Miracle-dz/UniFAR-FTUs huggingface dataset:
Extending the WILDS Benchmark for Unsupervised Adaptation

Machine learning systems deployed in the wild are often trained on a source distribution but deployed on a diﬀerent target distribution. Unlabeled data can be a powerful point of leverage for mitigating these distribution shifts, as it is frequently much more available than labeled data and can often be obtained from distributions beyond the source distribution as well. However, existing distribution shift benchmarks with unlabeled data do not reﬂect the breadth of scenarios that arise in real-world applications. In this work, we present the Wilds 2.0 update, which extends 8 of the 10 datasets in the Wilds benchmark of distribution shifts to include curated unlabeled data that would be realistically obtainable in deployment. These datasets span a wide range of applications (from histology to wildlife conservation), tasks (classiﬁcation, regression, and detection), and modalities (photos, satellite images, microscope slides, text, molecular graphs). The update maintains consistency with the original Wilds benchmark by using identical labeled training, validation, and test sets, as well as the evaluation metrics. On these datasets, we systematically benchmark state-of-the-art methods that leverage unlabeled data, including domain-invariant, self-training, and self-supervised methods, and show that their success on Wilds is limited. To facilitate method development and evaluation, we provide an open-source package that automates data loading and contains all of the model architectures and methods used in this paper. Code and leaderboards are available at https://wilds.stanford. edu .
A Qualitative Study on the Sources, Impacts, and Mitigation Strategies of Flaky Tests

Test flakiness forms a major testing concern. Flaky tests manifest non-deterministic outcomes that cripple continu-ous integration and lead developers to investigate false alerts. Industrial reports indicate that on a large scale, the accrual of flaky tests breaks the trust in test suites and entails significant computational cost. To alleviate this, practitioners are constrained to identify flaky tests and investigate their impact. To shed light on such mitigation mechanisms, we interview 14 practitioners with the aim to identify (i) the sources of flakiness within the testing ecosystem, (ii) the impacts of flakiness, (iii) the measures adopted by practitioners when addressing flakiness, and (iv) the automation opportunities for these measures. Our analysis shows that, besides the tests and code, flakiness stems from interactions between the system components, the testing infrastructure, and external factors. We also highlight the impact of flakiness on testing practices and product quality and show that the adoption of guidelines together with a stable infrastructure are key measures in mitigating the problem.
Gaussian Process Constraint Learning for Scalable Chance-Constrained Motion Planning from Demonstrations

We propose a method for learning constraints represented as Gaussian processes (GPs) from locally-optimal demonstrations. Our approach uses the Karush-Kuhn-Tucker (KKT) optimality conditions to determine where on the demonstrations the constraint is tight, and a scaling of the constraint gradient at those states. We then train a GP representation of the constraint which is consistent with and which generalizes this information. We further show that the GP uncertainty can be used within a kinodynamic RRT to plan probabilistically-safe trajectories, and that we can exploit the GP structure within the planner to exactly achieve a specified safety probability. We demonstrate our method can learn complex, nonlinear constraints demonstrated on a 5D nonholonomic car, a 12D quadrotor, and a 3-link planar arm, all while requiring minimal prior information on the constraint. Our results suggest the learned GP constraint is accurate, outperforming previous constraint learning methods that require more a priori knowledge.
Torsion Obstructions to Positive Scalar Curvature

We study obstructions to the existence of Riemannian metrics of positive scalar curvature on closed smooth manifolds arising from torsion classes in the integral homology of their fundamental groups. As an application, we construct new examples of manifolds which do not admit positive scalar curvature metrics, but whose Cartesian products admit such metrics.
A Simple and efficient deep Scanpath Prediction

Visual scanpath is the sequence of fixation points that the human gaze travels while observing an image, and its prediction helps in modeling the visual attention of an image. To this end several models were proposed in the literature using complex deep learning architectures and frameworks. Here, we explore the efficiency of using common deep learning architectures, in a simple fully convolutional regressive manner. We experiment how well these models can predict the scanpaths on 2 datasets. We compare with other models using different metrics and show competitive results that sometimes surpass previous complex architectures. We also compare the different leveraged backbone architectures based on their performances on the experiment to deduce which ones are the most suitable for the task.
A Comprehensive Measurement of the Local Value of the Hubble Constant with 1 km s−1 Mpc−1 Uncertainty from the Hubble Space Telescope and the SH0ES Team

We report observations from the Hubble Space Telescope (HST) of Cepheid variables in the host galaxies of 42 Type Ia supernovae (SNe Ia) used to calibrate the Hubble constant (H 0). These include the complete sample of all suitable SNe Ia discovered in the last four decades at redshift z ≤ 0.01, collected and calibrated from ≥1000 HST orbits, more than doubling the sample whose size limits the precision of the direct determination of H 0. The Cepheids are calibrated geometrically from Gaia EDR3 parallaxes, masers in NGC 4258 (here tripling that sample of Cepheids), and detached eclipsing binaries in the Large Magellanic Cloud. All Cepheids in these anchors and SN Ia hosts were measured with the same instrument (WFC3) and filters (F555W, F814W, F160W) to negate zero-point errors. We present multiple verifications of Cepheid photometry and six tests of background determinations that show Cepheid measurements are accurate in the presence of crowded backgrounds. The SNe Ia in these hosts calibrate the magnitude–redshift relation from the revised Pantheon+ compilation, accounting here for covariance between all SN data and with host properties and SN surveys matched throughout to negate systematics. We decrease the uncertainty in the local determination of H 0 to 1 km s−1 Mpc−1 including systematics. We present results for a comprehensive set of nearly 70 analysis variants to explore the sensitivity of H 0 to selections of anchors, SN surveys, redshift ranges, the treatment of Cepheid dust, metallicity, form of the period–luminosity relation, SN color, peculiar-velocity corrections, sample bifurcations, and simultaneous measurement of the expansion history. Our baseline result from the Cepheid–SN Ia sample is H 0 = 73.04 ± 1.04 km s−1 Mpc−1, which includes systematic uncertainties and lies near the median of all analysis variants. We demonstrate consistency with measures from HST of the TRGB between SN Ia hosts and NGC 4258, and include them simultaneously to yield 72.53 ± 0.99 km s−1 Mpc−1. The inclusion of high-redshift SNe Ia yields H 0 = 73.30 ± 1.04 km s−1 Mpc−1 and q 0 = −0.51 ± 0.024. We find a 5σ difference with the prediction of H 0 from Planck cosmic microwave background observations under ΛCDM, with no indication that the discrepancy arises from measurement uncertainties or analysis variations considered to date. The source of this now long-standing discrepancy between direct and cosmological routes to determining H 0 remains unknown.
Improved approximation algorithms for two Euclidean k-Center variants

The $k$-Center problem is one of the most popular clustering problems. After decades of work, the complexity of most of its variants on general metrics is now well understood. Surprisingly, this is not the case for a natural setting that often arises in practice, namely the Euclidean setting, in which the input points are points in $\mathbb{R}^d$, and the distance between them is the standard $\ell_2$ Euclidean distance. In this work, we study two Euclidean $k$-Center variants, the Matroid Center problem on the real line and the Robust Euclidean $k$-Supplier problem, and provide algorithms that improve upon the best approximation guarantees known for these problems. In particular, we present a simple $2.5$-approximation algorithm for the Matroid Center problem on the real line, thus improving upon the $3$-approximation factor algorithm of Chen, Li, Liang, and Wang (2016) that works for general metrics. Moreover, we present a $(1 + \sqrt{3})$-approximation algorithm for the Robust Euclidean $k$-Supplier problem, thus improving upon the state-of-the-art $3$-approximation algorithm for Robust $k$-Supplier on general metrics and matching the best approximation factor known for the non-robust setting by Nagarajan, Schieber and Shachnai (2020).
Probing 
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 anomalies via dimuon tails at a future collider

We investigate the sensitivity of future proton-proton colliders to a contact interaction of the form $1/\Lambda^2 (\bar b_L \gamma_\mu s_L)(\bar \mu_L \gamma^\mu \mu_L)$ as indicated by the long-standing rare $B$-decay anomalies. We include NLO QCD and electroweak effects and employ an optimized binning scheme, and carefully validate our background calculation against ATLAS and CMS data. We find that the FCC-hh with $40$ ab$^{-1}$ of luminosity is able to exclude scales $\Lambda$ up to 26 TeV at $95 \%$ CL, and discover $\Lambda$ up to 20 TeV. While this is not quite enough to exclude or discover the current best-fit value of $39$ TeV, this can in principle be achieved with more luminosity and/or higher energy, as we study quantitatively. Our analysis is conservative in that it assumes only a $\bar b s \mu \mu $ contact interaction.
Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings

We have created a knowledge graph based on major data sources used in ecotoxicological risk assessment. We have applied this knowledge graph to an important task in risk assessment, namely chemical effect prediction. We have evaluated nine knowledge graph embedding models from a selection of geometric, decomposition, and convolutional models on this prediction task. We show that using knowledge graph embeddings can increase the accuracy of effect prediction with neural networks. Furthermore, we have implemented a fine-tuning architecture which adapts the knowledge graph embeddings to the effect prediction task and leads to a better performance. Finally, we evaluate certain characteristics of the knowledge graph embedding models to shed light on the individual model performance.
Learning multiple gaits of quadruped robot using hierarchical reinforcement learning

There is a growing interest in learning a velocity command tracking controller of quadruped robot using reinforcement learning due to its robustness and scalability. However, a single policy, trained end-to-end, usually shows a single gait regardless of the command velocity. This could be a suboptimal solution considering the existence of optimal gait according to the velocity for quadruped animals. In this work, we propose a hierarchical controller for quadruped robot that could generate multiple gaits (i.e. pace, trot, bound) while tracking velocity command. Our controller is composed of two policies, each working as a central pattern generator and local feedback controller, and trained with hierarchical reinforcement learning. Experiment results show 1) the existence of optimal gait for specific velocity range 2) the efficiency of our hierarchical controller compared to a controller composed of a single policy, which usually shows a single gait. Codes are publicly available.
Building Quantum Field Theories Out of Neurons

An approach to field theory is studied in which fields are comprised of N constituent random neurons. Gaussian theories arise in the infinite-N limit when neurons are independently distributed, via the Central Limit Theorem, while interactions arise due to finite-N effects or non-independently distributed neurons. Euclidean-invariant ensembles of neurons are engineered, with tunable twopoint function, yielding families of Euclidean-invariant field theories. Some Gaussian, Euclidean invariant theories are reflection positive, which allows for analytic continuation to a Lorentz-invariant quantum field theory. Examples are presented that yield dual theories at infinite-N , but have different symmetries at finite-N . Landscapes of classical field configurations are determined by local maxima of parameter distributions. Predictions arise from mixed field-neuron correlators. NearGaussianity is exhibited at large-N , potentially explaining a feature of field theories in Nature.
Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation

We present Neural Descriptor Fields (NDFs), an object representation that encodes both points and relative poses between an object and a target (such as a robot gripper or a rack used for hanging) via category-level descriptors. We employ this representation for object manipulation, where given a task demonstration, we want to repeat the same task on a new object instance from the same category. We propose to achieve this objective by searching (via optimization) for the pose whose descriptor matches that observed in the demonstration. NDFs are conveniently trained in a self-supervised fashion via a 3D auto-encoding task that does not rely on expert-labeled keypoints. Further, NDFs are SE(3)-equivariant, guaranteeing performance that generalizes across all possible 3D object translations and rotations. We demonstrate learning of manipulation tasks from few (∼5-10) demonstrations both in simulation and on a real robot. Our performance generalizes across both object instances and 6-DoF object poses, and significantly outperforms a recent baseline that relies on 2D descriptors. Project website: https://yilundu.github. io/ndf/
A Cross-laboratory Comparison Study of Titan Haze Analogs: Surface Energy

In Titan’s nitrogen-methane atmosphere, photochemistry leads to the production of complex organic particles, forming Titan’s thick haze layers. Laboratory-produced aerosol analogs, or “tholins,” are produced in a number of laboratories; however, most previous studies have investigated analogs produced by only one laboratory rather than a systematic, comparative analysis. In this study, we performed a comparative study of an important material property, the surface energy, of seven tholin samples produced in three independent laboratories under a broad range of experimental conditions, and we explored their commonalities and differences. All seven tholin samples are found to have high surface energies and are therefore highly cohesive. Thus, if the surface sediments on Titan are similar to tholins, future missions such as Dragonfly will likely encounter sticky sediments. We also identified a commonality between all the tholin samples: a high dispersive (nonpolar) surface energy component of at least 30 mJ m−2. This common property could be shared by the actual haze particles on Titan as well. Given that the most abundant species interacting with the haze on Titan (methane, ethane, and nitrogen) are nonpolar in nature, the dispersive surface energy component of the haze particles could be a determinant factor in condensate−haze and haze−lake liquid interactions on Titan. With this common trait of tholin samples, we confirmed the findings of a previous study by Yu et al. that haze particles are likely good cloud condensation nuclei for methane and ethane clouds and would likely be completely wetted by the hydrocarbon lakes on Titan.
Experimental Demonstration of Neuromorphic Network with STT MTJ Synapses

We present the first experimental demonstration of a neuromorphic network with magnetic tunnel junction (MTJ) synapses, which performs image recognition via vector-matrix multiplication. We also simulate a large MTJ network performing MNIST handwritten digit recognition, demonstrating that MTJ crossbars can match memristor accuracy while providing increased precision, stability, and endurance.
From Good to Best: Two-Stage Training for Cross-lingual Machine Reading Comprehension

Cross-lingual Machine Reading Comprehension (xMRC) is a challenging task due to the lack of training data in low-resource languages. Recent approaches use training data only in a resource-rich language (such as English) to fine-tune large-scale cross-lingual pre-trained language models, which transfer knowledge from resource-rich languages (source) to low-resource languages (target). Due to the big difference between languages, the model fine-tuned only by the source language may not perform well for target languages. In our study, we make an interesting observation that while the top 1 result predicted by the previous approaches may often fail to hit the ground-truth answer, there are still good chances for the correct answer to be contained in the set of top k predicted results. Intuitively, the previous approaches have empowered the model certain level of capability to roughly distinguish good answers from bad ones. However, without sufficient training data, it is not powerful enough to capture the nuances between the accurate answer and those approximate ones. Based on this observation, we develop a two-stage approach to enhance the model performance. The first stage targets at recall; we design a hard-learning (HL) algorithm to maximize the likelihood that the top k predictions contain the accurate answer. The second stage focuses on precision, where an answer-aware contrastive learning (AA-CL) mechanism is developed to learn the minute difference between the accurate answer and other candidates. Extensive experiments show that our model significantly outperforms strong baselines on two cross-lingual MRC benchmark datasets.
A Wide Planetary Mass Companion Discovered through the Citizen Science Project Backyard Worlds: Planet 9

Through the Backyard Worlds: Planet 9 citizen science project we discovered a late-type L dwarf co-moving with the young K0 star BD+60 1417 at a projected separation of 37″ or 1662 au. The secondary—CWISER J124332.12+600126.2 (W1243)—is detected in both the CatWISE2020 and 2MASS reject tables. The photometric distance and CatWISE proper motion both match that of the primary within ∼1σ and our estimates for a chance alignment yield a zero probability. Follow-up near-infrared spectroscopy reveals W1243 to be a very red 2MASS (J–K s = 2.72), low surface gravity source that we classify as L6–L8γ. Its spectral morphology strongly resembles that of confirmed late-type L dwarfs in 10–150 Myr moving groups as well as that of planetary mass companions. The position on near- and mid-infrared color–magnitude diagrams indicates the source is redder and fainter than the field sequence, a telltale sign of an object with thick clouds and a complex atmosphere. For the primary we obtained new optical spectroscopy and analyzed all available literature information for youth indicators. We conclude that the Li i abundance, its loci on color–magnitude and color–color diagrams, and the rotation rate revealed in multiple TESS sectors are all consistent with an age of 50–150 Myr. Using our re-evaluated age of the primary and the Gaia parallax, along with the photometry and spectrum for W1243, we find T eff = 1303 ± 31 K, log g = 4.3 ± 0.17 cm s−2, and a mass of 15 ± 5 M Jup. We find a physical separation of ∼1662 au and a mass ratio of ∼0.01 for this system. Placing it in the context of the diverse collection of binary stars, brown dwarfs, and planetary companions, the BD+60 1417 system falls in a sparsely sampled area where the formation pathway is difficult to assess.
Forward-modelling the luminosity, distance, and size distributions of the milky way satellites

We use GRUMPY, a simple regulator-type model for dwarf galaxy formation and evolution, to forward model the dwarf galaxy satellite population of the Milky Way (MW) using the Caterpillar zoom-in simulation suite. We show that luminosity and distance distributions of the model satellites are consistent with the distributions measured in the DES, PS1 and SDSS surveys, even without including a model for the orphan galaxies. We also show that our model for dwarf galaxy sizes can simultaneously reproduce the observed distribution of stellar half-mass radii, r1/2, of the MW satellites and the overall r1/2 − M⋆ relation exhibited by observed dwarf galaxies. The model predicts that some of the observed faint stellar systems with r1/2 < 10 pc are ultra-faint dwarf galaxies. Scaling of the stellar mass M⋆ and peak halo mass Mpeak for the model satellites is not described by a power law, but has a clear flattening of M⋆ − Mpeak scaling at Mpeak < 108 M⊙ imprinted by reionization. As a result, the fraction of low mass haloes (Mpeak < 108 M⊙) hosting galaxies with MV < 0 is predicted to be 50 per cent at Mpeak ∼ 3.6 × 107 M⊙. We find that such high fraction at that halo mass helps to reproduce the number of dwarf galaxies discovered recently in the HSC-SSP survey. Using the model we forecast that there should be the total of $440^{+201}_{-147}$ (68 per cent confidence interval) MW satellites with MV < 0 and r1/2 > 10 pc within 300 kpc and make specific predictions for the HSC-SSP, DELVE-WIDE and LSST surveys.
Revisiting Global Statistics Aggregation for Improving Image Restoration

Global spatial statistics, which are aggregated along entire spatial dimensions, are widely used in top-performance image restorers. For example, mean, variance in Instance Nor-malization (IN) which is adopted by HINet, and global average pooling (i.e. mean) in Squeeze and Excitation (SE) which is applied to MPRNet. This paper ﬁrst shows that statistics aggregated on the patches-based/entire-image-based feature in the training/testing phase respectively may distribute very differently and lead to performance degradation in image re-storers. It has been widely overlooked by previous works. To solve this issue, we propose a simple approach, Test-time Local Statistics Converter (TLSC), that replaces the region of statistics aggregation operation from global to local, only in the test time. Without retraining or ﬁnetuning, our approach signiﬁcantly improves the image restorer’s performance. In particular, by extending SE with TLSC to the state-of-the-art models, MPRNet boost by 0.65 dB in PSNR on GoPro dataset, achieves 33.31 dB, exceeds the previous best result 0.6 dB. In addition, we simply apply TLSC to the high-level vision task, i.e. semantic segmentation, and achieves competitive results. Extensive quantity and quality experiments are conducted to demonstrate TLSC solves the issue with marginal costs while signiﬁcant gain. The code is available at https://github.com/megvii-research/tlsc.
The Perils of Being Unhinged: On the Accuracy of Classifiers Minimizing a Noise-Robust Convex Loss

Abstract van Rooyen, Menon, and Williamson (2015) introduced a notion of convex loss functions being robust to random classification noise and established that the “unhinged” loss function is robust in this sense. In this letter, we study the accuracy of binary classifiers obtained by minimizing the unhinged loss and observe that even for simple linearly separable data distributions, minimizing the unhinged loss may only yield a binary classifier with accuracy no better than random guessing.