Miracle-dz/UniFAR-FTUs huggingface dataset:
X-RAY/GeV EMISSIONS FROM CRAB-LIKE PULSARS IN THE LMC

We discuss X-ray and gamma-ray emissions from Crab-like pulsars, PSRs J0537-6910 and J0540-6919, in the Large Magellanic Cloud. Fermi-LAT observations have resolved the gamma-ray emissions from these two pulsars and found pulsed emissions from PSR J0540-6919. The total pulsed radiation in the X-ray/gamma-ray energy bands of PSR J0540-6919 is observed with efficiency (in 4π sr), which is about a factor of ten larger than of the Crab pulsar. Although PSR J0537-6910 has the highest spin-down power among currently known pulsars, the efficiency of the observed X-ray emissions is about two orders of magnitude smaller than that of PSR J0540-6919. This paper mainly discusses what causes the difference in the radiation efficiencies of these three energetic Crab-like pulsars. We discuss electron/positron acceleration and high-energy emission processes within the outer gap model. By solving the outer gap structure with the dipole magnetic field, we show that the radiation efficiency decreases as the inclination angle between the magnetic axis and the rotation axis increases. To explain the difference in the pulse profile and in the radiation efficiency, our model suggests that PSR J0540-6919 has an inclination angle much smaller than that of the Crab pulsar (here we assume the inclination angles of both pulsars are ). On the other hand, we speculate that the difference in the radiation efficiencies between PSRs J0537-6910 and J0549-6919 is mainly caused by the difference in the Earth viewing angle, and that we see PSR J0537-6910 with an Earth viewing angle (or ) measured from the spin axis, while we see PSR J0540-6919 with .
Bayesian correction of H(z) data uncertainties

We compile 41 $H(z)$ data from literature and use them to constrain O$\Lambda$CDM and flat $\Lambda$CDM parameters. We show that the available $H(z)$ suffers from uncertainties overestimation and propose a Bayesian method to reduce them. As a result of this method, using $H(z)$ only, we find, in the context of O$\Lambda$CDM, $H_0=69.5\pm2.5\mathrm{\,km\,s^{-1}Mpc^{-1}}$, $\Omega_m=0.242\pm0.036$ and $\Omega_\Lambda=0.68\pm0.14$. In the context of flat $\Lambda$CDM model, we have found $H_0=70.4\pm1.2\mathrm{\,km\,s^{-1}Mpc^{-1}}$ and $\Omega_m=0.256\pm0.014$. This corresponds to an uncertainty reduction of up to 30\% when compared to the uncorrected analysis in both cases.
Label Noise in Adversarial Training: A Novel Perspective to Study Robust Overfitting

We show that label noise exists in adversarial training. Such label noise is due to the mismatch between the true label distribution of adversarial examples and the label inherited from clean examples - the true label distribution is distorted by the adversarial perturbation, but is neglected by the common practice that inherits labels from clean examples. Recognizing label noise sheds insights on the prevalence of robust overfitting in adversarial training, and explains its intriguing dependence on perturbation radius and data quality. Also, our label noise perspective aligns well with our observations of the epoch-wise double descent in adversarial training. Guided by our analyses, we proposed a method to automatically calibrate the label to address the label noise and robust overfitting. Our method achieves consistent performance improvements across various models and datasets without introducing new hyper-parameters or additional tuning.
PREDICTED SPACE MOTIONS FOR HYPERVELOCITY AND RUNAWAY STARS: PROPER MOTIONS AND RADIAL VELOCITIES FOR THE GAIA Era

We predict the distinctive three-dimensional space motions of hypervelocity stars (HVSs) and runaway stars moving in a realistic Galactic potential. For nearby stars with distances less than 10 kpc, unbound stars are rare; proper motions alone rarely isolate bound HVSs and runaways from indigenous halo stars. At large distances of 20–100 kpc, unbound HVSs are much more common than runaways; radial velocities easily distinguish both from indigenous halo stars. Comparisons of the predictions with existing observations are encouraging. Although the models fail to match observations of solar-type HVS candidates from SEGUE, they agree well with data for B-type HVS and runaways from other surveys. Complete samples of g ≲ 20 stars with Gaia should provide clear tests of formation models for HVSs and runaways and will enable accurate probes of the shape of the Galactic potential.
AfroLID: A Neural Language Identification Tool for African Languages

Language identification (LID) is a crucial precursor for NLP, especially for mining web data. Problematically, most of the world’s 7000+ languages today are not covered by LID technologies. We address this pressing issue for Africa by introducing AfroLID, a neural LID toolkit for 517 African languages and varieties. AfroLID exploits a multi-domain web dataset manually curated from across 14 language families utilizing five orthographic systems. When evaluated on our blind Test set, AfroLID achieves 95.89 F_1-score. We also compare AfroLID to five existing LID tools that each cover a small number of African languages, finding it to outperform them on most languages. We further show the utility of AfroLID in the wild by testing it on the acutely under-served Twitter domain. Finally, we offer a number of controlled case studies and perform a linguistically-motivated error analysis that allow us to both showcase AfroLID’s powerful capabilities and limitations
Large Language Models Can Self-Improve

Large Language Models (LLMs) have achieved excellent performances in various tasks. However, fine-tuning an LLM requires extensive supervision. Human, on the other hand, may improve their reasoning abilities by self-thinking without external inputs. In this work, we demonstrate that an LLM is also capable of self-improving with only unlabeled datasets. We use a pre-trained LLM to generate"high-confidence"rationale-augmented answers for unlabeled questions using Chain-of-Thought prompting and self-consistency, and fine-tune the LLM using those self-generated solutions as target outputs. We show that our approach improves the general reasoning ability of a 540B-parameter LLM (74.4%->82.1% on GSM8K, 78.2%->83.0% on DROP, 90.0%->94.4% on OpenBookQA, and 63.4%->67.9% on ANLI-A3) and achieves state-of-the-art-level performance, without any ground truth label. We conduct ablation studies and show that fine-tuning on reasoning is critical for self-improvement.
Spin-wave excitations and superconducting resonant mode in CsxFe2−ySe2

We report neutron inelastic scattering measurements on the normal and superconducting states of single-crystalline Cs0.8Fe1. 9Se2. Consistent with previous measurements on RbxFe2−ySe2, we observe two distinct spin excitation signals: (i) spin-wave excitations characteristic of the block antiferromagnetic order found in insulating AxFe2−ySe2 compounds, and (ii) a resonance-like magnetic peak localized in energy at 11 meV and at an in-plane wave-vector of (0.25,0.5). The resonance peak increases below Tc=27 K, and has a similar absolute intensity to the resonance peaks observed in other Fe-based superconductors. The existence of a magnetic resonance in the spectrum of RbxFe2−ySe2 and now of CsxFe2−ySe2 suggests that this is a common feature of superconductivity in this family. The low-energy spin-wave excitations in Cs0.8Fe1. 9Se2 show no measurable response to superconductivity, consistent with the notion of spatially separate magnetic and superconducting phases.
Design and Validation of a Multi-Arm Robotic Platform for Scientific Exploration

—There are a large number of robotic platforms with two or more arms targeting surgical applications. Despite that, very few groups have employed such platforms for scientiﬁc exploration. Possible applications of a multi-arm platform in scientiﬁc exploration involve the study of the mechanisms of intractable diseases by using organoids (i.e., miniature human organs). The study of organoids requires the preparation of a cranial window which is done by carefully removing an 8 mm patch of the mouse skull. In this work, we present the ﬁrst prototype of the AI robot science platform for scientiﬁc experimentation, its digital twins, and perform validation ex- periments under teleoperation. The experiments showcase the dexterity of the platform by performing peg transfer, gauze cutting, mock experiments using eggs, and the world’s ﬁrst teleoperated drilling for a cranial window. The digital twins and related control software are freely available for noncommercial use at https://AISciencePlatform.github. io.
Query semantic reconstruction for background in few-shot segmentation

Few-shot segmentation (FSS) aims to segment unseen classes using a few annotated samples. Typically, a prototype representing the foreground class is extracted from annotated support image(s) and is matched to features representing each pixel in the query image. However, models learnt in this way are insufficiently discriminatory, and often produce false positives: misclassifying background pixels as foreground. Some FSS methods try to address this issue by using the background in the support image(s) to help identify the background in the query image. However, the backgrounds of these images are often quite distinct, and hence, the support image background information is uninformative. This article proposes a method, QSR, that extracts the background from the query image itself, and as a result is better able to discriminate between foreground and background features in the query image. This is achieved by modifying the training process to associate prototypes with class labels including known classes from the training data and latent classes representing unknown background objects. This class information is then used to extract a background prototype from the query image. To successfully associate prototypes with class labels and extract a background prototype that is capable of predicting a mask for the background regions of the image, the machinery for extracting and using foreground prototypes is induced to become more discriminative between different classes. Experiments achieves state-of-the-art results for both 1-shot and 5-shot FSS on the PASCAL- $$5^{i}$$ 5 i and COCO- $$20^{i}$$ 20 i dataset. As QSR operates only during training, results are produced with no extra computational complexity during testing.
Boundary description of microstates of the two-dimensional black hole

We identify the microstates of the non-supersymmetric, asymptotically flat 2d black hole in the dual c=1 matrix quantum mechanics (MQM). We calculate the partition function of the theory using Hamiltonian methods and reproduce one of two conflicting results found by Kazakov and Tseytlin. We find the entropy by counting states and the energy by approximately solving the Schrödinger equation. The dominant contribution to the partition function in the double-scaling limit is a novel bound state that can be considered an explicit dual of the black hole microstates. This bound state is long-lived and evaporates slowly, exactly like a black hole in asymptotically flat space.
The geometry of a radio pulsar beam

Taxonomy of radio pulsar profiles is mostly based on a system of Ptolemaic artificiality, consisting of separated rings and a core, arbitrarily located at disparate altitudes in the magnetosphere. Diversity of observed profile shapes clearly exceeds the interpretive capability of such conal model. Moreover, bifurcated features observed in pulsar profiles imply a system of fan beams radially extending away from the dipole axis. The bifurcations can be understood as the imprint of the elementary radiation pattern of the long-sought radio emission mechanism, thus identifying the latter. Their size, however, is several times larger than implied by the curvature of magnetic dipole lines. Here I show that the illusion of disconnected rings and the size of bifurcated features can be explained through a natural geometry which combines the properties of both the cone and the fan beam. It is a flaring spiral which makes several revolutions around the dipole axis on its way to leave the magnetosphere. Such geometry is consistent with a stream of outflowing and laterally drifting plasma. The bifurcated components are so wide, because the curvature on such a spiral is larger than that of the dipolar magnetic field, hence they are consistent with the extraordinary mode curvature radiation.
Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transport

Despite recent advances in automated machine learning, model selection is still a complex and computationally intensive process. For Gaussian processes (GPs), selecting the kernel is a crucial task, often done manually by the expert. Additionally, evaluating the model selection criteria for Gaussian processes typically scales cubically in the sample size, rendering kernel search particularly computationally expensive. We propose a novel, efficient search method through a general, structured kernel space. Previous methods solved this task via Bayesian optimization and relied on measuring the distance between GP's directly in function space to construct a kernel-kernel. We present an alternative approach by defining a kernel-kernel over the symbolic representation of the statistical hypothesis that is associated with a kernel. We empirically show that this leads to a computationally more efficient way of searching through a discrete kernel space.
Joint Speech Translation and Named Entity Recognition

Modern automatic translation systems aim at place the human at the center by providing contextual support and knowledge. In this context, a critical task is enriching the output with information regarding the mentioned entities, which is currently achieved processing the generated translation with named entity recognition (NER) and entity linking systems. In light of the recent promising results shown by direct speech translation (ST) models and the known weaknesses of cascades (error propagation and additional latency), in this paper we propose multitask models that jointly perform ST and NER, and compare them with a cascade baseline. The experimental results show that our models significantly outperform the cascade on the NER task (by 0.4-1.0 F1), without degradation in terms of translation quality, and with the same computational efficiency of a plain direct ST model.
Towards Human-centered Explainable AI: User Studies for Model Explanations

Explainable AI (XAI) is widely viewed as a sine qua non for ever-expanding AI research. A better understanding of the needs of XAI users, as well as human-centered evaluations of explainable models are both a necessity and a challenge. In this paper, we explore how HCI and AI researchers conduct user studies in XAI applications based on a systematic literature review. After identifying and thoroughly analyzing 85 core papers with human-based XAI evaluations over the past five years, we categorize them along the measured characteristics of explanatory methods, namely trust, understanding, fairness, usability , and human-AI team performance . Our research shows that XAI is spreading more rapidly in certain application domains, such as recommender systems than in others, but that user evaluations are still rather sparse and incorporate hardly any insights from cognitive or social sciences. Based on a comprehensive discussion of best practices, i.e., common models, design choices, and measures in user studies, we propose practical guidelines on designing and conducting user studies for XAI researchers and practitioners. Lastly, this survey also highlights several open research directions, particularly linking psychological science and human-centered XAI.
Optimal Contextual Bandits with Knapsacks under Realizibility via Regression Oracles

We study the stochastic contextual bandit with knapsacks (CBwK) problem, where each action, taken upon a context, not only leads to a random reward but also costs a random resource consumption in a vector form. The challenge is to maximize the total reward without violating the budget for each resource. We study this problem under a general realizability setting where the expected reward and expected cost are functions of contexts and actions in some given general function classes $\mathcal{F}$ and $\mathcal{G}$, respectively. Existing works on CBwK are restricted to the linear function class since they use UCB-type algorithms, which heavily rely on the linear form and thus are difficult to extend to general function classes. Motivated by online regression oracles that have been successfully applied to contextual bandits, we propose the first universal and optimal algorithmic framework for CBwK by reducing it to online regression. We also establish the lower regret bound to show the optimality of our algorithm for a variety of function classes.
CRT-6D: Fast 6D Object Pose Estimation with Cascaded Refinement Transformers

Learning based 6D object pose estimation methods rely on computing large intermediate pose representations and/or iteratively refining an initial estimation with a slow render-compare pipeline. This paper introduces a novel method we call Cascaded Pose Refinement Transformers, or CRT-6D. We replace the commonly used dense intermediate representation with a sparse set of features sampled from the feature pyramid we call OSKFs(Object Surface Keypoint Features) where each element corresponds to an object keypoint. We employ lightweight deformable transformers and chain them together to iteratively refine proposed poses over the sampled OSKFs. We achieve inference runtimes 2× faster than the closest real-time state of the art methods while supporting up to 21 objects on a single model. We demonstrate the effectiveness of CRT-6D by performing extensive experiments on the LM-O and YCBV datasets. Compared to real-time methods, we achieve state of the art on LM-O and YCB-V, falling slightly behind methods with inference runtimes one order of magnitude higher. The source code is available at: https://github.com/PedroCastro/CRT-6D
Online and Lightweight Kernel-based Approximated Policy Iteration for Dynamic P-norm Linear Adaptive Filtering

This paper introduces a solution to the problem of selecting dynamically (online) the ``optimal'' p-norm to combat outliers in linear adaptive filtering without any knowledge on the probability density function of the outliers. The proposed online and data-driven framework is built on kernel-based reinforcement learning (KBRL). To this end, novel Bellman mappings on reproducing kernel Hilbert spaces (RKHSs) are introduced. These mappings do not require any knowledge on transition probabilities of Markov decision processes, and are nonexpansive with respect to the underlying Hilbertian norm. The fixed-point sets of the proposed Bellman mappings are utilized to build an approximate policy-iteration (API) framework for the problem at hand. To address the ``curse of dimensionality'' in RKHSs, random Fourier features are utilized to bound the computational complexity of the API. Numerical tests on synthetic data for several outlier scenarios demonstrate the superior performance of the proposed API framework over several non-RL and KBRL schemes.
Efficiently Tuned Parameters Are Task Embeddings

Intermediate-task transfer can benefit a wide range of NLP tasks with properly selected source datasets. However, it is computationally infeasible to experiment with all intermediate transfer combinations, making choosing a useful source task a challenging problem. In this paper, we anticipate that task-specific parameters updated in parameter-efficient tuning methods are likely to encode task-specific information. Therefore, such parameters can be predictive for inter-task transferability. Thus, we propose to exploit these efficiently tuned parameters as off-the-shelf task embeddings for the efficient selection of source datasets for intermediate-task transfer. We experiment with 11 text classification tasks and 11 question answering tasks. Experimental results show that our approach consistently outperforms existing inter-task transferability prediction methods while being conceptually simple and computationally efficient. Our analysis also reveals that the ability of efficiently tuned parameters on transferability prediction is disentangled with their in-task performance. This allows us to use parameters from early checkpoints as task embeddings to further improve efficiency.
Learning Robust Dynamics through Variational Sparse Gating

Learning world models from their sensory inputs enables agents to plan for actions by imagining their future outcomes. World models have previously been shown to improve sample-efficiency in simulated environments with few objects, but have not yet been applied successfully to environments with many objects. In environments with many objects, often only a small number of them are moving or interacting at the same time. In this paper, we investigate integrating this inductive bias of sparse interactions into the latent dynamics of world models trained from pixels. First, we introduce Variational Sparse Gating (VSG), a latent dynamics model that updates its feature dimensions sparsely through stochastic binary gates. Moreover, we propose a simplified architecture Simple Variational Sparse Gating (SVSG) that removes the deterministic pathway of previous models, resulting in a fully stochastic transition function that leverages the VSG mechanism. We evaluate the two model architectures in the BringBackShapes (BBS) environment that features a large number of moving objects and partial observability, demonstrating clear improvements over prior models.
Adversarial Permutation Invariant Training for Universal Sound Separation

Universal sound separation consists of separating mixes with arbitrary sounds of different types, and permutation invariant training (PIT) is used to train source agnostic models that do so. In this work, we complement PIT with adversarial losses but find it challenging with the standard formulation used in speech source separation. We overcome this challenge with a novel I-replacement context-based adversarial loss, and by training with multiple discriminators. Our experiments show that by simply improving the loss (keeping the same model and dataset) we obtain a non-negligible improvement of 1.4 dB SI-SNRI in the reverberant FUSS dataset. We also find adversarial PIT to be effective at reducing spectral holes, ubiquitous in mask-based separation models, which highlights the potential relevance of adversarial losses for source separation.