Miracle-dz/UniFAR-FTUs huggingface dataset:
DEFENSIVE RESPONSE OF COMMERCIAL VARIETIES OF Prunus persica L. TO THE ATTACK OF Myzus persicae (Sulzer) APHID

Peaches and nectarines are among the most important fruit worldwide. Unfortunately, they are heavily attacked by the aphid M. persicae causing direct and indirect damages. In Chile, peaches and nectarines are also important fruit products. However, there is little information regarding the variation in susceptibility/resistance among cultivars frequently planted. In this study the interaction between this aphid and commercial genotypes of P. persica was addressed. First, commercial cultivars of peaches and nectarines were screened regarding differential resistance in the field (aphid occurrence and performance), as well as in laboratory experiments (no-choice test and feeding behavior). It was found a wide range of different responses in resistance to M. persicae, either in the field and laboratory experiments, with some cultivar exhibiting antibiosis, antixenosis or mixed resistances. In a further study, the cultivars with most contrasting resistant patterns were selected with the aim to assess the effect of irrigation on resistance. Two different levels of irrigation were used, well-watered (100% field capacity) and water-deficient (50% field capacity) plants, and the plant defensive response was evaluated. This experiment showed that resistance was modulated by irrigation as under water-deficiency curling produced by aphids was similar between the resistant and susceptible cultivars. It was also found induced resistance and induced susceptibility regardless the cultivar genotypes (resistant or susceptible). In addition, a tolerant response, measured as plant growth after aphid damage, was observed in both cultivars, capacity that was also dependent of irrigation levels with higher tolerance displayed by well-watered plants. Finally, in order to assess how aphids respond to plants with differences in resistant and irrigation, the proteomic profile of aphids that fed on peach cultivars with different resistance and different irrigation treatment was also performed. The proteomic analysis showed that aphids suffered of higher changes in the regulation of proteins after feeding on the susceptible cultivar than on the resistant one. The proteins upregulated on aphid fed in the susceptible cultivar were mostly involved in the energy metabolism, whereas on the resistant cultivar the most up-regulated proteins were those associated to the cytoskeleton. All the above helped to a better understanding of both, the defensive response of Prunus persica and to the proteomic response of the aphid to plants with different resistance and level of irrigation. Verdugo, J.A. (2011). Respuesta defensiva de variedades comerciales de Prunus persica L. al ataque del áfido Myzus persicae (Sulzer). (Tesis doctoral). Facultad de Ciencias Agrarias – Universidad de Talca – Chile y Gembloux, University of Liege – Gembloux Agro-Bio Tech, Bélgica.
DeGaS-MC: Dense Gas Survey in the Magellanic Clouds

Context. Understanding the star-forming processes is key to understanding the evolution of galaxies. Investigating star formation requires precise knowledge of the properties of the dense molecular gas complexes where stars form and a quantification of how they are affected by the physical conditions to which they are exposed. The proximity, low metallicity, and wide range of star formation activity of the Large and Small Magellanic Clouds (LMC and SMC) make them prime laboratories to study how local physical conditions impact the dense gas reservoirs and their star formation efficiency. Aims. The aim of the Dense Gas Survey for the Magellanic Clouds (DeGaS-MC) project is to expand our knowledge of the relation between dense gas properties and star formation activity by targeting the LMC and SMC observed in the HCO+(2−1) and HCN(2−1) transitions. Methods. We carried out a pointing survey targeting two lines toward ∼30 LMC and SMC molecular clouds using the SEPIA180 instrument installed on the Atacama Pathfinder EXperiment (APEX) telescope. We performed a follow-up mapping campaign of the emission in the same transition in 13 star-forming regions. This first paper provides line characteristic catalogs and integrated line-intensity maps of the sources. Results. HCO+(2−1) is detected in 20 and HCN(2−1) in 8 of the 29 pointings observed. The dense gas velocity pattern follows the line-of-sight velocity field derived from the stellar population. The three SMC sources targeted during the mapping campaign were unfortunately not detected in our mapping campaign but both lines are detected toward the LMC 30Dor, N44, N105, N113, N159W, N159E, and N214 regions. The HCN emission is less extended than the HCO+ emission and is restricted to the densest regions. The HCO+(2−1)/HCN(2−1) brightness temperature ratios range from 1 to 7, which is consistent with the large ratios commonly observed in low-metallicity environments. A larger number of young stellar objects are found at high HCO+ intensities and lower HCO+/HCN flux ratios, and thus toward denser lines of sight. The dense gas luminosities correlate with the star formation rate traced by the total infrared luminosity over the two orders of magnitude covered by our observations, although substantial region-to-region variations are observed.
An investigation into the genes mediating myoblast migration in the nematode : Caenorhabditis elegans

During C. elegans embryogenesis, myoblasts initially form two rows along the left and right lateral midlines and at ~290 min of development migrate dorsally and ventrally to form the four muscle quadrants present upon hatching (Sulston et al, 1983). As the myoblasts migrate they are still dividing, as are many other cells in their immediate environment. This means the cell-cell contact of cells during migration is dynamic and can vary from animal to animal (Schnabel et al, 1997). This situation creates an environment where the extracellular matrix (ECM) and cell surface contacts are in constant flux, which begs the questions as to how these cells navigate unerringly to their final destination. In an attempt to identify genes mediating these migrations, I performed an RNAi based screen targeting 776 genes predicted to be members of the extracellular matrix (ECM), or one of its receptors. Using both feeding and injection based RNAi, I was able to identify three genes of interest. Knockdowns of F56B3.2 resulted in paralyzed animals with detached muscle, making it a good candidate for a new component of the muscle attachment complex. F33G12.4 knockdowns resulted in an embryonic arrest phenotype with an abnormal muscle lineage, possibly stemming from polarity defects. The only knockdown that resulted in muscle migration defects was that for lam-2, which encodes for the laminin gamma subunit. Analysis of the lam-2 knockdown, as well as knockdowns for the other laminin subunits, revealed dorsal/ventral migration defects as well as a posterior displacement of the anterior-most ventral muscle
Recognition Oriented Iris Image Quality Assessment in the Feature Space

A large portion of iris images captured in real world scenarios are poor quality due to the uncontrolled environment and the non-cooperative subject. To ensure that the recognition algorithm is not affected by low-quality images, traditional hand-crafted factors based methods discard most images, which will cause system timeout and disrupt user experience. In this paper, we propose a recognition-oriented quality metric and assessment method for iris image to deal with the problem. The method regards the iris image em-beddings Distance in Feature Space (DFS) as the quality metric and the prediction is based on deep neural networks with the attention mechanism. The quality metric proposed in this paper can significantly improve the performance of the recognition algorithm while reducing the number of images discarded for recognition, which is advantageous over hand-crafted factors based iris quality assessment methods. The relationship between Image Rejection Rate (IRR) and Equal Error Rate (EER) is proposed to evaluate the performance of the quality assessment algorithm under the same image quality distribution and the same recognition algorithm. Compared with hand-crafted factors based methods, the proposed method is a trial to bridge the gap between the image quality assessment and biometric recognition.
Tensor Relational Algebra for Machine Learning System Design

Machine learning (ML) systems have to support various tensor operations. However, such ML systems were largely developed without asking: what are the foundational abstractions necessary for building machine learning systems? We believe that proper computational and implementation abstractions will allow for the construction of self-configuring, declarative ML systems, especially when the goal is to execute tensor operations in a distributed environment, or partitioned across multiple AI accelerators (ASICs). To this end, we first introduce a tensor relational algebra (TRA), which is expressive to encode any tensor operation represented by the Einstein notation; we then transform it to an implementation algebra (IA) that enables effective logical and physical optimizations for paralleled and distributed environment.
A Digital Twin for Reconfigurable Intelligent Surface Assisted Wireless Communication

Reconfigurable Intelligent Surface (RIS) has emerged as one of the key technologies for 6G in recent years, which comprise a large number of low-cost passive elements that can smartly interact with the impinging electromagnetic waves for performance enhancement. However, optimally configuring massive number of RIS elements remains a challenge. In this paper, we present a novel digital-twin framework for RIS-assisted wireless networks which we name it Environment-Twin (Env-Twin). The goal of the Env-Twin framework is to enable automation of optimal control at various granularities. In this paper, we present one example of the Env-Twin models to learn the mapping function between the RIS configuration with measured attributes for the receiver location, and the corresponding achievable rate in an RIS-assisted wireless network without involving explicit channel estimation or beam training overhead. Once learned, our Env-Twin model can be used to predict optimal RIS configuration for any new receiver locations in the same wireless network. We leveraged deep learning (DL) techniques to build our model and studied its performance and robustness. Simulation results demonstrate that the proposed Env-Twin model can recommend near-optimal RIS configurations for test receiver locations which achieved close to an upper bound performance that assumes perfect channel knowledge. Our Env-Twin model was trained using less than 2% of the total receiver locations. This promising result represents great potential of the proposed Env-Twin framework for developing a practical RIS solution where the panel can automatically configure itself without requesting channel state information (CSI) from the wireless network infrastructure.
The Eulerian variational formulation of the gyrokinetic system in general spatial coordinates

The Eulerian variational formulation of the gyrokinetic system with electrostatic turbulence is presented in general spatial coordinates by extending our previous work [H. Sugama, {\it et al}. , Phys.\ Plasmas {\bf 25}, 102506 (2018)]. The invariance of the Lagrangian of the system under an arbitrary spatial coordinate transformation is used to derive the local momentum balance equation satisfied by the gyrocenter distribution functions and the turbulent potential which are given as solutions of the governing equations. This derivation is in contrast with the conventional method using the spatial translation in which the asymmetric canonical pressure tensor generally enters the momentum balance equation. In the present study, the variation of the Lagrangian density with respect to the metric tensor is taken to directly obtain the symmetric pressure tensor which includes the effect of turbulence on the momentum transport. In addition, it is shown in this work how the momentum balance is modified when the collision and/or external source terms are added to the gyrokinetic equation. The results obtained here are considered useful for global gyrokinetic simulations investigating both neoclassical and turbulent transport processes even in general non-axisymmetric toroidal systems.
Excess deviations for points disconnected by random interlacements

We consider random interlacements on $ \mathbb{Z}^d$, $d \ge 3$, when their vacant set is in a strongly percolative regime. Given a large box centered at the origin, we establish an asymptotic upper bound on the exponential rate of decay of the probability that the box contains an excessive fraction $\nu$ of points that are disconnected by random interlacements from the boundary of a concentric box of double size. As an application we show that when $\nu$ is not too large, this asymptotic upper bound matches the asymptotic lower bound derived in a previous work of the author, and the exponential rate of decay is governed by a certain variational problem in the continuum which involves the percolation function of the vacant set of random interlacements.
Growth signals determine the topology of evolving networks

Network science provides an indispensable theoretical framework for studying the structure and function of real complex systems. Different network models are often used for finding the rules that govern their evolution, whereby the correct choice of model details is crucial for obtaining relevant insights. Here, we study how the structure of networks generated with the aging nodes model depends on the properties of the growth signal. We use different fluctuating signals and compare structural dissimilarities of the networks with those obtained with a constant growth signal. We show that networks with power-law degree distributions, which are obtained with time-varying growth signals, are correlated and clustered, while networks obtained with a constant growth signal are not. Indeed, the properties of the growth signal significantly determine the topology of the obtained networks and thus ought to be considered prominently in models of complex systems.
Entanglement, non-hermiticity, and duality

Usually duality process keeps energy spectrum invariant. In this paper, we provide a duality, which keeps entanglement spectrum invariant, in order to diagnose quantum entanglement of non-Hermitian non-interacting fermionic systems. We limit our attention to non-Hermitian systems with a complete set of biorthonormal eigenvectors and an entirely real energy spectrum. The original system has a reduced density matrix \rho_\mathrm{o}ρo and the real space is partitioned via a projecting operator \mathcal{R}_{\mathrm o}ℛo. After dualization, we obtain a new reduced density matrix \rho_{\mathrm{d}}ρd and a new real space projector \mathcal{R}_{\mathrm d}ℛd. Remarkably, entanglement spectrum and entanglement entropy keep invariant. Inspired by the duality, we defined two types of non-Hermitian models, upon \mathcal R_{\mathrm{o}}ℛo is given. In type-I exemplified by the "non-reciprocal model'', there exists at least one duality such that \rho_{\mathrm{d}}ρd is Hermitian. In other words, entanglement information of type-I non-Hermitian models with a given \mathcal{R}_{\mathrm{o}}ℛo is entirely controlled by Hermitian models with \mathcal{R}_{\mathrm{d}}ℛd. As a result, we are allowed to apply known results of Hermitian systems to efficiently obtain entanglement properties of type-I models. On the other hand, the duals of type-II models, exemplified by "non-Hermitian Su-Schrieffer-Heeger model’’, are always non-Hermitian. For the practical purpose, the duality provides a potentially computation route to entanglement of non-Hermitian systems. Via connecting different models, the duality also sheds lights on either trivial or nontrivial role of non-Hermiticity played in quantum entanglement, paving the way to potentially systematic classification and characterization of non-Hermitian systems from the entanglement perspective.
Graph-based Model of Smart Grid Architectures

The rising use of information and communication technology in smart grids likewise increases the risk of failures that endanger the security of power supply, e.g., due to errors in the communication configuration, faulty control algorithms, or cyber-attacks. Co-simulations can be used to investigate such effects, but require precise modeling of the energy, communication, and information domain within an integrated smart grid infrastructure model. Given the complexity and lack of detailed publicly available communication network models for smart grid scenarios, there is a need for an automated and systematic approach to creating such coupled models. In this paper, we present an approach to automatically generate smart grid infrastructure models based on an arbitrary electrical distribution grid model using a generic architectural template. We demonstrate the applicability and unique features of our approach alongside examples concerning network planning, co-simulation setup, and specification of domain-specific intrusion detection systems.
Direct CMOS Implementation of Neuromorphic Temporal Neural Networks for Sensory Processing

Temporal Neural Networks (TNNs) use time as a resource to represent and process information, mimicking the behavior of the mammalian neocortex. This work focuses on implementing TNNs using off-the-shelf digital CMOS technology. A microarchitecture framework is introduced with a hierarchy of building blocks including: multi-neuron columns, multi-column layers, and multi-layer TNNs. We present the direct CMOS gate-level implementation of the multi-neuron column model as the key building block for TNNs. Post-synthesis results are obtained using Synopsys tools and the 45 nm CMOS standard cell library. The TNN microarchitecture framework is embodied in a set of characteristic equations for assessing the total gate count, die area, compute time, and power consumption for any TNN design. We develop a multi-layer TNN prototype of 32M gates. In 7 nm CMOS process, it consumes only 1.54 mm^2 die area and 7.26 mW power and can process 28x28 images at 107M FPS (9.34 ns per image). We evaluate the prototype's performance and complexity relative to a recent state-of-the-art TNN model.
Off Policy Risk Sensitive Reinforcement Learning Based Optimal Tracking Control with Prescribe Performances

An off policy reinforcement learning based control strategy is developed for the optimal tracking control problem to achieve the prescribed performance of full states during the learning process. The optimal tracking control problem is converted as an optimal regulation problem based on an auxiliary system. The requirements of prescribed performances are transformed into constraint satisfaction problems that are dealt with by risk sensitive state penalty terms under an optimization framework. To get approximated solutions of the Hamilton Jacobi Bellman equation, an off policy adaptive critic learning architecture is developed by using current data and experience data together. By using experience data, the proposed weight estimation update law of the critic learning agent guarantees weight convergence to the actual value. This technique enjoys practicability comparing with common methods that need to incorporate external signals to satisfy the persistence of excitation condition for weight convergence. The proofs of stability and weight convergence of the closed loop system are provided. Simulation results reveal the validity of the proposed off policy risk sensitive reinforcement learning based control strategy.
Habitable Zones in Binary Star Systems: A Zoology

Several concepts have been brought forward to determine where terrestrial planets are likely to remain habitable in multi-stellar environments. Isophote-based habitable zones, for instance, rely on insolation geometry to predict habitability, whereas radiative habitable zones take the orbital motion of a potentially habitable planet into account. Dynamically informed habitable zones include gravitational perturbations on planetary orbits, and full scale, self consistent simulations promise detailed insights into the evolution of select terrestrial worlds. All of the above approaches agree that stellar multiplicity does not preclude habitability. Predictions on where to look for habitable worlds in such environments can differ between concepts. The aim of this article is to provide an overview of current approaches and present simple analytic estimates for the various types of habitable zones in binary star systems.
Top-k Socio-Spatial Co-Engaged Location Selection for Social Users

With the advent of location-based social networks, users can tag their daily activities in different locations through check-ins. These check-in locations signify user preferences for various socio-spatial activities and can be used to improve the quality of services in some applications such as recommendation systems, advertising, and group formation. To support such applications, in this paper, we formulate a new problem of <italic>identifying top-k</italic> <italic>S</italic><italic>ocio-</italic><italic>S</italic><italic>patial co-engaged</italic> <italic>L</italic><italic>ocation</italic> <italic>S<italic/>election</italic> (<italic>SSLS</italic>) for users in a social graph, that selects the best set of <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="li-ieq2-3151095.gif"/></alternatives></inline-formula> locations from a large number of location candidates relating to the user and her friends. The selected locations should be (i) <italic>spatially and socially relevant</italic> to the user and her friends, and (ii) <italic>diversified both spatially and socially</italic> to maximize the coverage of friends in the socio-spatial space. This problem has been proved as NP-hard. To address such a challenging problem, we first develop an <monospace>Exact</monospace> solution by designing some pruning strategies based on derived bounds on diversity. To make the solution scalable for large datasets, we also develop an approximate solution by deriving relaxed bounds and advanced termination rules to filter out insignificant intermediate results. To further accelerate the efficiency, we present one fast exact approach and a meta-heuristic approximate approach by avoiding the repeated computation of diversity at the running time. Finally, we have performed extensive experiments to evaluate the performance of our proposed algorithms against three adapted existing methods using four large real-world datasets.
Kinetic Simulations of Cosmic-Ray-modified Shocks. II. Particle Spectra

Diffusive shock acceleration is a prominent mechanism for producing energetic particles in space and in astrophysical systems. Such energetic particles have long been predicted to affect the hydrodynamic structure of the shock, in turn leading to CR spectra flatter than the test-particle prediction. However, in this work along with a companion paper, we use self-consistent hybrid (kinetic ion–fluid electron) simulations to show for the first time how CR-modified shocks actually produce steeper spectra. The steepening is driven by the enhanced advection of CRs embedded in magnetic turbulence downstream of the shock, in what we call the “postcursor. ” These results are consistent with multiwavelength observations of supernovae and supernova remnants and have significant phenomenological implications for space/astrophysical shocks in general.