of the skeleton except the manus, it is assumed that Lagosuchus is a form intermediate between Pseudosuchia and Saurischia. The presacral vertebrae show three morphological zones that may be related to bipedality: 1) the anterior cervicals; 2) short cervico-dorsals; and 3) the posterior dorsals. The pelvis as a whole is advanced, in particular the pubis and acetabular area of the ischium, but the ilium is rather primitive. The hind limb has a longer tibia than femur, and the symmetrical foot is as long as the tibia. The tarsus is of the mesotarsal type. The morphology of the distal area of the tibia and fibula, and the proximal area of the tarsus, suggest a stage transitional between the crurotarsal and mesotarsal conditions. The forelimb is proportionally short, 48% of the hind limb. The humerus is slender, with advanced features in the position of the deltoid crest. The radius and ulna are also slender, the latter with a pronounced olecranon process. A new family of Pseudosuchia is proposed for this form: Lagosuchidae. Its level of organization is discussed in some detail and the transitional condition of Lagosuchus between Pseudosuchia and Saurischia is analyzed. The possible relationships of Lagosuchus with Triassic saurischians are discussed according to the information from vertebral zonation, pelvis, hind limb, and disparity of the limbs. It is concluded that Lagosuchus is part of a thecodont radiation that probably produced both orders of dinosaurs. * Original citation: Bonaparte, J. F. 1975. Nuevos materiales de Lagosuchus talampayensis Romer (Thecodontia Pseudosuchia) y su significado en el origen de los Saurischia, Chañarense Inferior, Triasico Medio de Argentina. Acta Geologica Lilloana 13(1):5-90. Translated by Matthew Carrano, Department of Organismal Biology and Anatomy, University of Chicago, August 1996.
Accurate Range Query With Privacy Preservation for Outsourced Location-Based Service in IoT

With the maturity of Internet-of-Things technology, location-based service (LBS) is developing rapidly in intelligent terminal devices, and it brings new vitality to the fields of logistics, transportation, product traceability and so on. The popularity of LBS produces a lot of spatial data, which inevitably brings burden to the storage and management of LBS provider (LBSP). With the help of cloud computing and cloud storage, outsourcing spatial data to cloud server has become a new trend. However, due to the cloud server is not trusted, data outsourcing will face the problems of data disclosure and query disclosure. Range query is a common query in LBS, considering the situation of data outsourcing, this article proposes an accurate range query (ARQ) scheme, which can realize efficient range query while preserving LBSP’s data privacy and user’s query privacy from being disclosed to the cloud server. The ARQ scheme is suitable for spatial data in any form without being limited to the case that the data points are only integers, which has a certain practical significance. In addition, by dividing the region into atomic regions, ARQ can realize sublinear search time and ensure dynamic update of spatial data. We proved the security of the proposed scheme through security analysis, and demonstrated the effectiveness of the scheme through experiments.
H Long Terminal Repeat-Associating 2 (HHLA2) is a Biomarker of Advanced Stage Hepatocellular Carcinoma and Promotes Tumor Cell Development In Vitro

Background Several risk factors contribute to the inflammation promoting hepatocellular carcinoma (HCC) development, but the underlying mechanisms are unknown. Human endogenous retrovirus H long terminal repeat-associating 2 (HHLA2), a B7 family member, is highly expressed in various malignant tumor tissues and is related to tumor progression and metastasis. Material/Methods Bioinformatics analysis was used to analyze the gene expression chip GSE33006 of HCC tissue in the GEO database, draw a heat map of differentially expressed genes, and analyze the GO pathway of gene function annotation. Then, we compared HCC tissues with para-carcinoma liver tissues from 55 patients for expression patterns and associations with HHLA2. Effects of HHLA2 knockdown were examined in the human HCC cell line HepG2 to explore effects of HHLA2 on HepG2 cells. Results A significantly higher expression of HHLA2 at the mRNA and protein levels was detected in HCC tissues than in para-carcinoma liver tissues, which was similar to HHLA2 expression in the GSE33006 data. A higher expression of HHLA2 protein was associated with advanced cancer stage, tumor differentiation, and invasion of adjacent structures. In vitro knockdown of HHLA2 expression significantly increased HepG2 cell adhesion, promoted cell apoptosis, induced cell cycle arrest in the G1/S phase, and inhibited cell proliferation, migration, and invasion. Conclusions Our data indicated there was a higher expression of HHLA2 in HCC tissues than in para-carcinoma liver tissues, and HHLA2 plays a major role in the development and progression of HCC. Owing to its higher expression, HHLA2 is a potential prognostic biomarker for HCC.
Cortical and Subcortical Brain Volume Alterations Following Endurance Running at 38.6 km and 119.2 km in Male Athletes

Background Although several studies have shown that ultramarathon running causes severe physical and mental stress and harms organ systems, its effect on brain tissue remains unclear. The purpose of this study was to investigate the volumetric change of cortical and subcortical brain structures following 38.6-km and 119.8-km mountain races. Material/Methods A total of 23 healthy male runners (age, 49.05±5.99 years) were classified as short-trail (ST; n=9) and ultra-trail (UT; n=14) endurance running. Pre- and post-test scanning of brain tissue was performed by using a 3-Tesla magnetic resonance imaging (MRI). Pre- and post-race differences in cortical and subcortical volumes in the ST and UT groups were separately determined by Wilcoxon signed-rank test. Results Cortical gray matter (GM) and cerebral GM volume significantly increased after the race in both ST and UT groups, whereas the volume of the thalamus, caudate, pallidus, and hippocampus significantly increased only in the UT group. Cerebrospinal fluid (CSF) and white-matter (WM) volumes did not change after endurance running and remained unaltered in both groups. Conclusions Endurance running has a site-specific acute effect on cortical and subcortical structures and may attenuate GM volume decrease in older adult male athletes. The increased volume of subcortical structures might be a response of physical exercise and additional physical stress experienced by ultramarathon runners.
Characteristics of menthone encapsulated complex by mungbean, tapioca, and rice starches

ABSTRACT In order to use commercial starch for flavor encapsulation, three starches (mungbean, rice, and tapioca) were selected to determine their ability to form inclusion complex with menthone. Mungbean starch had the highest amylose content, followed by rice starch and tapioca starch, respectively. After complexation, appearance and microstructure changes were observed for all starches. Mungbean and tapioca complexes with well-organized V7 crystalline pattern gave similar menthone entrapment (around 4%) which was higher than that of the rice sample (<1%). Molecular size distribution of the complexes revealed the role of amylose fractions for both mungbean and tapioca complexes. According to differential scanning calorimetric analysis, menthone complexes formation was confirmed by a thermoreversible event. Regarding the high yield of starch precipitate and menthone entrapment, mungbean starch is considered a more suitable material for menthone encapsulation rather than tapioca and rice starches.
Rigorous computation of escape times for parameter intervals in the quadratic map

We study the quadratic family of one-dimensional maps $f_a (x) = a - x^2$. We conduct comprehensive numerical analysis of collections of finite orbits of the critical point, computed for intervals of parameter values using rigorous numerical methods. We use the computer to explicitly construct a collection of several thousand parameter intervals, contained in $\Omega=[1.4, 2]$, that are proved to have a specific so-called escape time, which roughly means that some effectively computed iterate of the critical point taken over all the parameters in that interval has considerable width in the phase space. In particular, we compute a rigorous lower bound on this width, in addition to the upper bound. We investigate the effect of certain constraints imposed on the numerical computations upon the resulting collection of intervals. Additionally, we illustrate and discuss the distribution of the computed intervals in the parameter space. The purpose of our work is to establish grounds for further numerical computation of a lower bound on the measure of stochastic parameters in $\Omega$. The source code of the software and the data discussed in the paper are freely available at http://www.pawelpilarczyk. com/quadr/. This web page also allows carrying out some limited computations. The ideas and procedures introduced in the paper can be easily generalised to apply to other parametrised families of dynamical systems.
Generalized Spoofing Detection Inspired from Audio Generation Artifacts

State-of-the-art methods for audio generation suffer from fingerprint artifacts and repeated inconsistencies across temporal and spectral domains. Such artifacts could be well captured by the frequency domain analysis over the spectrogram. Thus, we propose a novel use of long-range spectro-temporal modulation feature -- 2D DCT over log-Mel spectrogram for the audio deepfake detection. We show that this feature works better than log-Mel spectrogram, CQCC, MFCC, as a suitable candidate to capture such artifacts. We employ spectrum augmentation and feature normalization to decrease overfitting and bridge the gap between training and test dataset along with this novel feature introduction. We developed a CNN-based baseline that achieved a 0.0849 t-DCF and outperformed the previously top single systems reported in the ASVspoof 2019 challenge. Finally, by combining our baseline with our proposed 2D DCT spectro-temporal feature, we decrease the t-DCF score down by 14% to 0.0737, making it a state-of-the-art system for spoofing detection. Furthermore, we evaluate our model using two external datasets, showing the proposed feature's generalization ability. We also provide analysis and ablation studies for our proposed feature and results.
Periodic Load Rejection for Floating Offshore Wind Turbines via Constrained Subspace Predictive Repetitive Control *

Individual Pitch Control (IPC) is an effective control strategy to mitigate the blade loads on large-scale wind turbines. Since IPC usually requires high pitch actuation, the safety constraints of the pitch actuator should be taken into account when designing the controller. This paper introduces a constrained Subspace Predictive Repetitive Control (SPRC) approach, which considers the limitation of blade pitch angle and pitch rate. To fulfill this goal, a model predictive control scheme is implemented in the fully data-driven SPRC approach to incorporate the physical limitations of the pitch actuator in the control problem formulation. An optimal control law subjected to constraints is then formulated so that future constraint violations are anticipated and prevented. Case studies show that the developed constrained SPRC reduces the pitch activities necessary to mitigate the blade loads when experiencing wind turbulence and abrupt wind gusts. More importantly, the approach allows the wind farm operator to design conservative bounds for the pitch actuator constraints that satisfies safety limitations, design specifications and physical restrictions. This will help to alleviate the cyclic fatigue loads on the actuators, increase the structural reliability and extend the lifespan of the pitch control system.
Cascade-Forward Neural Network Based on Resilient Backpropagation for Simultaneous Parameters and State Space Estimations of Brushed DC Machines

Hacene MELLAH1,2*, Kamel Eddine HEMSAS2, Rachid TALEB3 Electrical engineering department, faculty of sciences and applied sciences, University of Akli Mouhand Oulhadj-Bouira, Algeria 2 Department of Electrical Engineering, Faculty of Technology, Ferhat Abbas Sétif 1 University, LAS laboratory, Sétif, Algeria 3 Department of Electrical Engineering, LGEER Laboratory, Hassiba Benbouali University, Chlef, Algeria
Multiwavelength Follow-up of FRB180309

We report on the results of multiwavelength follow-up observations with Gemini, Very Large Array (VLA), and Australia Telescope Compact Array to search for a host galaxy and any persistent radio emission associated with FRB 180309. This FRB is among the most luminous FRB detections to date, with a luminosity of >8.7 × 1032 erg Hz−1 at the dispersion-based redshift upper limit of 0.32. We used the high-significance detection of FRB 180309 with the Parkes Telescope and a beam model of the Parkes Multibeam Receiver to improve the localization of the FRB to a region spanning approximately ∼2′×2′ . We aimed to seek bright galaxies within this region to determine the strongest candidates as the originator of this highly luminous FRB. We identified optical sources within the localization region above our r-band magnitude limit of 24.27, 14 of which have photometric redshifts whose fitted mean is consistent with the redshift upper limit (z < 0.32) of our FRB. Two of these galaxies are coincident with marginally detected “persistent” radio sources of flux density 24.3 μJy beam−1 and 22.1 μJy beam−1, respectively. Our redshift-dependent limit on the luminosity of any associated persistent radio source is comparable to the luminosity limits for other localized FRBs. We analyze several properties of the candidate hosts we identified, including chance association probability, redshift, and presence of radio emission; however, it remains possible that any of these galaxies could be the host of this FRB. Follow-up spectroscopy on these objects to explore their Hα emission and ionization contents, as well as to obtain more precisely measured redshifts, may be able to isolate a single host for this luminous FRB.
Hybrid Comptonization and Electron–Positron Pair Production in the Black-hole X-Ray Binary MAXI J1820+070

We study X-ray and soft gamma-ray spectra from the hard state of the accreting black-hole binary MAXI J1820+070. We perform an analysis of two joint spectra from NuSTAR and INTEGRAL, covering the range of 3–650 keV, and of an average joint spectrum over the rise of the hard state, covering the 3–2200 keV range. The spectra are well modeled by Comptonization of soft seed photons. However, the distributions of the scattering electrons are not purely thermal; we find they have substantial high-energy tails, well modeled as power laws. The photon tail in the average spectrum is detected well beyond the threshold for electron–positron pair production, 511 keV. This allows us to calculate the rate of the electron–positron pair production and put a lower limit on the size of the source from pair equilibrium. At the fitted Thomson optical depth of the Comptonizing plasma, the limit is about 4 gravitational radii. If we adopt the sizes estimated by us from the reflection spectroscopy of >20 gravitational radii, the fractional pair abundance becomes much less than unity. The low pair abundance is confirmed by the lack of both an annihilation feature and of a pair absorption cutoff above 511 keV in the average spectrum.
Peaks are preserved under run-sorting

We study a sorting procedure (run-sorting) on permutations, where runs are rearranged in lexicographic order. We describe a rather surprising bijection on permutations on length n, with the property that it sends the set of peak-values (also known as the pinnacle set) to the set of peak-values after run-sorting. We also prove that the expected number of descents in a permutation σ ∈ Sn after run-sorting is equal to (n−2)/3. Moreover, we provide a closed-form of the exponential generating function introduced by Nabawanda, Rakotondrajao, and Bamunoba in 2020, for the number of run-sorted permutations of [n], (RSP(n)) having k runs, which gives a new interpretation to the sequence http://oeis.org/A124324. We show that the descent generating polynomials, An(t) for RSP(n) are real rooted, and satisfy an interlacing property similar to that satisfied by the Eulerian polynomials. Finally, we study run-sorted binary words and compute the expected number of descents after run-sorting a binary word of length n.
AlephBERT: A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application With

Large Pre-trained Language Models (PLMs) have become ubiquitous in the development of language understanding technology and lie at the heart of many artificial intelligence advances. While advances reported for English using PLMs are unprecedented, reported advances using PLMs in Hebrew are few and far between. The problem is twofold. First, Hebrew resources available for training NLP models are not at the same order of magnitude as their English counterparts. Second, there are no accepted tasks and benchmarks to evaluate the progress of Hebrew PLMs on. In this work we aim to remedy both aspects. First, we present AlephBERT, a large pre-trained language model for Modern Hebrew, which is trained on larger vocabulary and a larger dataset than any Hebrew PLM before. Second, using AlephBERT we present new state-of-the-art results on multiple Hebrew tasks and benchmarks, including: Segmentation, Part-of-Speech Tagging, full Morphological Tagging, Named-Entity Recognition and Sentiment Analysis. We make our AlephBERT model publicly available, providing a single point of entry for the development of Hebrew NLP applications.
Fundamental Studies on Hydrogen Production in the Acid-Forming Phase and Its Bacteria in Anaerobic Treatment Processes – The Effects of Solids Retention Time

Effects of solids retention time (SRT) on hydrogen gas production, glucose degradation and anaerobic bacteria in anaerobic treatment processes were investigated with use of 11,700 mg/l glucose solution as a substrate. Five series of experiments were conducted at 36 ± 1°C. Volatile fatty acids were produced in the order: acetic, n-butyric, propionic and n-valeric with concentration of effluent. Counts of general anaerobic bacteria and acid-forming bacteria in each reactor increased with increasing SRT, and counts of genus Clostridium and sulphate reducing bacteria in the reactor decreased with increasing SRT of the reactor. Gas production rates and gas composition were investigated to obtain information on energy production. Solids retention time increased from 2 to 10 h, hydrogen gas content decreased from 12 to 9%. The kinetic constants such as the microbial decay coefficient (K d ), the maintenance coefficient (m) and the growth yield for microorganisms (Y G ) were 4.04,41.7 and 0.097 day −1 , respectively.
Joint analysis of anisotropic power spectrum, bispectrum and trispectrum: application to N-body simulations

We perform for the first time a joint analysis of the monopole and quadrupoles for power spectrum, bispectrum and integrated trispectrum (i-trispectrum) from the redshift space matter field in N-body simulations. With a full Markov Chain Monte Carlo exploration of the posterior distribution, we quantify the constraints on cosmological parameters for an object density of n p = 5 × 10-4 (h Mpc-1)3, redshift z = 0.5, and a covariance corresponding to a survey volume of V survey = 25 (h -1Gpc)3, a set up which is representative of forthcoming galaxy redshift surveys. We demonstrate the complementarity of the bispectrum and i-trispectrum in constraining key cosmological parameters. In particular, compared to the state-of-the-art power spectrum (monopole plus quadrupole) and bispectrum (monopole) analyses, we find 1D 68% credible regions smaller by a factor of (72%,78%,72%,47%,46%) for the parameters (f,σ8,f nl,α∥,α⊥) respectively. This work motivates the additional effort necessary to include the redshift-space anisotropic signal of higher-order statistics in the analysis and interpretation of ongoing and future galaxy surveys.
Know What and Know Where: An Object-and-Room Informed Sequential BERT for Indoor Vision-Language Navigation

Vision-and-Language Navigation (VLN) requires an agent to navigate to a remote location on the basis of natural-language instructions and a set of photo-realistic panoramas. Most existing methods take words in instructions and discrete views of each panorama as the minimal unit of encoding. However, this requires a model to match different textual landmarks in instructions (e.g., TV, table) against the same view feature. In this work, we propose an object-informed sequential BERT to encode visual perceptions and linguistic instructions at the same ﬁne-grained level, namely objects and words, to facilitate the matching between visual and textual entities and hence “know what”. Our sequential BERT enables the visual-textual clues to be interpreted in light of the temporal context, which is crucial to multi-round VLN tasks. Additionally, we enable the model to identify the relative direction (e.g., left/right/front/back) of each navigable location and the room type (e.g., bedroom, kitchen) of its current and ﬁnal navigation goal, namely “know where”, as such information is widely mentioned in instructions implying the desired next and ﬁnal locations. Extensive experiments demonstrate the effectiveness compared against several state-of-the-art methods on three indoor VLN tasks: REVERIE, NDH, and R2R.
Replay in Deep Learning: Current Approaches and Missing Biological Elements

Abstract Replay is the reactivation of one or more neural patterns that are similar to the activation patterns experienced during past waking experiences. Replay was first observed in biological neural networks during sleep, and it is now thought to play a critical role in memory formation, retrieval, and consolidation. Replay-like mechanisms have been incorporated in deep artificial neural networks that learn over time to avoid catastrophic forgetting of previous knowledge. Replay algorithms have been successfully used in a wide range of deep learning methods within supervised, unsupervised, and reinforcement learning paradigms. In this letter, we provide the first comprehensive comparison between replay in the mammalian brain and replay in artificial neural networks. We identify multiple aspects of biological replay that are missing in deep learning systems and hypothesize how they could be used to improve artificial neural networks.