Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Dietary Selenium Deficiency or Excess on Selenoprotein Gene Expression in the Spleen Tissue of Pigs

Simple Summary Selenium (Se) is an essential nutrient for humans and many other species including pigs, and it plays an important role in many aspects of biological functions especially in the immune system. The metabolic roles of Se implement its biological function through its incorporation into selenoproteins, that contain a unique amino acid selenocysteine (Sec). Although 24–25 selenoprotein genes have been identified in mammals as the most important immune organ, the expression pattern of selenoproteins in the spleen of pigs regulated by different Se levels remains poorly known. The present study is conducted to evaluate the effects of dietary Se deficiency and excess on the mRNA expression levels of selenoproteins in pig spleen tissues. The results show that dietary Se levels can significantly affect the transcription levels of 14 selenoprotein genes in the spleen of pigs, which can help us to understand the biological functions of Se further and improve the use of Se in livestock production. Abstract To evaluate the effects of dietary Se deficiency and excess on the mRNA levels of selenoproteins in pig spleen tissues, 20 healthy uncastrated boars (Duroc × Landrace × Yorkshire, 10 ± 0.72 kg) were randomly divided into four groups (5 pigs per group). The pigs were fed a Se deficient corn-soybean basal feed (Se content <0.03 mg/kg) or basal feed with added sodium selenite at 0.3, 1.0, or 3.0 mg Se/kg diet, respectively. The experiment lasted 16 weeks. The spleen tissue was collected to examine the mRNA expression levels of 24 selenoprotein genes at the end of the study. Compared with pigs in other groups, those fed with the 1.0 mg Se/kg diet had higher mRNA levels of glutathione peroxidase 1 (Gpx1), glutathione peroxidase 2 (Gpx2), deiodinase type II (Dio2), thioredoxin reductase 3 (Txnrd3), selenoprotein H (Selh), selenoprotein N, 1 (Sepn1), selenoprotein P1 (Sepp1), and selenoprotein V (Selv) in the spleen (p < 0.05). Dietary Se deficiency resulted in lower mRNA levels of Gpx1, Gpx2, glutathione peroxidase 3 (Gpx3), Dio2, thioredoxin reductase 2 (Txnrd2), Txnrd3, Selh, selenoprotein I (Seli), selenoprotein K (Selk), selenoprotein M (Selm), Sepn1, Sepp1, and Selv in the spleen than the other three groups. Dietary Se levels did not affect the mRNA levels of glutathione peroxidase 4 (Gpx4), deiodinase type I (Dio1), deiodinase type III (Dio3), selenophosphate synthetase 2 (Sephs2), thioredoxin reductase 1 (Txnrd1), selenoprotein O (Selo), selenoprotein S (Sels), selenoprotein W (Selw), selenoprotein X (Selx), and selenoprotein 15 (Sel15) in the spleen (p > 0.05). Dietary Se levels can affect the transcription levels of 14 selenoprotein genes in the spleen of pigs.
Multiple Sources Localization by the WSN Using the Direction-of-Arrivals Classified by the Genetic Algorithm

Simultaneously locating multiple sources passively in the wireless sensor networks (WSN) is challenging in the internet of things (IoT) applications, where reducing the computation and communication load is of great importance due to the requirement on real-time processing and the energy constraint. This is especially true when the number of sources or the number of sensor nodes is large. In this paper, a localization algorithm to estimate multiple sources’ positions in the three-dimensional space is proposed. With the direction-of-arrival (DOA) estimates for multiple sources obtained at each sensor node, it is crucial to discriminate which estimate corresponds to which source. To save the computation resources, a classification method based on the genetic algorithm is proposed to handle the multiple sources. A fitness function is designed to assess the clustering of the DOA estimates. Extensive simulations are carried out to analyze the algorithm performance under the various settings. Numerical examples show that the proposed method could lower the computational burden by orders of magnitude compared to the conventional method, without significantly sacrificing the estimation accuracy.
Mergers and Acquisitions: Causes and Effects

The aim of this research is to investigate current trend in international and italian M&A. At this aim we first recognize the main theoretical lines of research that identify determinants of M&A processes. Than, using Thompson Financial database, Zephyr database and KPMG reports, we mapped completed mergers and acquisitions (M&A) over the past decade, trying to identify main causes of the present boom. The present boom in M&A, started in 2005, bears a number of similarities as well as differences with the previous one, started in 1999. The value and number of M&A in 2005 were comparable to the averages in 1999-2001, as were the number of mega deals. The increase in global M&A in 2005 was driven by macroeconomic, microeconomic and institutional factors. The most important factor at the macroeconomic level has been continued economic growth. In 2005 in fact, world real GDP grew by 4.8% (IMF 2006). Favourable conditions in financial and stock markets prompted the growth of domestic and cross-border M&A. At the microeconomic level, a surge of financial flows to collective investment institutions, e.g. private equity funds, led to massive cross-border investments by these funds. The last part of this research is focalized on the Italian banking industry and the main cases of consolidation in this sector.
Draft Changes in rhizosphere bacterial communities associated to tree decline : grapevine esca syndrome case study

Journal: Canadian Journal of Microbiology Manuscript ID cjm-2019-0384.R1 Manuscript Type: Article Date Submitted by the Author: 16-Sep-2019 Complete List of Authors: Saccà, Maria Ludovica; CREA, Agriculture and Environment Manici, Luisa Maria; CREA, Agriculture and Environment Caputo, Francesco; CREA, Agriculture and Environment Frisullo, Salvatore; Università degli Studi di Foggia Keyword: Pseudomonas; actinomycetes; vineyards; qPCR; next generation sequencing. Is the invited manuscript for consideration in a Special Issue? : Not applicable (regular submission)
Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity

We provide a theoretical explanation for the effectiveness of gradient clipping in training deep neural networks. The key ingredient is a new smoothness condition derived from practical neural network training examples. We observe that gradient smoothness, a concept central to the analysis of first-order optimization algorithms that is often assumed to be a constant, demonstrates significant variability along the training trajectory of deep neural networks. Further, this smoothness positively correlates with the gradient norm, and contrary to standard assumptions in the literature, it can grow with the norm of the gradient. These empirical observations limit the applicability of existing theoretical analyses of algorithms that rely on a fixed bound on smoothness. These observations motivate us to introduce a novel relaxation of gradient smoothness that is weaker than the commonly used Lipschitz smoothness assumption. Under the new condition, we prove that two popular methods, namely, \emph{gradient clipping} and \emph{normalized gradient}, converge arbitrarily faster than gradient descent with fixed stepsize. We further explain why such adaptively scaled gradient methods can accelerate empirical convergence and verify our results empirically in popular neural network training settings.
A Catalog of M-dwarf Flares with ASAS-SN

We analyzed the light curves of 1376 early-to-late, nearby M dwarfs to search for white-light flares using photometry from the All-Sky Automated Survey for Supernovae. We identified 480 M dwarfs with at least one potential flare employing a simple statistical algorithm that searches for sudden increases in V-band flux. After more detailed evaluation, we identified 62 individual flares on 62 stars. The event amplitudes range from mag. Using classical flare models, we place lower limits on the flare energies and obtain V-band energies spanning erg. The fraction of flaring stars increases with spectral type, and most flaring stars show moderate to strong Hα emission. Additionally, we find that 14 of the 62 flaring stars are rotational variables, and they have shorter rotation periods and stronger Hα emission than nonflaring rotational variable M dwarfs.
A Regularized Factor-augmented Vector Autoregressive Model

We propose a regularized factor-augmented vector autoregressive (FAVAR) model that allows for sparsity in the factor loadings. In this framework, factors may only load on a subset of variables which simplifies the factor identification and their economic interpretation. We identify the factors in a data-driven manner without imposing specific relations between the unobserved factors and the underlying time series. Using our approach, the effects of structural shocks can be investigated on economically meaningful factors and on all observed time series included in the FAVAR model. We prove consistency for the estimators of the factor loadings, the covariance matrix of the idiosyncratic component, the factors, as well as the autoregressive parameters in the dynamic model. In an empirical application, we investigate the effects of a monetary policy shock on a broad range of economically relevant variables. We identify this shock using a joint identification of the factor model and the structural innovations in the VAR model. We find impulse response functions which are in line with economic rationale, both on the factor aggregates and observed time series level.
Algorithmic Price Discrimination

We consider a generalization of the third degree price discrimination problem studied in Bergemann et al. (2015), where an intermediary between the buyer and the seller can design market segments to maximize any linear combination of consumer surplus and seller revenue. Unlike in Bergemann et al. (2015), we assume that the intermediary only has partial information about the buyer's value. We consider three different models of information, with increasing order of difficulty. In the first model, we assume that the intermediary's information allows him to construct a probability distribution of the buyer's value. Next we consider the sample complexity model, where we assume that the intermediary only sees samples from this distribution. Finally, we consider a bandit online learning model, where the intermediary can only observe past purchasing decisions of the buyer, rather than her exact value. For each of these models, we present algorithms to compute optimal or near optimal market segmentation.
0-Step Capturability, Motion Decomposition and Global Feedback Control of the 3D Variable Height-Inverted Pendulum

One common method for stabilizing robots after a push is the Instantaneous Capture Point, however, this has the fundamental limitation of assuming constant height. Although there are several works for balancing bipedal robots including height variations in 2D, the amount of literature on 3D models is limited. There are optimization methods using variable Center of Pressure (CoP) and reaction force to the ground, although they do not provide the physical region where a robot can step and require a precomputation for the analysis. This work provides the necessary and sufficient conditions to maintain balance of the 3D Variable Height Inverted Pendulum (VHIP) with both, fixed and variable CoP. We also prove that the 3D VHIP with Fixed CoP is the same as its 2D version, and we generalize controllers working on the 2D VHIP to the 3D VHIP. We also show the generalization of the Divergent Component of Motion to the 3D VHIP and we provide an alternative motion decomposition for the analysis of height and CoP strategies independently. This allow us to generalize previous global feedback controllers done in the 2D VHIP to the 3D VHIP with a Variable CoP.
A recipe for creating ideal hybrid memristive-CMOS neuromorphic computing systems

The development of memristive device technologies has reached a level of maturity to enable the design of complex and large-scale hybrid memristive-CMOS neural processing systems. These systems offer promising solutions for implementing novel in-memory computing architectures for machine learning and data analysis problems. We argue that they are also ideal building blocks for the integration in neuromorphic electronic circuits suitable for ultra-low power brain-inspired sensory processing systems, therefore leading to the innovative solutions for always-on edge-computing and Internet-of-Things (IoT) applications. Here we present a recipe for creating such systems based on design strategies and computing principles inspired by those used in mammalian brains. We enumerate the specifications and properties of memristive devices required to support always-on learning in neuromorphic computing systems and to minimize their power consumption. Finally, we discuss in what cases such neuromorphic systems can complement conventional processing ones and highlight the importance of exploiting the physics of both the memristive devices and of the CMOS circuits interfaced to them.
The Use of Machine Learning and Big Five Personality Taxonomy to Predict Construction Workers' Safety Behaviour

Research has found that many occupational accidents are foreseeable, being the result of people's unsafe behaviour from a retrospective point of view. The prediction of workers' safety behaviour will enable the prior insights into each worker's behavioural tendency and will be useful in the design of management practices prior to the occurrence of accidents and contribute to the reduction of injury rates. In recent years, researchers have found that people do have stable predispositions to engage in certain safety behavioural patterns which vary among individuals as a function of personality features. In this study, an innovative forecasting model, which employs machine learning algorithms, is developed to estimate construction workers' behavioural tendency based on the Big Five personality taxonomy. The data-driven nature of machine learning technique enabled a reliable estimate of the personality-safety behaviour relationship, which allowed this study to provide novel insight that nonlinearity may exist in the relationship between construction workers' personality traits and safety behaviour. The developed model is found to be sufficient to have satisfactory accuracy in explaining and predicting workers' safety behaviour. This finding provides the empirical evidence to support the usefulness of personality traits as effective predictors of people's safety behaviour at work. In addition, this study could have practical implications. The machine learning model developed can help identify vulnerable workers who are more prone to undertake unsafe behaviours, which is proven to have good prediction accuracy and is thereby potentially useful for decision making and safety management on construction sites.
Inferring Input Grammars from Dynamic Control Flow

A program is characterized by its input model, and a formal input model can be of use in diverse areas including vulnerability analysis, reverse engineering, fuzzing and software testing, clone detection and refactoring. Unfortunately, input models for typical programs are often unavailable or out of date. While there exist algorithms that can mine the syntactical structure of program inputs, they either produce unwieldy and incomprehensible grammars, or require heuristics that target specific parsing patterns. In this paper, we present a general algorithm that takes a program and a small set of sample inputs and automatically infers a readable context-free grammar capturing the input language of the program. We infer the syntactic input structure only by observing access of input characters at different locations of the input parser. This works on all program stack based recursive descent input parsers, including PEG and parser combinators, and can do entirely without program specific heuristics. Our Mimid prototype produced accurate and readable grammars for a variety of evaluation subjects, including expr, URLparse, and microJSON.
SOFIA/HAWC+ View of an Extremely Luminous Infrared Galaxy: WISE 1013+6112

We present far-infrared (FIR) properties of an extremely luminous infrared galaxy (ELIRG) at zspec = 3.703, WISE J101326.25+611220.1 (WISE 1013+6112). This ELIRG is selected as an IR-bright dust-obscured galaxy based on the photometry from the Sloan Digital Sky Survey and the Wide-field Infrared Survey Explorer (WISE). In order to derive its accurate IR luminosity, we perform follow-up observations at 89 and 154 μm using the High-resolution Airborne Wideband Camera-plus (HAWC+) on board the 2.7 m Stratospheric Observatory For Infrared Astronomy (SOFIA) telescope. We conduct spectral energy distribution (SED) fitting with CIGALE using 15 photometric data (0.4–1300 μm). We successfully pin down FIR SED of WISE 1013+6112 and its IR luminosity is estimated to be LIR = (1.62 ± 0.08) × 1014L☉, making it one of the most luminous IR galaxies in the universe. We determine the dust temperature of WISE 1013+6112 is Tdust = 89 ± 3 K, which is significantly higher than that of other populations such as submillimeter galaxies and FIR-selected galaxies at similar IR luminosities. The resultant dust mass is Mdust = (2.2 ± 0.1) × 108 M☉. This indicates that WISE 1013+6112 has a significant active galactic nucleus and star-forming activity behind a large amount of dust.
Large-scale Kernel Methods and Applications to Lifelong Robot Learning

As the size and richness of available datasets grow larger, the opportunities for solving increasingly challenging problems with algorithms learning directly from data grow at the same pace. Consequently, the capability of learning algorithms to work with large amounts of data has become a crucial scientific and technological challenge for their practical applicability. Hence, it is no surprise that large-scale learning is currently drawing plenty of research effort in the machine learning research community. In this thesis, we focus on kernel methods, a theoretically sound and effective class of learning algorithms yielding nonparametric estimators. Kernel methods, in their classical formulations, are accurate and efficient on datasets of limited size, but do not scale up in a cost-effective manner. Recent research has shown that approximate learning algorithms, for instance random subsampling methods like Nystr\"om and random features, with time-memory-accuracy trade-off mechanisms are more scalable alternatives. In this thesis, we provide analyses of the generalization properties and computational requirements of several types of such approximation schemes. In particular, we expose the tight relationship between statistics and computations, with the goal of tailoring the accuracy of the learning process to the available computational resources. Our results are supported by experimental evidence on large-scale datasets and numerical simulations. We also study how large-scale learning can be applied to enable accurate, efficient, and reactive lifelong learning for robotics. In particular, we propose algorithms allowing robots to learn continuously from experience and adapt to changes in their operational environment. The proposed methods are validated on the iCub humanoid robot in addition to other benchmarks.
Hot Super-Earths with Hydrogen Atmospheres: A Model Explaining Their Paradoxical Existence

In this paper, we propose a new mechanism that could explain the survival of hydrogen atmospheres on some hot super-Earths. We argue that on close-orbiting tidally locked super-Earths, the tidal forces, together with the orbital and rotational centrifugal forces, can partially confine the atmosphere on the nightside. Assuming a super-terran body with an atmosphere dominated by volcanic species and a large hydrogen component, the heavier molecules can be shown to be confined within latitudes of ≲80° while the volatile hydrogen is not. Because of this disparity, the hydrogen has to slowly diffuse out into the dayside where X-ray and ultraviolet irradiation destroys it. For this mechanism to take effect, it is necessary for the exoplanet to become tidally locked before losing the totality of its hydrogen envelope. Consequently, for super-Earths with this proposed configuration, it is possible to solve the tidal-locking and mass-loss timescales in order to constrain their formation “birth” masses. Our model predicts that 55 Cancri e formed with a day length between approximately 17−18.5 hr and an initial mass less than ∼12M⊕, hence allowing it to become tidally locked before the complete destruction of its atmosphere. For comparison, CoRoT-7b, an exoplanet with very similar properties to 55 Cancri e but lacking an atmosphere, formed with a day length significantly different from ∼20.5 hr while also having an initial mass smaller than ∼9M⊕.
GPRInvNet: Deep Learning-Based Ground-Penetrating Radar Data Inversion for Tunnel Linings

A DNN architecture referred to as GPRInvNet was proposed to tackle the challenges of mapping the ground-penetrating radar (GPR) B-Scan data to complex permittivity maps of subsurface structures. The GPRInvNet consisted of a trace-to-trace encoder and a decoder. It was specially designed to take into account the characteristics of GPR inversion when faced with complex GPR B-Scan data, as well as addressing the spatial alignment issues between time-series B-Scan data and spatial permittivity maps. It displayed the ability to fuse features from several adjacent traces on the B-Scan data to enhance each trace, and then further condense the features of each trace separately. As a result, the sensitive zones on the permittivity maps spatially aligned to the enhanced trace could be reconstructed accurately. The GPRInvNet has been utilized to reconstruct the permittivity map of tunnel linings. A diverse range of dielectric models of tunnel linings containing complex defects has been reconstructed using GPRInvNet. The results have demonstrated that the GPRInvNet is capable of effectively reconstructing complex tunnel lining defects with clear boundaries. Comparative results with existing baseline methods also demonstrated the superiority of the GPRInvNet. For the purpose of generalizing the GPRInvNet to real GPR data, some background noise patches recorded from practical model testing were integrated into the synthetic GPR data to retrain the GPRInvNet. The model testing has been conducted for validation, and experimental results revealed that the GPRInvNet had also achieved satisfactory results with regard to the real data.
Coloring graph neural networks for node disambiguation

In this paper, we show that a simple coloring scheme can improve, both theoretically and empirically, the expressive power of Message Passing Neural Networks (MPNNs). More specifically, we introduce a graph neural network called Colored Local Iterative Procedure (CLIP) that uses colors to disambiguate identical node attributes, and show that this representation is a universal approximator of continuous functions on graphs with node attributes. Our method relies on separability, a key topological characteristic that allows to extend well-chosen neural networks into universal representations. Finally, we show experimentally that CLIP is capable of capturing structural characteristics that traditional MPNNs fail to distinguish, while being state-of-the-art on benchmark graph classification datasets.