Miracle-dz/UniFAR-FTUs huggingface dataset:
Liming and sulfur amendments improve growth and yields of maize in Rubona Ultisol and Nyamifumba Oxisol

Aluminum toxicity is a major limitation to crop production on highly weathered and leached soils in Rwanda. Moreover, sulfur though widely deficient in Rwanda acidic soils has received little attention by soil fertility researchers. A field experiment on maize response and soil nutrients status to liming materials of travertines at 3.4 t ha−1, ash wood 1.2 t ha−1 of CaO equivalent and sulfur at 10 kg ha−1 combined with NPK at 80, 60, and 45 kg ha−1 respectively was conducted in Rubona Ultisol and Nyamifumba Oxisol. Results revealed that travertine and wood ash increased the soil pH from 4.7 to 5.8 or higher and decreased exchangeable Al3+ and H+ to near 0 cmolc kg−1. Soil nutrients generally increased to high or medium ranges for crop production. Leaf dry biomass, plant height and maize grain yields were significantly higher in Rubona Ultisol than in Nyamifumba Oxisol. Plots that received wood ash, with NPKS or with NPK, generally had higher maize yields, followed by those which received travertines and NPKS or NPK which had maize growth response as compared to the control plots which received NPK only. Thereby, a combination of wood ash with NPKS or NPK, travertines with NPKS was found to neutralize soil aluminum toxicity, increase soil nutrients status to required levels for plant growth and increase maize yields significantly.
The Magellanic Edges Survey I: Description and first results

We present an overview of, and first science results from, the Magellanic Edges Survey (MagES), an ongoing spectroscopic survey mapping the kinematics of red clump and red giant branch stars in the highly substructured periphery of the Magellanic Clouds. In conjunction with Gaia astrometry, MagES yields a sample of ~7000 stars with individual 3D velocities that probes larger galactocentric radii than most previous studies. We outline our target selection, observation strategy, data reduction and analysis procedures, and present results for two fields in the northern outskirts ($>10^{\circ}$ on-sky from the centre) of the Large Magellanic Cloud (LMC). One field, located in the vicinity of an arm-like overdensity, displays apparent signatures of perturbation away from an equilibrium disk model. This includes a large radial velocity dispersion in the LMC disk plane, and an asymmetric line-of-sight velocity distribution indicative of motions vertically out of the disk plane for some stars. The second field reveals 3D kinematics consistent with an equilibrium disk, and yields $V_{\text{circ}}=87.7\pm8.0$km s$^{-1}$ at a radial distance of ~10.5kpc from the LMC centre. This leads to an enclosed mass estimate for the LMC at this radius of $(1.8\pm0.3)\times10^{10}\text{M}_{\odot}$.
Almost Envy-freeness, Envy-rank, and Nash Social Welfare Matchings

Envy-freeness up to one good (EF1) and envy-freeness up to any good (EFX) are two well-known extensions of envy-freeness for the case of indivisible items. It is shown that EF1 can always be guaranteed for agents with subadditive valuations. In sharp contrast, it is unknown whether or not an EFX allocation always exists, even for four agents and additive valuations. In addition, the best approximation guarantee for EFX is (φ − 1) ≃ 0.61 by Amanitidis et al. In order to find a middle ground to bridge this gap, in this paper we suggest another fairness criterion, namely envy-freeness up to a random good or EFR, which is weaker than EFX, yet stronger than EF1. For this notion, we provide a polynomial-time 0.73-approximation allocation algorithm. For our algorithm we introduce Nash Social Welfare Matching which makes a connection between Nash Social Welfare and envy freeness.
MeTRAbs: Metric-Scale Truncation-Robust Heatmaps for Absolute 3D Human Pose Estimation

Heatmap representations have formed the basis of human pose estimation systems for many years, and their extension to 3D has been a fruitful line of recent research. This includes 2.5D volumetric heatmaps, whose X and Y axes correspond to image space and Z to metric depth around the subject. To obtain metric-scale predictions, 2.5D methods need a separate post-processing step to resolve scale ambiguity. Further, they cannot localize body joints outside the image boundaries, leading to incomplete estimates for truncated images. To address these limitations, we propose metric-scale truncation-robust (<italic>MeTRo</italic>) volumetric heatmaps, whose dimensions are all defined in metric 3D space, instead of being aligned with image space. This reinterpretation of heatmap dimensions allows us to directly estimate complete, metric-scale poses without test-time knowledge of distance or relying on anthropometric heuristics, such as bone lengths. To further demonstrate the utility our representation, we present a differentiable combination of our 3D metric-scale heatmaps with 2D image-space ones to estimate absolute 3D pose (our <italic>MeTRAbs</italic> architecture). We find that supervision via absolute pose loss is crucial for accurate non-root-relative localization. Using a ResNet-50 backbone without further learned layers, we obtain state-of-the-art results on Human3.6M, MPI-INF-3DHP and MuPoTS-3D. Our code is publicly available. <xref ref-type="fn" rid="fn1"><sup>1</sup></xref><fn id="fn1"><label><sup>1</sup></label><p><monospace><uri>https://vision.rwth-aachen.de/metrabs</uri></monospace></p></fn>
Hardy-type inequalities and principle frequency of the $p-$Laplacian

We prove a sharp $L^p$ weighted Hardy inequality involving boundary distance $\delta$ for any domain $\Omega\subsetneq \mathbb R^n$. The inequality may be improved substantially under the additional assumption that $-\log \delta$ is subharmonic. Applications of these inequalities to the principle frequency of the $p-$Laplacian are given.
Follow the bisector: a simple method for multi-objective optimization

This study presents a novel Equiangular Direction Method (EDM) to solve a multi-objective optimization problem. We consider optimization problems, where multiple differentiable losses have to be minimized. The presented method computes descent direction in every iteration to guarantee equal relative decrease of objective functions. This descent direction is based on the normalized gradients of the individual losses. Therefore, it is appropriate to solve multi-objective optimization problems with multi-scale losses. We test the proposed method on the imbalanced classification problem and multi-task learning problem, where standard datasets are used. EDM is compared with other methods to solve these problems.
Fatty acid composition and tocopherol content of drought stressed florunner peanuts

Abstract Drought stress has been heavily investigated for its effects on production efficiency and yield, but less attention has been given to its effects on peanut quality. Peanuts (Florunner cv) were stressed for 30 days, by withholding irrigation and using shelters, at the pre-flowering period (stressl), pod formation period (stress2), and maturation period (stress3). Fatty acid composition, oleic linoleic ratio (O/L), computed iodine value (IV) and tocopherol content of Florunner peanuts were investigated. The fatty acid composition acid composition, O/L ration, IV, alpha-tocopherol (α-T) and gamma-tocopherol (γ-T) were significantly affected by drought stress and peanut grade. As peanuts increased in size regardless of stressing period, long chain saturated fatty acids [arachidic acid (20:0), behenic acid (22:0), and lignoceric acid (24:0)], eicosenoic acid (20:1), (O/L), and α-tocopherol decreased significantly. Stressing peanuts during the maturation period is most detrimental to peanut stability, .
Calliergon megalophyllum rediscovered in the Netherlands after 50 years: comparison to Swedish habitats

The moss Calliergon megalophyllum is rediscovered in the Netherlands after approximately 50 years of absence, in a location different from before: National Park Weerribben-Wieden. This is a Natura 2000 wetland area, and a Dutch hotspot for rich-fen bryophytes. The species was growing in a fen pool. Plant species composition and water chemistry were compared with Swedish samples collected throughout the country. Water chemistry of C. megalophyllum in Sweden was also compared with four other (semi-)aquatic species: C. giganteum, Scorpidium scorpioides, Sarmentypnum trichophyllum and S. exannulatum. The species is characteristic for poorly buffered habitats, but has nevertheless relatively high pH, which makes it sensitive to acidification, especially when atmospheric deposition is high. In the Dutch locality, buffer capacity is maintained by input of base-rich ditch water through small channels in the fen. The data further suggest that, like other Calliergon species, C. megalophyllum is growing in relatively nutrient-rich habitats, especially with respect to P and K. In the Netherlands, plant nutrient concentrations suggest that P is indeed not limiting, which may enhance survival of the species, as P-poor habitats in this country have become very rare.
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models

Abstract Recent work has shown that pre-trained language models such as BERT improve robustness to spurious correlations in the dataset. Intrigued by these results, we find that the key to their success is generalization from a small amount of counterexamples where the spurious correlations do not hold. When such minority examples are scarce, pre-trained models perform as poorly as models trained from scratch. In the case of extreme minority, we propose to use multi-task learning (MTL) to improve generalization. Our experiments on natural language inference and paraphrase identification show that MTL with the right auxiliary tasks significantly improves performance on challenging examples without hurting the in-distribution performance. Further, we show that the gain from MTL mainly comes from improved generalization from the minority examples. Our results highlight the importance of data diversity for overcoming spurious correlations.1
Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy

We provide a detailed asymptotic study of gradient flow trajectories and their implicit optimization bias when minimizing the exponential loss over "diagonal linear networks". This is the simplest model displaying a transition between "kernel" and non-kernel ("rich" or "active") regimes. We show how the transition is controlled by the relationship between the initialization scale and how accurately we minimize the training loss. Our results indicate that some limit behaviors of gradient descent only kick in at ridiculous training accuracies (well beyond $10^{-100}$). Moreover, the implicit bias at reasonable initialization scales and training accuracies is more complex and not captured by these limits.
SRDCNN: Strongly Regularized Deep Convolution Neural Network Architecture for Time-series Sensor Signal Classification Tasks

Deep Neural Networks (DNN) have been successfully used to perform classification and regression tasks, particularly in computer vision based applications. Recently, owing to the widespread deployment of Internet of Things (IoT), we identify that the classification tasks for time series data, specifically from different sensors are of utmost importance. In this paper, we present SRDCNN: Strongly Regularized Deep Convolution Neural Network (DCNN) based deep architecture to perform time series classification tasks. The novelty of the proposed approach is that the network weights are regularized by both L1 and L2 norm penalties. Both of the regularization approaches jointly address the practical issues of smaller number of training instances, requirement of quicker training process, avoiding overfitting problem by incorporating sparsification of weight vectors as well as through controlling of weight values. We compare the proposed method (SRDCNN) with relevant state-of-the-art algorithms including different DNNs using publicly available time series classification benchmark (the UCR/UEA archive) time series datasets and demonstrate that the proposed method provides superior performance. We feel that SRDCNN warrants better generalization capability to the deep architecture by profoundly controlling the network parameters to combat the training instance insufficiency problem of real-life time series sensor signals.
Single-partition adaptive Q-learning

This paper introduces single-partition adaptive Q-learning (SPAQL), an algorithm for model-free episodic reinforcement learning (RL), which adaptively partitions the state-action space of a Markov decision process (MDP), while simultaneously learning a time-invariant policy (i. e., the mapping from states to actions does not depend explicitly on the episode time step) for maximizing the cumulative reward. The trade-off between exploration and exploitation is handled by using a mixture of upper confidence bounds (UCB) and Boltzmann exploration during training, with a temperature parameter that is automatically tuned as training progresses. The algorithm is an improvement over adaptive Q-learning (AQL). It converges faster to the optimal solution, while also using fewer arms. Tests on episodes with a large number of time steps show that SPAQL has no problems scaling, unlike AQL. Based on this empirical evidence, we claim that SPAQL may have a higher sample efficiency than AQL, thus being a relevant contribution to the field of efficient model-free RL methods.
Electric and heat transport in a charge two-channel Kondo device

Motivated by the experimental realization of a multi-channel charge Kondo device [Iftikhar et al., Nature 526, 233 (2015)], we study generic charge and heat transport properties of the charge two-channel Kondo model. We present a comprehensive discussion of the out-of-equilibrium and time-dependent charge transport, as well as thermal transport within linear response theory. The transport properties are calculated at, and also in the vicinity of, the exactly solvable Emery-Kivelson point, which has the form of a Majorana fermion resonant level model. We focus on regimes where our solution gives exact results for the physical quantum dot device, and highlight new predictions relevant to future experiments.
Longitudinal Analysis of the Applicability of Program Repair on Past Commits

The applicability of program repair in the real world is a little researched topic. Existing program repair systems tend to only be tested on small bug datasets, such as Defects4J, that are not fully representative of real world projects. In this paper, we report on a longitudinal analysis of software repositories to investigate if past commits are amenable to program repair. Our key insight is to compute whether or not a commit lies in the search space of program repair systems. For this purpose, we present RSCommitDetector, which gets a Git repository as input and after performing a series of static analyses, it outputs a list of commits whose corresponding source code changes could likely be generated by notable repair systems. We call these commits the ``repair-space commits'', meaning that they are considered in the search space of a repair system. Using RSCommitDetector, we conduct a study on $41,612$ commits from the history of $72$ Github repositories. The results of this study show that $1.77\%$ of these commits are repair-space commits, they lie in the search space of at least one of the eight repair systems we consider. We use an original methodology to validate our approach and show that the precision and recall of RSCommitDetector are $77\%$ and $92\%$, respectively. To our knowledge, this is the first study of the applicability of program repair with search space analysis.
Impact of PAH photodissociation on the formation of small hydrocarbons in the Orion Bar and the horsehead PDRs

We study whether polycyclic aromatic hydrocarbons (PAHs) can be a weighty source of small hydrocarbons in photo-dissociation regions (PDRs). We modeled the evolution of 20 specific PAH molecules in terms of dehydrogenation and destruction of the carbon skeleton under the physical conditions of two well-studied PDRs, the Orion Bar and the Horsehead nebula which represent prototypical examples of PDRs irradiated by "high" and "low" ultraviolet radiation field. PAHs are described as microcanonical systems. The acetylene molecule is considered as the main carbonaceous fragment of the PAH dissociation as it follows from laboratory experiments and theory. We estimated the rates of acetylene production in gas phase chemical reactions and compared them with the rates of the acetylene production through the PAH dissociation. It is found that the latter rates can be higher than the former rates in the Orion Bar at $A_{\rm V} 3.5$. In the Horsehead nebula, the chemical reactions provide more acetylene than the PAH dissociation. The produced acetylene participate in the reactions of the formation of small hydrocarbons (C$_2$H, C$_3$H, C$_3$H$^{+}$, C$_3$H$_2$, C$_4$H). Acetylene production via the PAH destruction may increase the abundances of small hydrocarbons produced in gas phase chemical reactions in the Orion Bar only at $A_{\rm V}>3.5$. In the Horsehead nebula, the contribution of PAHs to the abundances of the small hydrocarbons is negligible. We conclude that the PAHs are not a major source of small hydrocarbons in both PDRs except some locations in the Orion Bar.
A Warm Jupiter Transiting an M Dwarf: A TESS Single-transit Event Confirmed with the Habitable-zone Planet Finder

We confirm the planetary nature of a warm Jupiter transiting the early M dwarf TOI-1899 using a combination of available TESS photometry; high-precision, near-infrared spectroscopy with the Habitable-zone Planet Finder; and speckle and adaptive optics imaging. The data reveal a transiting companion on an ∼29 day orbit with a mass and radius of and , respectively. The star, TOI-1899, is the lowest-mass star known to host a transiting warm Jupiter, and we discuss the follow-up opportunities afforded by a warm ( K) gas giant orbiting an M0 star. Our observations reveal that TOI-1899.01 is a puffy warm Jupiter, and we suggest additional transit observations to both refine the orbit and constrain the true dilution observed in TESS.
A 3D Framework to Explore the Propagation Effects in Stars Exhibiting Electron Cyclotron Maser Emission

Recently, coherent radio emission has been discovered from a number of hot magnetic stars, via the process of electron cyclotron maser emission (ECME). This emission, observed in the form of highly circularly polarized pulses, has interesting properties that contain information about the host star. One of the important properties of ECME is the frequency dependence of the pulse arrival time. This has been attributed to the propagation effect by Trigilio et al. and could explain the sequence observed for CU Vir qualitatively. However, no quantitative treatment exists for this phenomenon despite it being a promising tool to estimate the density in the stellar magnetosphere. Additionally, the effect of propagation through the magnetosphere on ECME has been thought to be limited to giving rise to a particular sequence of arrival of pulses, and in some cases producing the upper cutoff frequency for ECME. Here, we present a framework to deal with the propagation effect by considering continuous refraction in the inner magnetosphere of the star. This framework is capable of incorporating any type of density distribution, and in principle any type of magnetic field, though we limit ourselves to a dipolar magnetic field for this work. We show by simulation that for stars with high obliquity, the propagation effect can influence not only the sequence of arrival of pulses drastically, but also the pulse shapes, and the observability of a pulse from a particular magnetosphere.
Representation Transfer by Optimal Transport

Deep learning currently provides the best representations of complex objects for a wide variety of tasks. However, learning these representations is an expensive process that requires very large training samples and significant computing resources. Thankfully, sharing these representations is a common practice, enabling to solve new tasks with relatively little training data and few computing resources; the transfer of representations is nowadays an essential ingredient in numerous real-world applications of deep learning. Transferring representations commonly relies on the parameterized form of the features making up the representation, as encoded by the computational graph of these features. In this paper, we propose to use a novel non-parametric metric between representations. It is based on a functional view of features, and takes into account certain invariances of representations, such as the permutation of their features, by relying on optimal transport. This distance is used as a regularization term promoting similarity between two representations. We show the relevance of this approach in two representation transfer settings, where the representation of a trained reference model is transferred to another one, for solving a new related task (inductive transfer learning), or for distilling knowledge to a simpler model (model compression).
Consensus Halving for Sets of Items

Consensus halving refers to the problem of dividing a resource into two parts so that every agent values both parts equally. Prior work shows that, when the resource is represented by an interval, a consensus halving with at most n cuts always exists but is hard to compute even for agents with simple valuation functions. In this paper, we study consensus halving in a natural setting in which the resource consists of a set of items without a linear ordering. For agents with linear and additively separable utilities, we present a polynomial-time algorithm that computes a consensus halving with at most n cuts and show that n cuts are almost surely necessary when the agents’ utilities are randomly generated. On the other hand, we show that, for a simple class of monotonic utilities, the problem already becomes polynomial parity argument, directed version–hard. Furthermore, we compare and contrast consensus halving with the more general problem of consensus k-splitting, with which we wish to divide the resource into k parts in possibly unequal ratios and provide some consequences of our results on the problem of computing small agreeable sets.
The magnetic obliquity of accreting T Tauri stars

Classical T Tauri stars (CTTSs) accrete material from their discs through their magnetospheres. The geometry of the accretion flow strongly depends on the magnetic obliquity, i.e. the angle between the rotational and magnetic axes. We aim at deriving the distribution of magnetic obliquities in a sample of 10 CTTSs. For this, we monitored the radial velocity variations of the He i λ5876 Å line in these stars’ spectra along their rotational cycle. He i is produced in the accretion shock, close to the magnetic pole. When the magnetic and rotational axes are not aligned, the radial velocity of this line is modulated by stellar rotation. The amplitude of modulation is related to the star’s projected rotational velocity, vsin i, and the latitude of the hotspot. By deriving vsin i and He i λ5876 radial velocity curves from our spectra, we thus obtain an estimate of the magnetic obliquities. We find an average obliquity in our sample of 11.4° with an rms dispersion of 5.4°. The magnetic axis thus seems nearly, but not exactly aligned with the rotational axis in these accreting T Tauri stars, somewhat in disagreement with studies of spectropolarimetry, which have found a significant misalignment (≳20°) for several CTTSs. This could simply be an effect of low number statistics, or it may be due to a selection bias of our sample. We discuss possible biases that our sample may be subject to. We also find tentative evidence that the magnetic obliquity may vary according to the stellar interior and that there may be a significant difference between fully convective and partly radiative stars.