Miracle-dz/UniFAR-FTUs huggingface dataset:
Apsidal asymmetric-alignment of Jupiter Trojans

The most distant Kuiper belt objects exhibit the clustering in their orbits, and this anomalous architecture could be caused by Planet 9 with large eccentricity and high inclination. We then suppose that the orbital clustering of minor planets may be observed somewhere else in the solar system. In this paper, we consider the over 7000 Jupiter Trojans from the Minor Planet Center, and find that they are clustered in the longitude of perihelion $, around the locations $J + 60 ◦ and $J − 60◦ ($J is the longitude of perihelion of Jupiter) for the L4 and L5 swarms, respectively. Then we build a Hamiltonian system to describe the associated dynamical aspects for the co-orbital motion. The phase space displays the existence of the apsidally aligned islands of libration centered on ∆$ = $−$J ≈ ±60◦, for the Trojan-like orbits with eccentricities e < 0.1. Through a detailed analysis, we have shown that the observed Jupiter Trojans with proper eccentricities ep < 0.1 spend most of their time in the range of |∆$| = 0− 120◦, while the more eccentric ones with ep > 0.1 are too few to affect the orbital clustering within this ∆$ range for the entire Trojan population. Our numerical results further prove that, even starting from a uniform ∆$ distribution, the apsidal alignment of simulated Trojans similar to the observation can appear on the order of the age of the solar system. We conclude that the apsidal asymmetricalignment of Jupiter Trojans is robust, and this new finding can be helpful to design the survey strategy in the future.
Giant Outer Transiting Exoplanet Mass (GOT ‘EM) Survey. II. Discovery of a Failed Hot Jupiter on a 2.7 Yr, Highly Eccentric Orbit

Radial velocity (RV) surveys have discovered giant exoplanets on au-scale orbits with a broad distribution of eccentricities. Those with the most eccentric orbits are valuable laboratories for testing theories of high-eccentricity migration. However, few such exoplanets transit their host stars, thus removing the ability to apply constraints on formation from their bulk internal compositions. We report the discovery of Kepler-1704 b, a transiting 4.15 M J giant planet on a 988.88 day orbit with an extreme eccentricity of 0.921−0.015+0.010 . Our decade-long RV baseline from the Keck I telescope allows us to measure the orbit and bulk heavy-element composition of Kepler-1704 b and place limits on the existence of undiscovered companions. A failed hot Jupiter, Kepler-1704 b was likely excited to high eccentricity by scattering events that possibly began during its gas accretion phase. Its final periastron distance was too large to allow for tidal circularization, so now it orbits its host from distances spanning 0.16–3.9 au. The maximum difference in planetary equilibrium temperature resulting from this elongated orbit is over 700 K. A simulation of the thermal phase curve of Kepler-1704 b during periastron passage demonstrates that it is a remarkable target for atmospheric characterization from the James Webb Space Telescope, which could potentially also measure the planet’s rotational period as the hot spot from periastron rotates in and out of view. Continued characterization of the Kepler-1704 system promises to refine theories explaining the formation of hot Jupiters and cool giant planets like those in the solar system.
Optimality of the Discrete Fourier Transform for Beamspace Massive MU-MIMO Communication

Beamspace processing is an emerging technique to reduce baseband complexity in massive multiuser (MU) multiple-input multiple-output (MIMO) communication systems operating at millimeter-wave (mmWave) and terahertz frequencies. The high directionality of wave propagation at such high frequencies ensures that only a small number of transmission paths exist between user equipments and basestation (BS). In order to resolve the sparse nature of wave propagation, beamspace processing traditionally computes a spatial discrete Fourier transform (DFT) across a uniform linear antenna array at the BS where each DFT output is associated with a specific beam. In this paper, we study optimality conditions of the DFT for sparsity-based beamspace processing with idealistic mmWave channel models and realistic channels. To this end, we propose two algorithms that learn unitary beamspace transforms using an $\ell^{4}$-norm-based sparsity measure, and we investigate their optimality theoretically and via simulations.
Deep Learning on a Data Diet: Finding Important Examples Early in Training

Recent success in deep learning has partially been driven by training increasingly overparametrized networks on ever larger datasets. It is therefore natural to ask: how much of the data is superfluous, which examples are important for generalization, and how do we find them? In this work, we make the striking observation that, in standard vision datasets, simple scores averaged over several weight initializations can be used to identify important examples very early in training. We propose two such scores -- the Gradient Normed (GraNd) and the Error L2-Norm (EL2N) scores -- and demonstrate their efficacy on a range of architectures and datasets by pruning significant fractions of training data without sacrificing test accuracy. In fact, using EL2N scores calculated a few epochs into training, we can prune half of the CIFAR10 training set while slightly improving test accuracy. Furthermore, for a given dataset, EL2N scores from one architecture or hyperparameter configuration generalize to other configurations. Compared to recent work that prunes data by discarding examples that are rarely forgotten over the course of training, our scores use only local information early in training. We also use our scores to detect noisy examples and study training dynamics through the lens of important examples -- we investigate how the data distribution shapes the loss surface and identify subspaces of the model's data representation that are relatively stable over training.
Explore and Control with Adversarial Surprise

Unsupervised reinforcement learning (RL) studies how to leverage environment statistics to learn useful behaviors without the cost of reward engineering. However, a central challenge in unsupervised RL is to extract behaviors that meaningfully affect the world and cover the range of possible outcomes, without getting distracted by inherently unpredictable, uncontrollable, and stochastic elements in the environment. To this end, we propose an unsupervised RL method designed for high-dimensional, stochastic environments based on an adversarial game between two policies (which we call Explore and Control) controlling a single body and competing over the amount of observation entropy the agent experiences. The Explore agent seeks out states that maximally surprise the Control agent, which in turn aims to minimize surprise, and thereby manipulate the environment to return to familiar and predictable states. The competition between these two policies drives them to seek out increasingly surprising parts of the environment while learning to gain mastery over them. We show formally that the resulting algorithm maximizes coverage of the underlying state in block MDPs with stochastic observations, providing theoretical backing to our hypothesis that this procedure avoids uncontrollable and stochastic distractions. Our experiments further demonstrate that Adversarial Surprise leads to the emergence of complex and meaningful skills, and outperforms state-of-the-art unsupervised reinforcement learning methods in terms of both exploration and zero-shot transfer to downstream tasks.
A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification

Black-box machine learning models are now routinely used in high-risk settings, like medical diagnostics, which demand uncertainty quantification to avoid consequential model failures. Conformal prediction is a user-friendly paradigm for creating statistically rigorous uncertainty sets/intervals for the predictions of such models. Critically, the sets are valid in a distribution-free sense: they possess explicit, non-asymptotic guarantees even without distributional assumptions or model assumptions. One can use conformal prediction with any pre-trained model, such as a neural network, to produce sets that are guaranteed to contain the ground truth with a user-specified probability, such as 90%. It is easy-to-understand, easy-to-use, and general, applying naturally to problems arising in the fields of computer vision, natural language processing, deep reinforcement learning, and so on. This hands-on introduction is aimed to provide the reader a working understanding of conformal prediction and related distribution-free uncertainty quantification techniques with one self-contained document. We lead the reader through practical theory for and examples of conformal prediction and describe its extensions to complex machine learning tasks involving structured outputs, distribution shift, time-series, outliers, models that abstain, and more. Throughout, there are many explanatory illustrations, examples, and code samples in Python. With each code sample comes a Jupyter notebook implementing the method on a real-data example; the notebooks can be accessed and easily run using our codebase.
Only Train Once: A One-Shot Neural Network Training And Pruning Framework

Structured pruning is a commonly used technique in deploying deep neural networks (DNNs) onto resource-constrained devices. However, the existing pruning methods are usually heuristic, task-specified, and require an extra fine-tuning procedure. To overcome these limitations, we propose a framework that compresses DNNs into slimmer architectures with competitive performances and significant FLOPs reductions by Only-Train-Once (OTO). OTO contains two keys: (i) we partition the parameters of DNNs into zero-invariant groups, enabling us to prune zero groups without affecting the output; and (ii) to promote zero groups, we then formulate a structured-sparsity optimization problem and propose a novel optimization algorithm, Half-Space Stochastic Projected Gradient (HSPG), to solve it, which outperforms the standard proximal methods on group sparsity exploration and maintains comparable convergence. To demonstrate the effectiveness of OTO, we train and compress full models simultaneously from scratch without fine-tuning for inference speedup and parameter reduction, and achieve state-of-the-art results on VGG16 for CIFAR10, ResNet50 for CIFAR10 and Bert for SQuAD and competitive result on ResNet50 for ImageNet. The source code is available at https://github.com/tianyic/only_train_once.
Axion-photon conversion in strongly magnetised plasmas

Axion dark matter can resonantly convert to photons in the magnetosphere of neutron stars, possibly giving rise to radio signals observable on Earth. This method for the indirect detection of axion dark matter has recently received significant attention in the literature. The calculation of the radio signal is complicated by a number of effects; most importantly, the gravitational infall of the axions onto the neutron star accelerates them to semi-relativistic speed, and the neutron star magnetosphere is highly anisotropic. Both of these factors complicate the calculation of the conversion of axions to photons. In this work, we present the first fully three-dimensional calculation of the axion-photon conversion in highly magnetised anisotropic media. Depending on the axion trajectory, this calculation leads to orders-of-magnitude differences in the conversion compared to the simplified one-dimensional calculation used so far in the literature, altering the directionality of the produced photons. Our results will have important implications for the radio signal one would observe in a telescope.
Multiple twins in permutations

By an $r$-tuplet in a permutation we mean a family of $r$ pairwise disjoint subsequences with the same relative order. The length of an $r$-tuplet is defined as the length of any single subsequence in the family. Let $t^{(r)}(n)$ denote the largest $k$ such that every permutation of length $n$ contains an $r$-tuplet of length $k$. We prove that $t^{(r)}(n)=O\left(n^{\frac r{2r-1}}\right)$ and $t^{(r)}(n)=\Omega\left( n^{\frac{R}{2R-1}} \right)$, where $R=\binom{2r-1}r$. We conjecture that the upper bound brings the correct order of magnitude of $t^{(r)}(n)$ and support this conjecture by proving that it holds for almost all permutations. Our work generalizes previous studies of the case $r=2$.
Modification of the surface chemistry of TMP with enzymes

SUMMARY: The surface chemistry of thermomechanical pulp (TMP) was modified with specific enzymes and the effect of extensive enzymatic treatments was studied with surface-sensitive spectroscopic methods (ESCA and ToF-SIMS) and by contact angle measurements. Fibers and different types of fines, i.e. fibrils and flakes, were isolated from the enzymatically treated pulps and analyzed separately. Enzymatic treatment had a pronounced effect on surface extractives, especially those found on the surface of fibrils. All the enzymatic treatments decreased the surface coverage of extractives on fibrils, but ToF-SIMS results indicate that only lipase and laccase treatments selectively removed extractives from the surfaces of fibrils and that other treatments only made the film of extractives thinner. Laccase treatment also reduced the content of guaiacylic lignin units on the surface of fibers, fibrils and flakes. All the treatments studied here lowered the contact angle of water on pulp handsheets, probably due to removal of extractives from the surface. ESCA and ToF-SIMS gave complementary information about the surface chemistry of pulp fibers and fines. ToF-SIMS was found to be suitable for studying chemical modifications on the surface of mechanical pulp components. When these two analytical techniques are combined, it must be remembered that their analysis depth is different, that ESCA results are dependent on proper extraction, and that ToF-SIMS is not a quantitative analysis method for organics.
Neural Code Summarization: How Far Are We?

—Source code summaries are important for the comprehension and maintenance of programs. However, there are plenty of programs with missing, outdated, or mismatched summaries. Recently, deep learning techniques have been exploited to automatically generate summaries for given code snippets. To achieve a profound understanding of how far we are from solving this problem, in this paper, we conduct a systematic and in-depth analysis of ﬁve state-of-the-art neural source code summarization models on three widely used datasets. Our evaluation results suggest that: (1) The BLEU metric, which is widely used by existing work for evaluating the performance of the summarization models, has many variants. Ignoring the differences among the BLEU variants could affect the validity of the claimed results. Furthermore, we discover an important, previously unknown bug about BLEU calculation in a commonly-used software package. (2) Code pre-processing choices can have a large impact on the summarization performance, therefore they should not be ignored. (3) Some important characteristics of datasets (corpus size, data splitting method, and duplication ratio) have a signiﬁcant impact on model evaluation. Based on the experimental results, we give some actionable guidelines on more systematic ways for evaluating code summarization and choosing the best method in different scenarios. We also suggest possible future research directions. We believe that our results can be of great help for practitioners and researchers in this interesting area.
Lorentzian vacuum transitions for anisotropic universes

The vacuum transition probabilities for anisotropic universes in the presence of a scalar field potential in the Wentzel-Kramers-Brillouin approximation are studied. We follow the work by Cespedes et al [Phys. Rev. D 104, 026013 (2021)], which discuss these transitions in the isotropic context using the Wheeler-DeWitt equation, the Lorentzian Hamiltonian approach and the thin wall limit. First, we propose a general procedure to adapt their formalism to compute the decay rates for any superspace model. Then we apply it to compute the transition probabilities of an Friedmann-Lemaitre-Robertson-Walker (FLRW) metric with both positive and zero curvature, reproducing in this way one of the results obtained at Cespedes et al. We then proceed to apply the formalism to three anisotropic metrics, namely, Kantowski-Sachs, Bianchi III and biaxial Bianchi IX to compute the rate decays for these three cases. In the process we find that this method involves some conditions which relates the effective number of independent degrees of freedom resulting on all probabilities being described with only two independent variables. For the Bianchi III metric, we find that a general effect of anisotropy is to decrease the transition probability as the degree of anisotropy is increased, having as the isotropic limit the flat FLRW result.
Mutation is all you need

Neural architecture search (NAS) promises to make deep learning accessible to non-experts by automating architecture engineering of deep neural networks. BANANAS is one state-of-the-art NAS method that is embedded within the Bayesian optimization framework. Recent experimental findings have demonstrated the strong performance of BANANAS on the NAS-Bench-101 benchmark being determined by its path encoding and not its choice of surrogate model. We present experimental results suggesting that the performance of BANANAS on the NAS-Bench-301 benchmark is determined by its acquisition function optimizer, which minimally mutates the incumbent.
Automatic Resource Allocation in Business Processes: A Systematic Literature Survey

For delivering products or services to their clients, organizations execute manifold business processes. During such execution, upcoming process tasks need to be allocated to internal resources. Resource allocation is a complex decision-making problem with high impact on the effectiveness and efficiency of processes. A wide range of approaches was developed to support research allocation automatically. This systematic literature survey provides an overview of approaches and categorizes them regarding their resource allocation goals and capabilities, their use of models and data, their algorithmic solutions, and their maturity. Rule-based approaches were identified as dominant, but heuristics and learning approaches also play a relevant role.
Sketching sounds: an exploratory study on sound-shape associations

Sound synthesiser controls typically correspond to technical parameters of signal processing algorithms rather than intuitive sound descriptors that relate to human perception of sound. This makes it difficult to realise sound ideas in a straightforward way. Cross-modal mappings, for example between gestures and sound, have been suggested as a more intuitive control mechanism. A large body of research shows consistency in human associations between sounds and shapes. However, the use of drawings to drive sound synthesis has not been explored to its full extent. This paper presents an exploratory study that asked participants to sketch visual imagery of sounds with a monochromatic digital drawing interface, with the aim to identify different representational approaches and determine whether timbral sound characteristics can be communicated reliably through visual sketches. Results imply that the development of a synthesiser exploiting sound-shape associations is feasible, but a larger and more focused dataset is needed in followup studies.
Coupling effects of water and nitrogen on dry matter accumulation,nitrogen uptake and water-nitrogen use efficiency of cotton under mulched drip irrigation

Field trials were conducted to study the coupling effects of water and nitrogen on nitrogen uptake and dry matter accumulation of cotton at different growth stages,lint yield and water-nitrogen use efficiency.Cottons were grown under three nitrogen application levels and two irrigation levels making six combination treatments.The results show that during the flowering and boll-setting stage,water and nitrogen supply can promote root and shoot growth of cotton.At the boll opening stage,nitrogen supply has no effects on above-ground biomass,while root dry weight is maximum at the 276 kg/ha nitrogen application level under water stress.Root dry weight is reduced with the nitrogen supply,and the above-ground dry weight and nutrient uptake are maximum at the 276 kg/ha nitrogen level under sufficient water supply.From the flowering and boll setting stage to the boll opening stage,too much nitrogen supply can inhibit root and shoot dry matter accumulation.The highest points of lint yield and water-nitrogen use efficiency are different.The lint yield and nitrogen use efficiency are increased,water use efficiency is decreased under the increase of irrigation amount.Under water stress,nitrogen supply has no effects on lint yield,but nitrogen use efficiency is decreased and water use efficiency is increased when the nitrogen level is increased.Under sufficient water supply,the lint yield is maximum at the 276 kg/ha nitrogen level.The water-nitrogen coupling N276W600 obtains maximum lint yield,and relatively high water-nitrogen use efficiency.
Large Deviations for the SSEP with slow boundary: the non-critical case

We prove a large deviations principle for the empirical measure of the one dimensional symmetric simple exclusion process in contact with reservoirs. The dynamics of the reservoirs is slowed down with respect to the dynamics of the system, that is, the rate at which the system exchanges particles with the boundary reservoirs is of order $n^{-\theta}$, where $n$ is number of sites in the system, $\theta$ is a non negative parameter, and the system is taken in the diffusive time scaling. Two regimes are studied here, the subcritical $\theta\in(0,1)$ whose hydrodynamic equation is the heat equation with Dirichlet boundary conditions and the supercritical $\theta\in(1,+\infty)$ whose hydrodynamic equation is the heat equation with Neumann boundary conditions. In the subcritical case $\theta\in(0,1)$, the rate function that we obtain matches the rate function corresponding to the case $\theta=0$ which was derived on previous works (see \cite{blm,flm}), but the challenges we faced here are much trickier. In the supercritical case $\theta\in(1,+\infty)$, the rate function is equal to infinity outside the set of trajectories which preserve the total mass, meaning that, despite the discrete system exchanges particles with the reservoirs, this phenomena has super-exponentially small probability in the diffusive scaling limit.
DiRe Committee : Diversity and Representation Constraints in Multiwinner Elections

The study of fairness in multiwinner elections focuses on settings where candidates have attributes. However, voters may also be divided into predefined populations under one or more attributes. The models that focus on candidate attributes alone may systematically under-represent smaller voter populations. Hence, we develop a model, DiRe Committee Winner Determination (DRCWD), which delineates candidate and voter attributes to select a committee by specifying diversity and representation constraints and a voting rule. We analyze its computational complexity and develop a heuristic algorithm, which finds the winning DiRe committee in under two minutes on 63% of the instances of synthetic datasets and on 100% of instances of real-world datasets. We also present an empirical analysis of feasibility and utility traded-off. Moreover, even when the attributes of candidates and voters coincide, it is important to treat them separately as diversity does not imply representation and vice versa. This is to say that having a female candidate on the committee, for example, is different from having a candidate on the committee who is preferred by the female voters, and who themselves may or may not be female.