Miracle-dz/UniFAR-FTUs huggingface dataset:
Nitrogen effect on position of harvestable bolls in cotton

Abstract Nitrogen (N) affects the number, size, disposition, and distribution of cotton (Gossypium spp.) bolls. Mapping cotton plants for boll number and position may help explain yield responses to N. Cotton was planted in central Louisiana on a Norwood silt loam soil [fine‐silty, mixed (calcareous), thermic Typic Udifluvent] in 1990 and 1992 and fertilized with N at seven rates ranging from 0 to 168 kg ha‐1 in 28‐kg increments. Plants were harvested from plots at maturity and bolls were mapped. Yield fit to a quadratic curve both years and increased with N to a maximum between 120 and 150 kg ha‐1 and then decreased. The number of fruiting branches generally coincided with yield in response to N both years. Total boll counts increased with N to a maximum and then decreased. Most harvestable bolls were in the middle portion of the cotton plant in both years. In 1990, there were more bolls in the lower part of the plant than in the upper while the reverse was true in 1992. In 1990, the percentage of bolls .
Variational Beam Search for Online Learning with Distribution Shifts

We consider the problem of online learning in the presence of sudden distribution shifts as frequently encountered in applications such as autonomous navigation. Distribution shifts require constant performance monitoring and re-training. They may also be hard to detect and can lead to a slow but steady degradation in model performance. To address this problem we propose a new Bayesian meta-algorithm that can both (i) make inferences about subtle distribution shifts based on minimal sequential observations and (ii) accordingly adapt a model in an online fashion. The approach uses beam search over multiple change point hypotheses to perform inference on a hierarchical sequential latent variable modeling framework. Our proposed approach is model-agnostic, applicable to both supervised and unsupervised learning, and yields significant improvements over state-of-the-art Bayesian online learning approaches.
Bayesian Optimization for Radio Resource Management: Open Loop Power Control

We provide the reader with an accessible yet rigorous introduction to Bayesian optimisation with Gaussian processes (BOGP) for the purpose of solving a wide variety of radio resource management (RRM) problems. We believe that BOGP is a powerful tool that has been somewhat overlooked in RRM research, although it elegantly addresses pressing requirements for fast convergence, safe exploration, and interpretability. BOGP also provides a natural way to exploit prior knowledge during optimization. After explaining the nuts and bolts of BOGP, we delve into more advanced topics, such as the choice of the acquisition function and the optimization of dynamic performance functions. Finally, we put the theory into practice for the RRM problem of uplink open-loop power control (OLPC) in 5G cellular networks, for which BOGP is able to converge to almost optimal solutions in tens of iterations without significant performance drops during exploration.
Dissecting the stellar content of Leo I: a dwarf irregular caught in transition

Leo I is considered one of the youngest dwarf spheroidals (dSph) in the Local Group. Its isolation, extended star formation history (SFH), and recent perigalacticon passage (~1 Gyr ago) make Leo~I one of the most interesting nearby stellar systems. Here, we analyse deep photometric Hubble Space Telescope data via colour-magnitude diagram fitting techniques to study its global and radially-resolved SFH. We find global star formation enhancements in Leo I ~13, 5.5, 2.0, and 1.0 Gyr ago, after which it was substantially quenched. Within the context of previous works focused on Leo I, we interpret the most ancient and the youngest ones as being linked to an early formation (surviving reionisation) and the latest perigalacticon passage (transition from dIrr to dSph), respectively. We clearly identify the presence of very metal poor stars ([Fe/H]~-2) ageing ~5-6 and ~13 Gyr old. We speculate with the possibility that this metal-poor population in Leo I is related to the merging with a low mass system (possibly an ultra-faint dwarf). This event would have triggered star formation (peak of star formation ~5.5 Gyr ago) and accumulated old, metal poor stars from the accreted system in LeoI. Some of the stars born during this event would also form from accreted gas of low-metallicity (giving rise to the 5-6 Gyr low-metallicity tail). Given the intensity and extension of the 2.0 Gyr burst, we hypothesise that this enhancement could also have an external origin. Despite the quenching of star formation around 1 Gyr ago (most probably induced by ram pressure stripping with the Milky Way halo at pericentre), we report the existence of stars as young as 300-500 Myr. We also distinguish two clear spatial regions: the inner ~190 pc presents an homogeneous stellar content (size of the gaseous star forming disc in LeoI from ~4.5 to 1 Gyr ago), whereas the outer regions display a clear positive age gradient.
Transport in the non-Fermi liquid phase of isotropic Luttinger semimetals

Luttinger semimetals have quadratic band crossings at the Brillouin zone-center in three spatial dimensions. Coulomb interactions in a model that describes these systems stabilize a non-trivial fixed point associated with a non-Fermi liquid state, also known as the Luttinger-Abrikosov-Beneslavskii phase. We calculate the optical conductivity $\sigma (\omega) $ and the dc conductivity $\sigma_{dc} (T) $ of this phase by means of the Kubo formula and the Mori-Zwanzig memory matrix method, respectively. Interestingly, we find that $\sigma (\omega) $, as a function of the frequency $\omega$ of an applied ac electric field, is characterized by a small violation of the hyperscaling property in the clean limit, which is in marked contrast to the low-energy effective theories that possess Dirac quasiparticles in the excitation spectrum and obey hyperscaling. Furthermore, the effects of weak short-ranged disorder on the temperature-dependence of $\sigma_{dc} (T)$ give rise to a much stronger power-law suppression at low temperatures compared to the clean limit. Our findings demonstrate that these disordered systems are actually power-law insulators. Our theoretical results agree qualitatively with the data from recent experiments performed on Luttinger semimetal compounds like the pyrochlore iridates [ (Y$_{1-x}$Pr$_x$)$_2$Ir$_2$O$_7$ ].
Endogenous Inverse Demand Functions

Endogenous Inverse Demand Functions Buying or selling assets in a financial market impacts the prices upward or downward. Quantifying these price impacts is fundamental to many problems within finance (e.g., optimal liquidation and systemic risk). In “Endogenous Inverse Demand Functions,” Bichuch and Feinstein construct an equilibrium risk sharing problem that results in an endogenous inverse demand function. This is taken in contrast to the often assumed exogenous linear or exponential forms for the inverse demand functions. The authors determine sufficient joint properties for the financial market and assets to replicate these common exogenous forms. The properties of the general endogenous inverse demand functions, found via the equilibrium risk sharing problem, are investigated; the authors deduce that price cross-impacts, which are often assumed to be zero, naturally arise in this equilibrium setting.
Molecular machine learning with conformer ensembles

Virtual screening can accelerate drug discovery by identifying promising candidates for experimental evaluation. Machine learning is a powerful method for screening, as it can learn complex structure–property relationships from experimental data and make rapid predictions over virtual libraries. Molecules inherently exist as a three-dimensional ensemble and their biological action typically occurs through supramolecular recognition. However, most deep learning approaches to molecular property prediction use a 2D graph representation as input, and in some cases a single 3D conformation. Here we investigate how the 3D information of multiple conformers, traditionally known as 4D information in the cheminformatics community, can improve molecular property prediction in deep learning models. We introduce multiple deep learning models that expand upon key architectures such as ChemProp and SchNet, adding elements such as multiple-conformer inputs and conformer attention. We then benchmark the performance trade-offs of these models on 2D, 3D and 4D representations in the prediction of drug activity using a large training set of geometrically resolved molecules. The new architectures perform significantly better than 2D models, but their performance is often just as strong with a single conformer as with many. We also find that 4D deep learning models learn interpretable attention weights for each conformer.
An ab initio study of the tautomeric and coding properties of 8-oxo-guanine

Self-consistent field (SCF) and density function theory (DFT) abinitio calculations of 8-oxo-guanine, 8-oxo 2′-deoxyguanosine and their pairing properties with canonical nucleic acid bases have been performed. The 6,8-diketo-form of 8-oxo-G was predicted to be the most stable tautomer among all possible H9 potential structures. The relative stability of the tautomers remains the same after immersion in solvents of different polarity. The tautomeric properties of 8-oxo 2′-deoxyguanosine were the same as those of 8-oxo-G. The syn-conformations of all tautomers of nucleosides were preferred over the anti-ones, both in vacuum and in aqueous solution. The pairing potential of 8-oxo-G was estimated by means of DFT and SCF calculations. This guanine derivative has a high mispairing potential, which was strongly dependent on its tautomeric form. The 6,8-diketo-isomer of 8-oxo-G may form many different pairs but those with cytosine and guanine are more stable than the standard GC pair. The 6-enol-8-keto tautomer may also be involved in a variety of dimers, but pairs with thymine and adenine are more favoured than standard GC ones. Moreover, the less favoured tautomers such as 6-enol-2-imino or 6,8-dienol-2-imino, were also potentially able to pair with cytosine and guanine. Despite the lower stability of the tautomers, the resulting pairs are characterised by higher energy than the standard GC pair. The same conclusions may be drawn after calculations in the presence of an electrostatic field of water. The good pairing with cytosine is not surprising, due to the great geometric and electrostatic similarities between the 6,8-diketo form of 8-oxo-G and standard guanine. However, very stable dimers with guanine and thymine, if they occur in the DNA interior, may be responsible for the GC → CG transversion and GC → AT transition. These results are in good agreement with some invivo and invitro experimental observations. The source of such mutations may be related not only to the miscoding potential of the dominant tautomer, but also to that of other, less stable, tautomers of 8-oxo-G.
Probabilistic Contrastive Principal Component Analysis

Dimension reduction is useful for exploratory data analysis. In many applications, it is of interest to discover variation that is enriched in a "foreground" dataset relative to a "background" dataset. Recently, contrastive principal component analysis (CPCA) was proposed for this setting. However, the lack of a formal probabilistic model makes it difficult to reason about CPCA and to tune its hyperparameter. In this work, we propose probabilistic contrastive principal component analysis (PCPCA), a model-based alternative to CPCA. We discuss how to set the hyperparameter in theory and in practice, and we show several of PCPCA's advantages over CPCA, including greater interpretability, uncertainty quantification and principled inference, robustness to noise and missing data, and the ability to generate data from the model. We demonstrate PCPCA's performance through a series of simulations and case-control experiments with datasets of gene expression, protein expression, and images.
Deep Bayesian Active Learning, A Brief Survey on Recent Advances

Active learning frameworks offer efficient data annotation without remarkable accuracy degradation. In other words, active learning starts training the model with a small size of labeled data while exploring the space of unlabeled data in order to select most informative samples to be labeled. Generally speaking, representing the uncertainty is crucial in any active learning framework, however, deep learning methods are not capable of either representing or manipulating model uncertainty. On the other hand, from the real world application perspective, uncertainty representation is getting more and more attention in the machine learning community. Deep Bayesian active learning frameworks and generally any Bayesian active learning settings, provide practical consideration in the model which allows training with small data while representing the model uncertainty for further efficient training. In this paper, we briefly survey recent advances in Bayesian active learning and in particular deep Bayesian active learning frameworks.
COLDz: Deep 34 GHz Continuum Observations and Free–Free Emission in High-redshift Star-forming Galaxies

The high-frequency radio sky has historically remained largely unexplored due to the typical faintness of sources in this regime, and the modest survey speed compared to observations at lower frequencies. However, high-frequency radio surveys offer an invaluable tracer of high-redshift star formation, as they directly target the faint radio free–free emission. We present deep continuum observations at 34 GHz in the COSMOS and GOODS-North fields from the Karl G. Jansky Very Large Array (VLA), as part of the COLDz survey. The deep COSMOS mosaic spans down to σ = 1.3 μJy beam−1, while the wider GOODS-N observations cover to σ = 5.3 μJy beam−1. We detect a total of 18 galaxies at 34 GHz, of which nine show radio emission consistent with being powered by star formation; although for two sources, this is likely due to thermal emission from dust. Utilizing deep ancillary radio data at 1.4, 3, 5, and 10 GHz, we decompose the spectra of the remaining seven star-forming galaxies into their synchrotron and thermal free–free components, and find typical thermal fractions and synchrotron spectral indices comparable to those observed in local star-forming galaxies. We further determine free–free star formation rates (SFRs), and show that these are in agreement with SFRs from spectral energy distribution-fitting and the far-infrared/radio correlation. Our observations place strong constraints on the high-frequency radio emission in typical galaxies at high redshift, and provide some of the first insights into what is set to become a key area of study with future radio facilities, such as the Square Kilometer Array Phase 1 and next-generation VLA.
Fe vii Emission Lines in the Wavelength Range 193–197 Å

The identifications of Fe vii emission lines in the wavelength range 193–197 Å are discussed in the light of new measurements of laboratory spectra and atomic data calculations. This region is of importance to studies of solar spectra from the EUV Imaging Spectrometer (EIS) on board the Hinode spacecraft, which has its peak sensitivity at these wavelengths. Ten lines are measured, arising from seven fine structure levels in the 3p 53d 3 configuration. Two lines have not previously been reported and lead to new experimental energies for the levels. Updated experimental energies are obtained for the remaining levels. The new atomic model is used to compute theoretical values for the two density diagnostic ratios λ196. 21/λ195. 39 and λ196. 21/λ196. 06, and densities are derived from EIS spectra of coronal loop footpoints.
Minimax Risk and Uniform Convergence Rates for Nonparametric Dyadic Regression

Let $i=1,\ldots,N$ index a simple random sample of units drawn from some large population. For each unit we observe the vector of regressors $X_{i}$ and, for each of the $N\left(N-1\right)$ ordered pairs of units, an outcome $Y_{ij}$. The outcomes $Y_{ij}$ and $Y_{kl}$ are independent if their indices are disjoint, but dependent otherwise (i.e., "dyadically dependent"). Let $W_{ij}=\left(X_{i}',X_{j}'\right)'$; using the sampled data we seek to construct a nonparametric estimate of the mean regression function $g\left(W_{ij}\right)\overset{def}{\equiv}\mathbb{E}\left[\left. Y_{ij}\right|X_{i},X_{j}\right]. $ We present two sets of results. First, we calculate lower bounds on the minimax risk for estimating the regression function at (i) a point and (ii) under the infinity norm. Second, we calculate (i) pointwise and (ii) uniform convergence rates for the dyadic analog of the familiar Nadaraya-Watson (NW) kernel regression estimator. We show that the NW kernel regression estimator achieves the optimal rates suggested by our risk bounds when an appropriate bandwidth sequence is chosen. This optimal rate differs from the one available under iid data: the effective sample size is smaller and $d_W=\mathrm{dim}(W_{ij})$ influences the rate differently.
Proton inelastic cross section at ultrahigh energies

We study the consequences of high-energy collider data on the best fits to total, elastic, and inelastic cross sections for $pp$ and $p\bar{p}$ scattering using two very distinct unitarisation schemes: the eikonal and the $U$-matrix. Despite their analytic differences, we find that the two schemes lead to almost identical predictions up to EeV energies, with differences only becoming significant at GUT-scale and higher energies.
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes

We study reinforcement learning (RL) with linear function approximation where the underlying transition probability kernel of the Markov decision process (MDP) is a linear mixture model (Jia et al., 2020; Ayoub et al., 2020; Zhou et al., 2020) and the learning agent has access to either an integration or a sampling oracle of the individual basis kernels. We propose a new Bernstein-type concentration inequality for self-normalized martingales for linear bandit problems with bounded noise. Based on the new inequality, we propose a new, computationally efficient algorithm with linear function approximation named $\text{UCRL-VTR}^{+}$ for the aforementioned linear mixture MDPs in the episodic undiscounted setting. We show that $\text{UCRL-VTR}^{+}$ attains an $\tilde O(dH\sqrt{T})$ regret where $d$ is the dimension of feature mapping, $H$ is the length of the episode and $T$ is the number of interactions with the MDP. We also prove a matching lower bound $\Omega(dH\sqrt{T})$ for this setting, which shows that $\text{UCRL-VTR}^{+}$ is minimax optimal up to logarithmic factors. In addition, we propose the $\text{UCLK}^{+}$ algorithm for the same family of MDPs under discounting and show that it attains an $\tilde O(d\sqrt{T}/(1-\gamma)^{1.5})$ regret, where $\gamma\in [0,1)$ is the discount factor. Our upper bound matches the lower bound $\Omega(d\sqrt{T}/(1-\gamma)^{1.5})$ proved in Zhou et al. (2020) up to logarithmic factors, suggesting that $\text{UCLK}^{+}$ is nearly minimax optimal. To the best of our knowledge, these are the first computationally efficient, nearly minimax optimal algorithms for RL with linear function approximation.
Chern-Weil theory for principal bundles over Lie groupoids.

Let $\mathbb{X}\,=[X_1\rightrightarrows X_0]$ be a Lie groupoid equipped with a connection, given by a smooth distribution $\mathcal{H}\subset TX_1$ transversal to the fibers of the source map. Under the assumption that the distribution $\mathcal{H}$ is integrable, we define an analog of de Rham cohomology for the pair $(\mathbb{X},\mathcal{H})$ and study connections on principal $G$-bundles over $(\mathbb{X},\mathcal{H})$ in terms of the associated Atiyah sequence of vector bundles. Finally, we develop the corresponding Chern-Weil theory and study characteristic classes.
Objective-Based Hierarchical Clustering of Deep Embedding Vectors

We initiate a comprehensive experimental study of objective-based hierarchical clustering methods on massive datasets consisting of deep embedding vectors from computer vision and NLP applications. This includes a large variety of image embedding (ImageNet, ImageNetV2, NaBirds), word embedding (Twitter, Wikipedia), and sentence embedding (SST-2) vectors from several popular recent models (e.g. ResNet, ResNext, Inception V3, SBERT). Our study includes datasets with up to 4.5 million data points with embedding dimensions up to 2048. In order to address the challenge of scaling up hierarchical clustering to such large datasets we propose a new practical hierarchical clustering algorithm B++&C. It gives a 5%/20% improvement on average for the popular Moseley-Wang (MW)/ Cohen-Addad et al. (CKMM) objectives (normalized) compared to a wide range of classic methods and recent heuristics. We also introduce a theoretical algorithm B2SAT&C which achieves a 0.74-approximation for the CKMM objective in polynomial time. This is the first substantial improvement over the trivial 2/3-approximation achieved by a random binary tree. Prior to this work, the best poly-time approximation of ≈2/3 + 0.0004 was due to Charikar et al. (SODA’19)
Leray theorems in bounded cohomology theory

The paper is devoted to a generalized and simplified version of author's approach to covering theorems in bounded cohomology theory. The amenability assumptions are replaced by weaker and more natural acyclicity assumprions. In the case of open coverings the paracompactness assumption is removed. It is shown that for paracompact spaces the case of closed coverings can be reduced to the case of open coverings if spaces and subspaces in question behave nicely with respect to fundamental groups and covering spaces. Another covering theorem for closed coverings assumes nice behavior with respect to singular homology; now only its proof uses the sheaf theory. The methods apply also to $l_1$-homology. The exposition is largely self-contained. In particular, the required results from the theory of paracompact spaces are presented with full proofs.
Propagation of polarized gravitational waves

The propagation of high-frequency gravitational waves can be analyzed using the geometrical optics approximation. In the case of large but finite frequencies, the geometrical optics approximation is no longer accurate and polarization-dependent corrections at first order in wavelength modify the propagation of gravitational waves, via a spin-orbit coupling mechanism. We present a covariant derivation from first principles of effective ray equations describing the propagation of polarized gravitational waves, up to first-order terms in wavelength, on arbitrary spacetime backgrounds. The effective ray equations describe a gravitational spin Hall effect for gravitational waves and are of the same form as those describing the gravitational spin Hall effect of light, derived from Maxwell's equations.