Miracle-dz/UniFAR-FTUs huggingface dataset:
Arbuscular mycorrhizal fungi effectively enhances 2 the growth of Gleditsia sinensis Lam . seedlings under 3 glasshouse conditions 4

The Chinese honey locust tree Gleditsia sinensis Lam. (Fabaceae) is a tree species which is 15 valuable in both an ecological and economic sense, and which has a wide range of uses. However, 16 knowledge regarding seedling cultivation, which would be central to the developent of Gleditsia 17 plantations, especially the use of arbuscular mycorrhizal fungi (AMF), is scarce. A pot experiment 18 was carried out under glasshouse conditions to estimate the effects of three AMF strains 19 (Funneliformis mosseae 1, Funneliformis mosseae 2, and Diversispora tortuosa) on the growth, 20 photosynthetic rate, and nutrient contents of G. sinensis seedlings. The results showed that the growth 21 parameters (seedling height, basal diameter, biomass) of seedlings were significantly increased by 22 each of the three AMF strains, associated with high root colonization rates (greater than 75%). The 23 chlorophyll concentrations and photosynthetic rates were also increased by AMF, and the 24 phosphorus (P), and potassium (K) contents in the three organs (leaf, stem, and root), and nitrogen 25 (N) contents in the leaf and stem of AM seedlings were significantly higher than in non-AM seedlings. 26 Mycorrhizal dependency of the AM seedlings was greater than 350%, and significantly correlated 27 with the increased P and K contents in all three organs and the increased N contents in leaf and stem 28 tissue. The positive effects of F. mosseae on the growth and nutrient contents of seedlings were higher 29 than those of D. tortuosa, but no significantly different effects on G. sinensis seedlings were observed 30 between the two strains of F. mosseae. Hence, the growth of G. sinensis seedlings was effectively 31 enhanced by AMF, with F. mosseae being more suitable for the inoculation of G. sinensis seedlings. 32 These results indicate that arbuscular mycorrhization is beneficial for the growth of young G. sinensis 33 plants. Further research is needed to determine whether the effects can be reproduced in the forest 34 situation. 35 36
Using isotope shift for testing nuclear theory: The case of nobelium isotopes

We calculate field isotope shifts for nobelium atoms using nuclear charge distributions which come from different nuclear models. We demonstrate that comparing calculated isotope shifts with experiment can serve as a testing ground for nuclear theories. It also provides a way of extracting parameters of nuclear charge distribution beyond nuclear root mean square (rms) radius, e.g., parameter of quadrupole deformation $\ensuremath{\beta}$. The measurements of at least two atomic transitions is needed to disentangle the contributions of the changes in deformation and nuclear rms radius into field isotopic shift. We argue that a previous interpretation of the isotope measurements in terms of $\ensuremath{\delta}\ensuremath{\langle}{r}^{2}\ensuremath{\rangle}$ between $^{252,254}\mathrm{No}$ isotopes should be amended when nuclear deformation is taken into account. We calculate isotope shifts for other known isotopes and for hypothetically metastable isotope $^{286}\mathrm{No}$ for which the predictions of nuclear models differ substantially.
10-GHz superconducting cavity piezo-optomechanics for microwave-optical photon conversion

Coherent photon conversion between microwave and optics holds promise for the realization of distributed quantum networks, in particular, the architecture that incorporates superconducting quantum processors with optical telecommunication channels. High-frequency gigahertz piezo-mechanics featuring low thermal excitations offers an ideal platform to mediate microwave-optical coupling. However, integrating nanophotonic and superconducting circuits at cryogenic temperatures to simultaneously achieve strong photon-phonon interactions remains a tremendous challenge. Here, we report the first demonstration of an integrated superconducting cavity piezo-optomechanical converter where 10-GHz phonons are resonantly coupled with photons in a superconducting microwave and a nanophotonic cavities at the same time. Benefited from the cavity-enhanced interactions, efficient bidirectional microwave-optical photon conversion is realized with an on-chip efficiency of 0.07% and an internal efficiency of 5.8%. The demonstrated superconducting piezo-optomechanical interface makes a substantial step towards quantum communication at large scale, as well as novel explorations in hybrid quantum systems such as microwave-optical photon entanglement and quantum sensing.
Learning Canonical Shape Space for Category-Level 6D Object Pose and Size Estimation

We present a novel approach to category-level 6D object pose and size estimation. To tackle intra-class shape variations, we learn canonical shape space (CASS), a unified representation for a large variety of instances of a certain object category. In particular, CASS is modeled as the latent space of a deep generative model of canonical 3D shapes with normalized pose. We train a variational auto-encoder (VAE) for generating 3D point clouds in the canonical space from an RGBD image. The VAE is trained in a cross-category fashion, exploiting the publicly available large 3D shape repositories. Since the 3D point cloud is generated in normalized pose (with actual size), the encoder of the VAE learns view-factorized RGBD embedding. It maps an RGBD image in arbitrary view into a poseindependent 3D shape representation. Object pose is then estimated via contrasting it with a pose-dependent feature of the input RGBD extracted with a separate deep neural networks. We integrate the learning of CASS and pose and size estimation into an end-to-end trainable network, achieving the state-of-the-art performance.
Graph link prediction in computer networks using Poisson matrix factorisation

Graph link prediction is an important task in cyber-security: relationships between entities within a computer network, such as users interacting with computers, or system libraries and the corresponding processes that use them, can provide key insights into adversary behaviour. Poisson matrix factorisation (PMF) is a popular model for link prediction in large networks, particularly useful for its scalability. In this article, PMF is extended to include scenarios that are commonly encountered in cyber-security applications. Specifically, an extension is proposed to explicitly handle binary adjacency matrices and include known covariates associated with the graph nodes. A seasonal PMF model is also presented to handle seasonal networks. To allow the methods to scale to large graphs, variational methods are discussed for performing fast inference. The results show an improved performance over the standard PMF model and other statistical network models.
Towards Secure Composition of Integrated Circuits and Electronic Systems: On the Role of EDA

Modern electronic systems become evermore complex, yet remain modular, with integrated circuits (ICs) acting as versatile hardware components at their heart. Electronic design automation (EDA) for ICs has focused traditionally on power, performance, and area. However, given the rise of hardware-centric security threats, we believe that EDA must also adopt related notions like secure by design and secure composition of hardware. Despite various promising studies, we argue that some aspects still require more efforts, for example: effective means for compilation of assumptions and constraints for security schemes, all the way from the system level down to the "bare metal"; modeling, evaluation, and consideration of security-relevant metrics; or automated and holistic synthesis of various countermeasures, without inducing negative cross-effects.In this paper, we first introduce hardware security for the EDA community. Next we review prior (academic) art for EDA-driven security evaluation and implementation of countermeasures. We then discuss strategies and challenges for advancing research and development toward secure composition of circuits and systems.
Searching for polarization in signed graphs: a local spectral approach

Signed graphs have been used to model interactions in social networks, which can be either positive (friendly) or negative (antagonistic). The model has been used to study polarization and other related phenomena in social networks, which can be harmful to the process of democratic deliberation in our society. An interesting and challenging task in this application domain is to detect polarized communities in signed graphs. A number of different methods have been proposed for this task. However, existing approaches aim at finding globally optimal solutions. Instead, in this paper we are interested in finding polarized communities that are related to a small set of seed nodes provided as input. Seed nodes may consist of two sets, which constitute the two sides of a polarized structure. In this paper we formulate the problem of finding local polarized communities in signed graphs as a locally-biased eigen-problem. By viewing the eigenvector associated with the smallest eigenvalue of the Laplacian matrix as the solution of a constrained optimization problem, we are able to incorporate the local information as an additional constraint. In addition, we show that the locally-biased vector can be used to find communities with approximation guarantee with respect to a local analogue of the Cheeger constant on signed graphs. By exploiting the sparsity in the input graph, an indicator-vector for the polarized communities can be found in time linear to the graph size. Our experiments on real-world networks validate the proposed algorithm and demonstrate its usefulness in finding local structures in this semi-supervised manner.
MAGIC very large zenith angle observations of the Crab Nebula up to 100 TeV

Aims. We measure the Crab Nebula γ-ray spectral energy distribution in the ~100 TeV energy domain and test the validity of existing leptonic emission models at these high energies. Methods. We used the novel very large zenith angle observations with the MAGIC telescope system to increase the collection area above 10 TeV. We also developed an auxiliary procedure of monitoring atmospheric transmission in order to assure proper calibration of the accumulated data. This employs recording optical images of the stellar field next to the source position, which provides a better than 10% accuracy for the transmission measurements. Results. We demonstrate that MAGIC very large zenith angle observations yield a collection area larger than a square kilometer. In only ~ 56 h of observations, we detect the γ-ray emission from the Crab Nebula up to 100 TeV, thus providing the highest energy measurement of this source to date with Imaging Atmospheric Cherenkov Telescopes. Comparing accumulated and archival MAGIC and Fermi/LAT data with some of the existing emission models, we find that none of them provides an accurate description of the 1 GeV to 100 TeV γ-ray signal.
Mass-to-light Ratios of Spatially Resolved Stellar Populations in M31

A galaxy’s stellar mass-to-light ratio ( ) is a useful tool for converting luminosity to stellar mass ( ). However, the practical utility of inferred from stellar population synthesis (SPS) models is limited by mismatches between the real and assumed models for star-formation history (SFH) and dust geometry, both of which vary within galaxies. Here, we measure spatial variations in and their dependence on color, SFH, and dust across the disk of M31, using a map of derived from color–magnitude diagrams of resolved stars in the Panchromatic Hubble Andromeda Treasury survey. First, we find comparable scatter in for the optical and mid-IR, contrary to the common idea that is less variable in the IR. Second, we confirm that is correlated with color for both the optical and mid-IR and report color versus relations (CMLRs) in M31 for filters used in the Sloan Digital Sky Survey and Widefield Infrared Survey Explorer. Third, we show that the CMLR residuals correlate with recent SFH, such that quiescent regions are offset to higher than star-forming regions at a fixed color. The mid-IR CMLR, however, is not linear due to the high scatter of in star-forming regions. Finally, we find a flatter optical CMLR than any SPS-based CMLRs in the literature. We show that this is an effect of dust geometry, which is typically neglected but should be accounted for when using optical data to map .
Deep Reinforcement Learning based Blind mmWave MIMO Beam Alignment

Directional beamforming is a crucial component for realizing robust wireless millimeter wave (mmWave) communication systems. Beam alignment using brute-force search introduces time overhead, and the location aided blind beam alignment adds additional hardware requirements to the system. In this paper, we propose a blind beam alignment method based on the radio frequency (RF) fingerprints of the user equipment obtained from the base stations. The proposed system performs blind beam alignment using deep reinforcement learning on a multiple-base station cellular environment with multiple mobile users. We present a novel neural network architecture that can handle a mix of both continuous and discrete actions and use policy gradient methods to train the model. Our results show that the proposed method can achieve a data rate of up to four times the data rate of the traditional method without any overheads.
The Whole Is Greater Than the Sum of Its Nonrigid Parts

According to Aristotle, a philosopher in Ancient Greece, "the whole is greater than the sum of its parts". This observation was adopted to explain human perception by the Gestalt psychology school of thought in the twentieth century. Here, we claim that observing part of an object which was previously acquired as a whole, one could deal with both partial matching and shape completion in a holistic manner. More specifically, given the geometry of a full, articulated object in a given pose, as well as a partial scan of the same object in a different pose, we address the problem of matching the part to the whole while simultaneously reconstructing the new pose from its partial observation. Our approach is data-driven, and takes the form of a Siamese autoencoder without the requirement of a consistent vertex labeling at inference time; as such, it can be used on unorganized point clouds as well as on triangle meshes. We demonstrate the practical effectiveness of our model in the applications of single-view deformable shape completion and dense shape correspondence, both on synthetic and real-world geometric data, where we outperform prior work on these tasks by a large margin.
Polygames: Improved Zero Learning

Since DeepMind’s AlphaZero, Zero learning quickly became the state-of-the-art method for many board games. It can be improved using a fully convolutional structure (no fully connected layer). Using such an architecture plus global pooling, we can create bots independent of the board size. The training can be made more robust by keeping track of the best checkpoints during the training and by training against them. Using these features, we release Polygames, our framework for Zero learning, with its library of games and its checkpoints. We won against strong humans at the game of Hex in 19 × 19, including the human player with the best ELO rank on LittleGolem; we incidentally also won against another Zero implementation, which was weaker than humans: in a discussion on LittleGolem, Hex19 was said to be intractable for zero learning. We also won in Havannah with size 8: win against the strongest player, namely Eobllor, with excellent opening moves. We also won several first places at the TAAI 2019 competitions and had positive results against strong bots in various games.
WASTED COMPLIANCE STRATEGIES? THE POLICY-MAKING STYLES OF HUNGARY AND POLAND IN THE IMPLEMENTATION OF EUROPEAN ENVIRONMENTAL DIRECTIVES

WASTED COMPLIANCE STRATEGIES? THE POLICY-MAKING STYLES OF HUNGARY AND POLAND IN THE IMPLEMENTATION OF EUROPEAN ENVIRONMENTAL DIRECTIVES How does the policy-making process affect policy compliance? Analysing the relationship between policy outcomes and their decision-making processes, the article explores the process of implementation of European directives in two CEE countries namely, Hungary and Poland, in the waste sector. Hungary and Poland have been considered as similar cases for their historical, political and economic experiences; however, EU monitoring reports have shown that these two countries have differently performed in the adoption and implementation of the EU waste legislation. The explanation for variation considered in this article relies on the existence of different “styles” of policy-making. To operationalise this concept, in the article is adopted the typology of “policy style” defined by Richardson (1982) which distinguishes between the environmental institutional and legal settings and the stakeholders’ involvement in the policy-making. The analysis of the Hungarian and Polish cases shows that, while similarities have characterised the legal and institutional environmental decision-making settings, greater difference has existed in the effective involvement of stakeholders in the environmental policy-making. In Hungary, front-runner in the implementation of the European waste legislation, we have the establishment of open and coordinated arenas of cooperation and consultation in which the stakeholders multilaterally are involved. In contrast, in Poland, laggard in the implementation of the EU waste legislation, we have the establishment of personal, bilateral and informal relations between officials of the Environmental Ministry or the Members of Parliament and the stakeholders involved in the environmental decision-making. Hence, the paper demonstrates that the existence of variations in the involvement of stakeholders determines also variation in the policy outcomes and may also explain variation in the compliance with the European legislation. Benedetta Cotta • Wasted compliance strategies? 5
HeliaGene, a bioinformatics portal for Helianthus sp. genomics

A bioinformatics portal, called HeliaGene (http://www.heliagene.org) has been developed for in-depth analyses of Helianthus sp. EST data. This portal uses the same approach as that already developed for Medicago truncatula (MENS database, http://medicago.toulouse.inra.fr/MENS), and provides a variety of pre-computed analyses and tools for EST clusters and for exploring gene function and protein families in a user-friendly fashion. The prediction of EST cluster-encoded peptides is supported by FrameD, a program originally developed for prokaryotic gene prediction. Analyses at the protein level, such as signature and domain searches, can be helpful to make predictions about gene function and to annotate EST clusters. The HeliaGene portal provides interactive access to the annotation of tens of thousands of clusters and their corresponding peptides. Generic workflows for similarities searches versus plant databases or protein family phylogenies are provided as well as specific workflows, like the detection of potential SNPs, based on the between and within Helianthus species sequence polymorphisms. In the future, HeliaGene will integrate more tools and results, including genetic maps, characterization of genetic resources and core collections, and integration of sequence-based expression data with transcriptomics experiment results.
Understanding Our People at Scale

Human psychology plays an important role in organizational performance. However, understanding our employees is a difficult task due to issues such as psychological complexities, unpredictable dynamics, and the lack of data. Leveraging evidence-based psychology knowledge, this paper proposes a hybrid machine learning plus ontology-based reasoning system for detecting human psychological artifacts at scale. This unique architecture provides a balance between system's processing speed and explain-ability. System outputs can be further consumed by graph science and/or model management system for optimizing business processes, understanding team dynamics, predicting insider threats, managing talents, and beyond.
Prediction of perovskite-related structures in 
ACuO3−x
 (
A
 = Ca, Sr, Ba, Sc, Y, La) using density functional theory and Bayesian optimization

Here, the authors predict the stability of oxygen-deficient perovskite structures in cuprates by density functional theory calculations. They introduce a combination of cluster expansion, Gaussian process, and Bayesian optimization to find stable oxygen-deficient structures. The calculations not only reproduce the reported structures but suggest the presence of unknown oxygen-deficient perovskite structures, some of which are stabilized at high pressures. This work demonstrates the great applicability of the present computational procedure for the elucidation of the structural stability of strongly correlated oxides.
Adaptive Compressive Imaging using Non-Random Dictionaries

Practical, real-time compressive sensing (CS) of images requires that an image is first partitioned into blocks to reduce the sensing matrix size. However, the blocks are found to have differing sparsity levels which makes reconstruction problematic. Adaptive CS attempts to detect blocks with low sparsity and assign more measurements to these blocks to increase reconstruction quality. This paper proposes two algorithms to estimate sparsity based on Block Boundary Variation (BBV) and 2D Discrete Cosine Transform (2D-DCT) Domain (DD) measurements. The former uses the variation in pixel values around the boundary of image blocks to modify the measurement rate whereas the latter uses 2D DCT measurements. The estimation measurements are sampled at the encoder side, from the overall budget of CS measurements available to the algorithm. The two schemes are combined with a non-random CS algorithm that uses linear transform measurements directly from the sparsifying domain, for example the Linear 2D DCT (L-DCT) to create two Adaptive L-DCT (AL-DCT) algorithms, AL-DCT-BBV and AL-DCT-DD, that can be reconstructed with very low computational complexity at the decoder, in real-time, with very good quality. The quality can be increased further by reconstructing the images using conventional CS techniques, for example the Denoising Approximate Message Passing (D-AMP) algorithm as a post-processing operation, but incurring significant time penalties. D-AMP was modified to produce two algorithms, DAMP-D and IDA, that produce better results on AL-DCT-BBV and AL-DCT-DD. The adaptive, block-based algorithms achieve state-of-the-art performance and can be reconstructed from images sensed with Single-Pixel Cameras, Multi-Pixel Cameras and Focal Plane Processing Image Sensors.
Age-of-Information Bandits

We consider a system with a single source that measures/tracks a time-varying quantity and periodically attempts to report these measurements to a monitoring station. Each update from the source has to be scheduled on one of K available communication channels. The probability of success of each attempted communication is a function of the channel used. This function is unknown to the scheduler. The metric of interest is the Age-of-Information (AoI), formally defined as the time elapsed since the destination received the recent most update from the source. We model our scheduling problem as a variant of the multi-arm bandit problem with communication channels as arms. We characterize a lower bound on the AoI regret achievable by any policy and characterize the performance of UCB, Thompson Sampling, and their variants. In addition, we propose novel policies which, unlike UCB and Thompson Sampling, use the current AoI to make scheduling decisions. Via simulations, we show the proposed AoI-aware policies outperform existing AoI-agnostic policies.