Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of novel transcriptional regulators of PKA subunits in Saccharomyces cerevisiae by Quantitative Promoter–Reporter Screening

The cAMP dependent protein kinase (PKA) signaling is a broad specificity pathway that plays important roles in the transduction of environmental signals triggering precise physiological responses. cAMP-signal transduction specificity is achieved and controlled at several levels. The Saccharomyces cereviciae PKA holoenzyme consists of two catalytic subunits encoded by TPK1, TPK2 and TPK3 genes, and two regulatory subunits encoded by BCY1 gene. In this work we studied the activity of these gene promoters using a reporter-synthetic genetic array screen, with the goal of identifying novel regulators of PKA subunits expression. Gene ontology (GO) analysis of the regulators identified showed that these regulators were enriched for annotations associated with roles in several GO biological process, as lipid and phosphate metabolism or transcription regulation and regulate all or some of the four promoters. Further characterization of the effect of these pathways on promoter activity and mRNA levels pointed to inositol, inositol polyphosphates, choline and phosphate as novel upstream signals that regulate transcription of PKA subunit genes. In addition, within each category there are genes that regulate only one of the promoters and genes that regulate more than one of them at the same time. These results support the role of transcription regulation of each PKA subunit in cAMP specificity signaling. Interestingly, many of the known targets of PKA phosphorylation are associated with the identified pathways, opening the possibility of a reciprocal regulation in which PKA would be coordinating different metabolic pathways and these processes would in turn, regulate expression of the kinase subunits.
Turbulent radial thermal counterflow in the framework of the HVBK model

We apply the coarse-grained Hall-Vinen-Bekarevich-Khalatnikov (HVBK) equations to model the statistically steady-state, turbulent, cylindrically symmetric radial counterflow generated by a moderately large heat flux from the surface of a cylinder immersed in superfluid 4He. We show that a time-independent solution exists only if a spatial non-uniformity of temperature and the dependence on temperature of the thermodynamic properties are accounted for. We demonstrate the formation of a thermal boundary layer whose thickness grows with temperature of the cylinder's surface, and analyze the properties of the flow in the radial direction, including the local average vortex line density.
Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation.

We propose a simple, but efficient and accurate machine learning (ML) model for developing high-dimensional potential energy surface. This so-called embedded atom neural network (EANN) approach is inspired by the well-known empirical embedded atom method (EAM) model used in condensed phase. It simply replaces the scalar embedded atom density in EAM with a Gaussian-type orbital based density vector, and represents the complex relationship between the embedded density vector and atomic energy by neural networks. We demonstrate that the EANN approach is equally accurate as several established ML models in representing both big molecular and extended periodic systems, yet with much fewer parameters and configurations. It is highly efficient as it implicitly contains the three-body information without an explicit sum of the conventional costly angular descriptors. With high accuracy and efficiency, EANN potentials can vastly accelerate molecular dynamics and spectroscopic simulations in complex systems at ab initio level.
Lens modelling of the strongly lensed Type Ia supernova iPTF16geu

In 2016, the first strongly lensed Type Ia supernova (SN Ia), iPTF16geu, at redshift z = 0.409 with four resolved images arranged symmetrically around the lens galaxy at z = 0.2163, was discovered. Here, refined observations of iPTF16geu, including the time delay between images, are used to decrease uncertainties in the lens model, including the the slope of the projected surface density of the lens galaxy, Σ ∝ r1 − η, and to constrain the universal expansion rate H0. Imaging with Hubble Space Telescope provides an upper limit on the slope η, in slight tension with the steeper density profiles indicated by imaging with Keck after iPTF16geu had faded, potentially due to dust extinction not corrected for in host galaxy imaging. Since smaller η implies larger magnifications, we take advantage of the standard candle nature of SNe Ia constraining the image magnifications, to obtain an independent constraint of the slope. We find that a smooth lens density fails to explain the iPTF16geu fluxes, regardless of the slope, and additional substructure lensing is needed. The total probability for the smooth halo model combined with star microlensing to explain the iPTF16geu image fluxes is maximized at 12 per cent for η ∼ 1.8, in excellent agreement with Keck high-spatial-resolution data, and flatter than an isothermal halo. It also agrees perfectly with independent constraints on the slope from lens velocity dispersion measurements. Combining with the observed time delays between the images, we infer a lower bound on the Hubble constant, $H_0 \gtrsim 40\, {\rm km \ s^{-1} Mpc^{-1}}$, at 68.3 per cent confidence level.
Towards the super fluid cloud

Traditionally, the number of VMs running on a server and how quickly these can be migrated has been less than optimal mostly because of the memory and CPU requirements imposed on the system by the full-fledged OSes that the VMs run. More recently, work towards VMs based on minimalistic or specialized OSes has started pushing the envelope of how reactive or fluid the cloud can be. In this demo we will demonstrate how to concurrently execute thousands of Xen-based VMs on a single inexpensive server. We will also show instantiation and migraion of such VMs in tens of milliseconds, and transparent, wide area migration of virtualized middleboxes by combining such VMs with the multi-path TCP (MPTCP) protocol.
New method for characterization of magnetic nanoparticles by scanning magnetic microscopy

In this paper, we present a new method for the magnetic characterization of bulk materials, microstructures, and nanostructures. We investigated the magnetic and morphological properties of two colloidal dispersions of iron oxide (Fe3O4) magnetic nanoparticles (MNPs), synthesized by chemical precipitation (co-precipitation) and pulsed laser ablation (PLA) in liquid, by scanning magnetic microscopy (SMM) applied to a small sample with mass on the order of tens of {\mu}g. We evaluated the performance of this technique by comparing magnetization curves and measurements obtained with commercial magnetometers, considered standard. The errors obtained for the saturation and remanent magnetization were approximately 0.18 Am2/kg and 0.6 Am2/kg, respectively. The average size distribution of the NPs estimated from the magnetization curve measurements is consistent with the results obtained by traditional transmission electron microscopy (TEM). The technique can be extended to measure and analyze magnetization curves (hysteresis loops), thus enabling an even more accurate estimation of overall NP sizes.
3D Modelling from Real Data

The genesis of a 3D model has basically two definitely different paths. Firstly we can consider the CAD generated models, where the shape is defined according to a user drawing action, operating with different mathematical “bricks” like B-Splines, NURBS or subdivision surfaces (mathematical CAD modelling), or directly drawing small polygonal planar facets in space, approximating with them complex free form shapes (polygonal CAD modelling). This approach can be used for both ideal elements (a project, a fantasy shape in the mind of a designer, a 3D cartoon, etc.) or for real objects. In the latter case the object has to be first surveyed in order to generate a drawing coherent with the real stuff. If the surveying process is not only a rough acquisition of simple distances with a substantial amount of manual drawing, a scene can be modelled in 3D by capturing with a digital instrument many points of its geometrical features and connecting them by polygons to produce a 3D result similar to a polygonal CAD model, with the difference that the shape generated is in this case an accurate 3D acquisition of a real object (reality-based polygonal modelling). Considering only device operating on the ground, 3D capturing techniques for the generation of reality-based 3D models may span from passive sensors and image data (Remondino and El-Hakim, 2006), optical active sensors and range data (Blais, 2004; Shan & Toth, 2008; Vosselman and Maas, 2010), classical surveying (e.g. total stations or Global Navigation Satellite System GNSS), 2D maps (Yin et al., 2009) or an integration of the aforementioned methods (Stumpfel et al., 2003; Guidi et al., 2003; Beraldin, 2004; Stamos et al., 2008; Guidi et al., 2009a; Remondino et al., 2009; Callieri et al., 2011). The choice depends on the required resolution and accuracy, object dimensions, location constraints, instrument’s portability and usability, surface characteristics, working team experience, project’s budget, final goal, etc. Although aware of the potentialities of the image-based approach and its recent developments in automated and dense image matching for non-expert the easy usability and reliability of optical active sensors in acquiring 3D data is generally a good motivation to decline image-based approaches. Moreover the great advantage of active sensors is the fact that they deliver immediately dense and detailed 3D point clouds, whose coordinate are metrically defined. On the other hand image data require some processing and a mathematical formulation to transform the two-dimensional image measurements into metric three-dimensional coordinates. Image-based modelling techniques (mainly
Multilevel models for continuous outcomes

Multilevel models (mixed-effect models or hierarchical linear models) are now a standard approach to analysing clustered and longitudinal data in the social, behavioural and medical sciences. This review article focuses on multilevel linear regression models for continuous responses (outcomes or dependent variables). These models can be viewed as an extension of conventional linear regression models to account for and learn from the clustering in the data. Common clustered applications include studies of school effects on student achievement, hospital effects on patient health, and neighbourhood effects on respondent attitudes. In all these examples, multilevel models allow one to study how the regression relationships vary across clusters, to identify those cluster characteristics which predict such variation, to disentangle social processes operating at different levels of analysis, and to make cluster-specific predictions. Common longitudinal applications include studies of growth curves of individual height and weight and developmental trajectories of individual behaviours. In these examples, multilevel models allow one to describe and explain variation in growth rates and to simultaneously explore predictors of both of intra- and inter-individual variation. This article introduces and illustrates this powerful class of model. We start by focusing on the most commonly applied two-level random-intercept and -slope models. We illustrate through two detailed examples how these models can be applied to both clustered and longitudinal data and in both observational and experimental settings. We then review more flexible three-level, cross-classified, multiple membership and multivariate response models. We end by recommending a range of further reading on all these topics.
STEPAR: an automatic code to infer stellar atmospheric parameters

Context. STEPAR is an automatic code written in Python 3.X designed to compute the stellar atmospheric parameters Teff, log g, [Fe/H], and ξ of FGK-type stars by means of the equivalent width (EW) method. This code has already been extensively tested in different spectroscopic studies of FGK-type stars with several spectrographs and against thousands of Gaia-ESO Survey UVES U580 spectra of late-type, low-mass stars as one of its 13 pipelines. Aims. We describe the code that we tested against a library of well characterised Gaia benchmark stars. We also release the code to the community and provide the link for download. Methods. We carried out the required EW determination of Fe I and Fe II spectral lines using the automatic tool TAME. STEPAR implements a grid of MARCS model atmospheres and the MOOG radiative transfer code to compute stellar atmospheric parameters by means of a Downhill Simplex minimisation algorithm. Results. We show the results of the benchmark star test and also discuss the limitations of the EW method, and hence the code. In addition, we find a small internal scatter for the benchmark stars of 9 ± 32 K in Teff, 0.00 ± 0.07 dex in log g, and 0.00 ± 0.03 dex in [Fe/H]. Finally, we advise against using STEPAR on double-lined spectroscopic binaries or spectra with R < 30 000, S/N < 20, or v sin i > 15 km s−1, and on stars later than K4 or earlier than F6.
Mutual Suppression Network for Video Prediction using Disentangled Features

Video prediction has been considered a difficult problem because the video contains not only high-dimensional spatial information but also complex temporal information. Video prediction can be performed by finding features in recent frames, and using them to generate approximations to upcoming frames. We approach this problem by disentangling spatial and temporal features in videos. We introduce a mutual suppression network (MSnet) which are trained in an adversarial manner and then produces spatial features which are free of motion information, and motion features with no spatial information. MSnet then uses motion-guided connection within an encoder-decoder-based architecture to transform spatial features from a previous frame to the time of an upcoming frame. We show how MSnet can be used for video prediction using disentangled representations. We also carry out experiments to assess the effectiveness of our method to disentangle features. MSnet obtains better results than other recent video prediction methods even though it has simpler encoders.
Toda lattice hierarchy and trigonometric Ruijsenaars–Schneider hierarchy

We consider solutions of the 2D Toda lattice hierarchy, which are trigonometric functions of the ‘zeroth’ time t0 = x. It is known that their poles move as particles of the trigonometric Ruijsenaars–Schneider model. We extend this correspondence to the level of hierarchies: the dynamics of poles with respect to the mth hierarchical time tm (respectively, ) of the 2D Toda lattice hierarchy is shown to be governed by the Hamiltonian which is proportional to the mth Hamiltonian (respectively, ) of the Ruijsenaars–Schneider model, where L is the Lax matrix.
Improving Throughput and Fairness in IEEE 802.11e

WLANs have become increasingly popular and widely deployed. The MAC protocol is one of the important technology of the WLAN and affects communication efficiency directly. In this paper, focusing on MAC protocol, we propose a novel protocol that network nodes dynamically optimize their backoff process to achieve high throughput while supporting satisfied QoS. Distributed model MAC protocol has an advantage that no infrastructure such as access point is necessary. On the other hand, total throughput decreases heavily and cannot guarantee QoS under a high traffic load, which needs to be improved. Through theoretical analysis, we find that the average idle interval can represent current network traffic load and can be used together with estimated number of nodes for setting optimal CW. Since necessary indexes can be obtained through direct measurement from channel, our scheme will not increase any added load to networks, which makes our schemes simpler and more effective. Through simulation comparison with conventional method, we show that our scheme can greatly enhance the throughput and the QoS no matter the network is in saturated or nonsaturated case, while maintaining good fairness.
Shape Optimization for Composite Materials and Scaffold Structures

This article combines shape optimization and homogenization techniques by looking for the optimal design of the microstructure in composite materials and of scaffolds. The development of materials with specific properties is of huge practical interest, for example, for medical applications or for the development of light weight structures in aeronautics. In particular, the optimal design of microstructures leads to fundamental questions for porous media: what is the sensitivity of homogenized coefficients with respect to the shape of the microstructure? We compute Hadamard's shape gradient for the problem of realizing a prescribed effective tensor and demonstrate the applicability and feasibility of our approach by numerical experiments.
BeSense: Leveraging WiFi Channel Data and Computational Intelligence for Behavior Analysis

The ever-evolving informatics technology has gradually bounded human and computer in a compact way. Understanding user behavior becomes a key enabler in many fields such as sedentary-related healthcare, human-computer interaction and affective computing. Traditional sensor-based and vision-based user behavior analysis approaches are obtrusive in general, hindering their usage in real-world. Therefore, in this article, we first introduce the WiFi signal as a new source instead of sensor and vision for unobtrusive user behaviors analysis. Then we design BeSense, a contactless behavior analysis system leveraging signal processing and computational intelligence over WiFi channel state information. We prototype BeSense on commodity low-cost WiFi devices and evaluate its performance in real-world environments. Experimental results have verified its effectiveness in recognizing user behaviors.
Gait Generation using Intrinsically Stable MPC in the Presence of Persistent Disturbances

From a control point of view, humanoid gait generation can be seen as a problem of tracking a suitable ZMP trajectory while guaranteeing internal stability. In the presence of disturbances, both these aspects are at risk, and a fall may ultimately occur. In this paper, we extend our previously proposed Intrinsically Stable MPC (IS-MPC) method, which guarantees stable tracking for the unperturbed case, to the case of persistent disturbances. This is achieved by designing a disturbance observer whose estimate is used to set up a modified stability constraint for the QP problem. The method is validated by MATLAB tests as well as dynamic simulations for a NAO humanoid in DART.
DeepSUM: Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images

Recently, convolutional neural networks (CNNs) have been successfully applied to many remote sensing problems. However, deep learning techniques for multi-image super-resolution (SR) from multitemporal unregistered imagery have received little attention so far. This article proposes a novel CNN-based technique that exploits both spatial and temporal correlations to combine multiple images. This novel framework integrates the spatial registration task directly inside the CNN, and allows one to exploit the representation learning capabilities of the network to enhance registration accuracy. The entire SR process relies on a single CNN with three main stages: shared 2-D convolutions to extract high-dimensional features from the input images; a subnetwork proposing registration filters derived from the high-dimensional feature representations; 3-D convolutions for slow fusion of the features from multiple images. The whole network can be trained end-to-end to recover a single high-resolution image from multiple unregistered low-resolution images. The method presented in this article is the winner of the PROBA-V SR challenge issued by the European Space Agency (ESA).
Learning One-hidden-layer neural networks via Provable Gradient Descent with Random Initialization

Although deep learning has shown its powerful performance in many applications, the mathematical principles behind neural networks are still mysterious. In this paper, we consider the problem of learning a one-hidden-layer neural network with quadratic activations. We focus on the under-parameterized regime where the number of hidden units is smaller than the dimension of the inputs. We shall propose to solve the problem via a provable gradient-based method with random initialization. For the non-convex neural networks training problem we reveal that the gradient descent iterates are able to enter a local region that enjoys strong convexity and smoothness within a few iterations, and then provably converges to a globally optimal model at a linear rate with near-optimal sample complexity. We further corroborate our theoretical findings via various experiments.
Distributed Kalman Filtering Under Model Uncertainty

We study the problem of distributed Kalman filtering for sensor networks in the presence of model uncertainty. More precisely, we assume that the actual state-space model belongs to a ball, in the Kullback–Leibler topology, about the nominal state-space model and whose radius reflects the mismatch modeling budget allowed for each time step. We propose a distributed Kalman filter with a diffusion step which is robust with respect to the aforementioned model uncertainty. Moreover, we derive the corresponding least favorable performance. Finally, we check the effectiveness of the proposed algorithm in the presence of uncertainty through a numerical example.
An Evolutionary Algorithm of Linear complexity: Application to Training of Deep Neural Networks

The performance of deep neural networks, such as Deep Belief Networks formed by Restricted Boltzmann Machines (RBMs), strongly depends on their training, which is the process of adjusting their parameters. This process can be posed as an optimization problem over n dimensions. However, typical networks contain tens of thousands of parameters, making this a High-Dimensional Problem (HDP). Although different optimization methods have been employed for this goal, the use of most of the Evolutionary Algorithms (EAs) becomes prohibitive due to their inability to deal with HDPs. For instance, the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) which is regarded as one of the most effective EAs, exhibits the enormous disadvantage of requiring $O(n^2)$ memory and operations, making it unpractical for problems with more than a few hundred variables. In this paper, we introduce a novel EA that requires $O(n)$ operations and memory, but delivers competitive solutions for the training stage of RBMs with over one million variables, when compared against CMA-ES and the Contrastive Divergence algorithm, which is the standard method for training RBMs.