Miracle-dz/UniFAR-FTUs huggingface dataset:
Relationship between eucalyptus resistance to eucalyptus dieback and defense enzyme system

In order to determine the biochemical index,which indicates resistance of eucalyptus to eucalyptus dieback(Cylindrodadium quinqueseptatum Morgan),11 eucalyptus cultivars were selected from Fujian Province and the activities of two major defense-enzymes,peroxidase(POD) and superoxide dismutase(SOD),analyzed.The results show that POD and SOD activities of healthy or infected leaves of the 11 cultivars change in a regular manner in the following order: resistant cultivar moderate resistant cultivar moderate susceptible cultivar susceptible cultivar.POD isozyme band shows no regularity with resistance to eucalyptus dieback before or after inoculation,while SOD isozyme band has a relatively positive relation to the resistance to eucalyptus dieback.In relation to breeding,POD and SOD activity before and after inoculation and SOD isozyme bands after inoculation can be used as early biochemical indexes for resistance of eucalyptus to eucalyptus dieback.
LACO: A Latency-Driven Network Slicing Orchestration in Beyond-5G Networks

Network Slicing is expected to become a game changer in the upcoming 5G networks and beyond, enlarging the telecom business ecosystem through still-unexplored vertical industry profits. This implies that heterogeneous service level agreements (SLAs) must be guaranteed per slice given the multitude of predefined requirements. In this paper, we pioneer a novel radio slicing orchestration solution that simultaneously provides latency and throughput guarantees in a multi-tenancy environment. Leveraging on a solid mathematical framework, we exploit the exploration-vs-exploitation paradigm by means of a multi-armed-bandit-based (MAB) orchestrator, LACO, that makes adaptive resource slicing decisions with no prior knowledge on the traffic demand or channel quality statistics. As opposed to traditional MAB methods that are blind to the underlying system, LACO relies on system structure information to expedite decisions. After a preliminary simulations campaign empirically proving the validness of our solution, we provide a robust implementation of LACO using off-the-shelf equipment to fully emulate realistic network conditions: near-optimal results within affordable computational time are measured when LACO is in place.
Masked Label Prediction: Unified Massage Passing Model for Semi-Supervised Classification

Graph neural network (GNN) and label propagation algorithm (LPA) are both message passing algorithms, which have achieved superior performance in semi-supervised classification. GNN performs feature propagation by a neural network to make predictions, while LPA uses label propagation across graph adjacency matrix to get results. However, there is still no effective way to directly combine these two kinds of algorithms. To address this issue, we propose a novel Unified Message Passaging Model (UniMP) that can incorporate feature and label propagation at both training and inference time. First, UniMP adopts a Graph Transformer network, taking feature embedding and label embedding as input information for propagation. Second, to train the network without overfitting in self-loop input label information, UniMP introduces a masked label prediction strategy, in which some percentage of input label information are masked at random, and then predicted. UniMP conceptually unifies feature propagation and label propagation and is empirically powerful. It obtains new state-of-the-art semi-supervised classification results in Open Graph Benchmark (OGB).
Semi-permeability of graphene nanodrums in sucrose solution

Semi-permeable membranes are important elements in water purification and energy generation applications, for which the atomic thickness and strength of graphene can enhance efficiency and permeation rate while maintaining good selectivity. Here, we show that an osmotic pressure difference forms across a suspended graphene membrane as a response to a sucrose concentration difference, providing evidence for its semi-permeability. This osmotic pressure difference is detected via the deflection of the graphene membrane that is measured by atomic force microscopy. Using this technique, the time dependence of this deflection allows us to measure the water permeation rate of a single 3.4 µm diameter graphene membrane. Its value is close to the expected value of a single nanopore in graphene. The method thus allows one to experimentally study the semi-permeability of graphene membranes at the microscale when the leakage rate is minuscule. It can therefore find use in the development of graphene membranes for filtration, and can enable sensors that measure the concentration and composition of solutions.
3′ Termini of Mammalian and Insect Mitochondrial rRNAs

We have presented evidence that the 3′ terminus of the large ribosomal subunit (LSU) RNA of hamster (BHK-21) mitochondria, 17S RNA, is distinctive. Most molecules contain 3′ -terminal oligoadenylate moieties, which we presume to be added posttranscriptionally; and the putative transcribed moieties are “ragged,” ending at any of several residues (Dubin et al. 1981). We have now defined the 3′ termini of 17S RNA more precisely, and we have done comparative studies on the small subunit (SSU) RNA of hamster cells, 13S RNA; on the homologous human rRNAs; and on the homologous RNA of mosquito ( Aedes albopictus ) cells. 17S RNA TERMINI Our approach to characterizing terminal sequences was similar to that described earlier (Dubin et al. 1981). Figure 1 shows a representative fingerprint from an RNase-T1 digest of 3′-end-labeled 17S RNA. Several of the larger oligonucleotides were sequenced using partial exonuclease digestion and mobility-shift analysis. The sequences of oligonucleotides 30 and 21 were found to be UUUAUUAGp and UUUAUUACp, respectively. Spot 21 is now seen to be the most plentiful member of a major oligoadenylated family, UUUAUUA n Cp (spots 20–25); the adenylated congeners of spot 30 appear as A n Cp in RNase-T1 digests. We presume that the minor family between the “20” and the “11” families is UUUAUA n Cp. This would indicate that the 11 family is U 3 A n Cp rather than U 2 A n Cp (cf. Dubin et al. 1981), and mobility-shift analysis confirmed this. Together with an AGGA n Cp family identified in RNase-A digests, these oligonucleotides can be overlapped to form a putative 3′-terminal-transcribed moiety, GUUUAUUAGGUUU.
The Peskin problem with viscosity contrast

The Peskin problem models the dynamics of a closed elastic filament immersed in an incompressible fluid. In this paper, we consider the case when the inner and outer viscosities are possibly different. This viscosity contrast adds further non-local effects to the system through the implicit non-local relation between the net force and the free interface. We prove the first global well-posedness result for the Peskin problem in this setting. The result applies for medium size initial interfaces in critical spaces and shows instant analytic smoothing. We carefully calculate the medium size constraint on the initial data. These results are new even without viscosity contrast.
Magnetization of the spin-1/2 Heisenberg antiferromagnet on the triangular lattice

After decades of debate, now there is a rough consensus that at zero temperature the spin-$1/2$ Heisenberg antiferromagnet on the triangular lattice is three-sublattice $120^\circ$ magnetically ordered, in contrast to a quantum spin liquid as originally proposed. However, there remains considerable discrepancy in the magnetization reported among various methods. To resolve this issue, in this work we revisit this model by the tensor-network state algorithm. The ground-state energy per bond $E_b$ and magnetization per spin $M_0$ in the thermodynamic limit are obtained with high precision. The former is estimated to be $E_b = -0.18334(10)$. This value agrees well with that from the series expansion. The three-sublattice magnetic order is firmly confirmed and the magnetization is determined as $M_0 = 0.161(5)$. It is about $32\%$ of its classical value and slightly below the lower bound from the series expansion. In comparison with the best estimated value by Monte Carlo and density-matrix renormalization group, our result is about $20\%$ smaller. This magnetic order is consistent with further analysis of the three-body correlation. Our work thus provides new benchmark results for this prototypical model.
Nonexistence of variational minimizers related to a quasilinear singular problem in metric measure spaces

In this article we consider a variational problem related to a quasilinear singular problem and obtain a nonexistence result in a metric measure space with a doubling measure and a Poincare inequality. Our method is purely variational and to the best of our knowledge, this is the first work concerning singular problems in a general metric setting.
Disorder and magnetic field induced Bose-metal state in two-dimensional 
Tax(SiO2)1−x
 granular films

The origin of the intermediate anomalous metallic state in two-dimensional superconductor materials remains enigmatic. In the present paper, we observe such a state in a series of $\sim$9.0 nm thick Ta$_x$(SiO$_2$)$_{1-x}$ ($x$ being the volume fraction of Ta) nanogranular films. At zero field, the $x$ $\gtrsim$ 0.75 films undergo a Berezinskii-Kosterlitz-Thouless transition as transform from normal to superconducing states upon cooling. For the $x$ $\lesssim$ 0.71 films, the resistance increases with decreasing temperature from 2 K down to 40 mK. A normal state to anomalous metallic state transition is observed in the $x$ $\simeq$ 0.73 film, i.e., near the transition temperature, the resistance of the film decreases sharply upon cooling as if the system would cross over to superconducting state, but then saturates to a value far less than that in normal state. When a small magnetic field perpendicular to the film plane is applied, the anomalous metallic state occurs in the $x$ $\gtrsim$ 0.75 films. It is found that both disorder and magnetic field can induce the transition from superconductor to anomalous metal and their influences on the transition are similar. For the the magnetic field induced case, we find the sheet resistance $R_{\square}(T,H)$ ($T$ and $H$ being the temperature and the magnitude of magnetic field) data near the crossover from the anomalous metal to superconductor and in the vicinity of the anomalous metal to insulator transition, respectively, obey unique scaling laws deduced from the Bose-metal model. Our results strongly suggest that the anomalous metallic state in the Ta$_x$(SiO$_2$)$_{1-x}$ granular films is bosonic and dynamical gauge field fluctuation resulting from superconducting quantum fluctuations plays a key role in its formation.
Comparison of camera-based and 3D LiDAR-based loop closures across weather conditions

Loop closure based on camera images provides excellent results on benchmarking datasets, but might struggle in real-world adverse weather conditions like direct sun, rain, fog, or just darkness at night. In automotive applications, the sensory setups include 3D LiDARs that provide information complementary to cameras. The presented article focuses on the evaluation of camera-based, LiDAR-based, and joint camera-LiDAR-based loop closures applying a similar processing pipeline consisting of a neural network under varying weather conditions using the newly available USyd dataset. The experiments performed on the same trajectories in diverse weather conditions over 50 weeks prove that a 16-line 3D LiDAR can be used to supplement image-based loop closure to increase loop closure performance. This proves that there is a need for more research into loop closures performed with multi-sensory setups.
On Spectral Properties of Signed Laplacians With Connections to Eventual Positivity

Signed graphs have appeared in a broad variety of applications, ranging from social networks to biological networks, from distributed control and computation to power systems. In this article, we investigate spectral properties of signed Laplacians for undirected signed graphs. We find conditions on the negative weights under which a signed Laplacian is positive semidefinite via the Kron reduction and multiport network theory. For signed Laplacians that are indefinite, we characterize their inertias with the same framework. Furthermore, we build connections between signed Laplacians, generalized $M$-matrices, and eventually exponentially positive matrices.
PGA-based Predictor-Corrector Algorithms for Monotone Generalized Variational Inequality

In this paper, we consider the monotone generalized variational inequality (MGVI) where the monotone operator is Lipschitz continuous. Inspired by the extragradient method and the projection contraction algorithms for monotone variational inequality (MVI), we propose a class of PGA-based Predictor-Corrector algorithms for MGVI. A significant characteristic of our algorithms for separable multi-blocks convex optimization problems is that they can be well adapted for parallel computation. Numerical simulations about different models for sparsity recovery show the wide applicability and effectiveness of our proposed methods.
AutoKWS: Keyword Spotting with Differentiable Architecture Search

Smart audio devices are gated by an always-on lightweight keyword spotting program to reduce power consumption. It is however challenging to design models that have both high accuracy and low latency for accurate and fast responsiveness. Many efforts have been made to develop end-to-end neural networks, in which depthwise separable convolutions, temporal convolutions, and LSTMs are adopted as building units. Nonetheless, these networks designed with human expertise may not achieve an optimal trade-off in an expansive search space. In this paper, we propose to leverage recent advances in differentiable neural architecture search to discover more efficient networks. Our searched model attains 97.2% top-1 accuracy on Google Speech Command Dataset v1 with only nearly 100K parameters.
QSAN: A Quantum-probability based Signed Attention Network for Explainable False Information Detection

False information detection on social media is challenging as it commonly requires tedious evidence-collecting but lacks available comparative information. Clues mined from user comments, as the wisdom of crowds, could be of considerable benefit to this task. However, it is non-trivial to capture the complex semantics from the contents and comments in consideration of their implicit correlations. Although deep neural networks have good expressive power, one major drawback is the lack of explainability. In this paper, we focus on how to learn from the post contents and related comments in social media to understand and detect the false information more effectively, with explainability. We thus propose a Quantum-probability based Signed Attention Network (QSAN) that integrates the quantum-driven text encoding and a novel signed attention mechanism in a unified framework. QSAN is not only able to distinguish important comments from the others, but also can exploit the conflicting social viewpoints in the comments to facilitate the detection. Moreover, QSAN is advantageous with its explainability in terms of transparency due to quantum physics meanings and the attention weights. Extensive experiments on real-world datasets show that our approach outperforms state-of-the-art baselines and can provide different kinds of user comments to explain why a piece of information is detected as false.
Nebojte se logistické regrese

The aim of this article is to acquaint Czech sociologists who are interested in quantitative methods of data analysis with logistic regression. The logistic regression model has been in use in statistical analyses for many years. It is the standard method for regression analysis of categorical data in many fields. Unfortunately, in the Czech Republic the method is less known, and consequently rarely used. The content of the article is divided into several parts according to the type of the explained variable. The article begins with the simplest case, in which the explained variable is dichotomous. It continues with the case, in which the explained variable is polytomous and nominal, or is treated as being nominal. The article ends with the case, in which the explained variable is ordinal. Special attention is devoted to the manner of treating categorical explaining variables, to the assessment of the adequacy of the fitted model, and to the interpretation of the coefficients of the logistic regression model. All these steps are demonstrated on real data set through the use of the SPSS and GoldMineR software packages. Sociologický časopis, 2000, Vol. 36 (No. 4: 475-492)
Targeted Exploration of Dark Photon Parameter Space at Mu3e

In 2016, the Atomki collaboration discovered an anomaly in the decay of beryllium excited states that matched theoretical predictions for an unknown force-carrying boson. We suggest a search for such a hypothesized `dark photon' over the same parameter space to be carried out at the Mu3e detector. We discuss the mechanisms by which a dark photon may be observed, and outline the details of this detector experiment. We extrapolate on this groundwork to analyze projections for the sensitivity of the experiment. Additionally, we quantify the expected precision of the measurements and how the resulting data would indicate the existence of a dark photon. Finally, we discuss potential challenges of the proposed search at Mu3e.
Commuting Magic Square Matrices

We review a known method of compounding two magic square matrices of order m and n with the all-ones matrix to form two magic square matrices of order mn. We show that these compounded matrices commute. Simple formulas are derived for their Jordan form and singular value decomposition. We verify that regular (associative) and pandiagonal commuting magic squares can be constructed by compounding. In a special case the compounded matrices are similar. Generalization of compounding to a wider class of commuting magic squares is considered. Three numerical examples illustrate our theoretical results.
Learning the smoothness of noisy curves with application to online curve estimation

Combining information both within and across trajectories, we propose a simple estimator for the local regularity of the trajectories of a stochastic process. Independent trajectories are measured with errors at randomly sampled time points. Non-asymptotic bounds for the concentration of the estimator are derived. Given the estimate of the local regularity, we build a nearly optimal local polynomial smoother from the curves from a new, possibly very large sample of noisy trajectories. We derive non-asymptotic pointwise risk bounds uniformly over the new set of curves. Our estimates perform well in simulations. Real data sets illustrate the effectiveness of the new approaches.
Dragon’s Lair: On the Large-scale Environment of BL Lac Objects

The most elusive and extreme subclass of active galactic nuclei (AGNs), known as BL Lac objects, shows features that can only be explained as the result of relativistic effects occurring in jets pointing at a small angle with respect to the line of sight. A longstanding issue is the identification of the BL Lac parent population with jets oriented at larger angles. According to the “unification scenario” of AGNs, radio galaxies with low luminosity and an edge-darkened radio morphology are the most promising candidate parent population of BL Lacs. Here we compare the large-scale environment, an orientation-independent property, of well-defined samples of BL Lacs with samples of radio galaxies all lying in the local universe. Our study reveals that BL Lacs and radio galaxies live in significantly different environments, challenging predictions of the unification scenario. We propose a solution to this problem proving that large-scale environments of BL Lacs are statistically consistent with those of compact radio sources, known as FR 0s, and share similar properties. This implies that highly relativistic jets are ubiquitous and are the natural outcome of the accretion of gas into the deep gravitational potential well produced by supermassive black holes.
Deep Learning, Predictability, and Optimal Portfolio Returns

We study optimal dynamic portfolio choice of a long-horizon investor who uses deep learning methods to predict equity returns when forming optimal portfolios. The results show statistically and economically significant out-of-sample portfolio benefits of deep learning as measured by high certainty equivalent returns and Sharpe ratios. Return predictability via deep learning generates substantially improved portfolio performance across different subsamples, particularly the recession periods. These gains are robust to including transaction costs, short-selling and borrowing constraints.
Genealogies in bistable waves

We study a model of selection acting on a diploid population (one in which each individual carries two copies of each gene) living in one spatial dimension. We suppose a particular gene appears in two forms (alleles) $A$ and $a$, and that individuals carrying $AA$ have a higher fitness than $aa$ individuals, while $Aa$ individuals have a lower fitness than both $AA$ and $aa$ individuals. The proportion of advantageous $A$ alleles expands through the population approximately according to a travelling wave. We prove that on a suitable timescale, the genealogy of a sample of $A$ alleles taken from near the wavefront converges to a Kingman coalescent as the population density goes to infinity. This contrasts with the case of directional selection in which the corresponding limit is thought to be the Bolthausen-Sznitman coalescent. The proof uses 'tracer dynamics'.
Multivariable times series classification through an interpretable representation

Multivariate time series classification is a task with increasing importance due to the proliferation of new problems in various fields (economy, health, energy, transport, crops, etc.) where a large number of information sources are available. Direct extrapolation of methods that traditionally worked in univariate environments cannot frequently be applied to obtain the best results in multivariate problems. This is mainly due to the inability of these methods to capture the relationships between the different variables that conform a multivariate time series. The multivariate proposals published to date offer competitive results but are hard to interpret. In this paper we propose a time series classification method that considers an alternative representation of time series through a set of descriptive features taking into account the relationships between the different variables of a multivariate time series. We have applied traditional classification algorithms obtaining interpretable and competitive results.