Miracle-dz/UniFAR-FTUs huggingface dataset:
Stellar representation of extremal Wigner-negative spin states

The Majorana stellar representation is used to characterize spin states that have a maximally negative Wigner quasiprobability distribution on a spherical phase space. These maximally Wigner-negative spin states generally exhibit a partial but not high degree of symmetry within their star configurations. In particular, for spin j > 2, maximal constellations do not correspond to a Platonic solid when available and do not follow an obvious geometric pattern as dimension increases. In addition, they are generally different from spin states that maximize other measures of nonclassicality such as anticoherence or geometric entanglement. Random states display on average a relatively high amount of negativity, but the extremal states and those with similar negativity are statistically rare in Hilbert space. We also prove that all spin coherent states of arbitrary dimension have non-zero Wigner negativity. This offers evidence that all pure spin states also have non-zero Wigner negativity. The results can be applied to qubit ensembles exhibiting permutation invariance.
Algorithmic Foundations of Empirical X-risk Minimization

This manuscript introduces a new optimization framework for machine learning and AI, named {\bf empirical X-risk minimization (EXM)}. X-risk is a term introduced to represent a family of compositional measures or objectives, in which each data point is compared with a large number of items explicitly or implicitly for defining a risk function. It includes surrogate objectives of many widely used measures and non-decomposable losses, e.g., AUROC, AUPRC, partial AUROC, NDCG, MAP, precision/recall at top $K$ positions, precision at a certain recall level, listwise losses, p-norm push, top push, global contrastive losses, etc. While these non-decomposable objectives and their optimization algorithms have been studied in the literature of machine learning, computer vision, information retrieval, and etc, optimizing these objectives has encountered some unique challenges for deep learning. In this paper, we present recent rigorous efforts for EXM with a focus on its algorithmic foundations and its applications. We introduce a class of algorithmic techniques for solving EXM with smooth non-convex objectives. We formulate EXM into three special families of non-convex optimization problems belonging to non-convex compositional optimization, non-convex min-max optimization and non-convex bilevel optimization, respectively. For each family of problems, we present some strong baseline algorithms and their complexities, which will motivate further research for improving the existing results. Discussions about the presented results and future studies are given at the end. Efficient algorithms for optimizing a variety of X-risks are implemented in the LibAUC library at \url{www.libauc. org}.
Cross-Domain Detection Transformer Based on Spatial-Aware and Semantic-Aware Token Alignment

Detection transformers such as DETR (Carion et al., 2020) have recently exhibited promising performance for many object detection tasks, but the generalization ability of those methods is still quite limited for cross-domain adaptation scenarios. To address the cross-domain issue, a straightforward method is to perform token alignment with adversarial training in transformers. However, its performance is often unsatisfactory because the tokens in detection transformers are quite diverse and represent different spatial and semantic information. In this paper, we propose a new method for cross-domain detection transformers called spatial-aware and semantic-aware token alignment (SSTA). Specifically, we take advantage of the characteristics of cross-attention as used in the detection transformer and propose spatial-aware token alignment (SpaTA) and semantic-aware token alignment (SemTA) strategies to guide the token alignment across domains. For spatial-aware token alignment, we extract the information from the cross-attention map (CAM) to align the distribution of tokens according to their attention to object queries. For semantic-aware token alignment, we inject the category information into the cross-attention map and construct domain embedding to guide the learning of a multi-class discriminator to model the category relationship and achieve category-level token alignment during the entire adaptation process. We conduct extensive experiments on several widely-used benchmarks, and the results clearly show the effectiveness of our proposed approach over existing state-of-the-art methods.
Graph Machine Learning for Design of High-Octane Fuels

Fuels with high-knock resistance enable modern spark-ignition engines to achieve high efficiency and thus low CO2 emissions. Identification of molecules with desired autoignition properties indicated by a high research octane number and a high octane sensitivity is therefore of great practical relevance and can be supported by computer-aided molecular design (CAMD). Recent developments in the field of graph machine learning (graph-ML) provide novel, promising tools for CAMD. We propose a modular graph-ML CAMD framework that integrates generative graph-ML models with graph neural networks and optimization, enabling the design of molecules with desired ignition properties in a continuous molecular space. In particular, we explore the potential of Bayesian optimization and genetic algorithms in combination with generative graph-ML models. The graph-ML CAMD framework successfully identifies well-established high-octane components. It also suggests new candidates, one of which we experimentally investigate and use to illustrate the need for further auto-ignition training data.
PaGO-LOAM: Robust Ground-Optimized LiDAR Odometry

Numerous researchers have conducted studies to achieve fast and robust ground-optimized LiDAR odometry methods for terrestrial mobile platforms. In particular, ground-optimized LiDAR odometry usually employs ground segmentation as a preprocessing method. This is because most of the points in a 3D point cloud captured by a 3D LiDAR sensor on a terrestrial platform are from the ground. However, the effect of the performance of ground segmentation on LiDAR odometry is still not closely examined. In this paper, a robust ground-optimized LiDAR odometry framework is proposed to facilitate the study to check the effect of ground segmentation on LiDAR SLAM based on the state-of-the-art (SOTA) method.By using our proposed odometry framework, it is easy and straightforward to test whether ground segmentation algorithms help extract well-described features and thus improve SLAM performance. In addition, by leveraging the SOTA ground segmentation method called Patchwork, which shows robust ground segmentation even in complex and uneven urban environments with little performance perturbation, a novel ground-optimized LiDAR odometry is proposed, called PaGO- LOAM. The methods were tested using the KITTI odometry dataset. PaGO-LOAM shows robust and accurate performance compared with the baseline method. Our code is available at https://github.com/url-kaist/AlterGround-LeGO-LOAM
Semantic Room Wireframe Detection from a Single View

Reconstruction of indoor surfaces with limited texture information or with repeated textures, a situation common in walls and ceilings, may be difficult with a monocular Structure from Motion system. We propose a Semantic Room Wireframe Detection task to predict a Semantic Wireframe from a single perspective image. Such predictions may be used with shape priors to estimate the Room Layout and aid reconstruction. To train and test the proposed algorithm we create a new set of annotations from the simulated Structured3D dataset. We show qualitatively that the SRW-Net handles complex room geometries better than previous Room Layout Estimation algorithms while quantitatively out-performing the baseline in non-semantic Wireframe Detection.
Incentivizing Combinatorial Bandit Exploration

Consider a bandit algorithm that recommends actions to self-interested users in a recommendation system. The users are free to choose other actions and need to be incentivized to follow the algorithm's recommendations. While the users prefer to exploit, the algorithm can incentivize them to explore by leveraging the information collected from the previous users. All published work on this problem, known as incentivized exploration, focuses on small, unstructured action sets and mainly targets the case when the users' beliefs are independent across actions. However, realistic exploration problems often feature large, structured action sets and highly correlated beliefs. We focus on a paradigmatic exploration problem with structure: combinatorial semi-bandits. We prove that Thompson Sampling, when applied to combinatorial semi-bandits, is incentive-compatible when initialized with a sufficient number of samples of each arm (where this number is determined in advance by the Bayesian prior). Moreover, we design incentive-compatible algorithms for collecting the initial samples.
SN 2021foa, a transitional event between a Type IIn (SN 2009ip-like)and a Type Ibn supernova

, ABSTRACT We present photometric and spectroscopic data of the unusual interacting supernova (SN) 2021foa. It rose to an absolute magnitude peak of M r = − 18 mag in 20days. The initial light curve decline shows some luminosity ﬂuctuations before a long-lasting ﬂattening. A faint source ( M r ∼ − 14mag) was detected in the weeks preceding the main event, showing a slowly rising luminosity trend. The r -band absolute light curve is very similar to those of SN 2009ip-like events, with a faint and shorter duration brightening (‘Event A’) followed by a much brighter peak (‘Event B’). The early spectra of SN 2021foa show a blue continuum with narrow ( ∼ 400kms − 1 ) H emission lines that, two weeks later, reveal a complex proﬁle, with a narrow P Cygni on top of an intermediate-width ( ∼ 2700kms − 1 ) component. At + 12days, metal lines in emission appear and He i lines become very strong, with He i λ 5876 reaching half of the H α luminosity, much higher than in previous SN 2009ip-like objects. We propose that SN 2021foa is a transitional event between the H-rich SN 2009ip-like SNe and the He-rich Type Ibn SNe.
Amortized backward variational inference in nonlinear state-space models

We consider the problem of state estimation in general state-space models using variational inference. For a generic variational family defined using the same backward decomposition as the actual joint smoothing distribution, we establish for the first time that, under mixing assumptions, the variational approximation of expectations of additive state functionals induces an error which grows at most linearly in the number of observations. This guarantee is consistent with the known upper bounds for the approximation of smoothing distributions using standard Monte Carlo methods. Moreover, we propose an amortized inference framework where a neural network shared over all times steps outputs the parameters of the variational kernels. We also study empirically parametrizations which allow analytical marginalization of the variational distributions, and therefore lead to efficient smoothing algorithms. Significant improvements are made over state-of-the art variational solutions, especially when the generative model depends on a strongly nonlinear and noninjective mixing function.
String Attractors and Infinite Words

The notion of string attractor has been introduced in [Kempa and Prezza, 2018] in the context of Data Compression and it represents a set of positions of a finite word in which all of its factors can be"attracted". The smallest size $\gamma^*$ of a string attractor for a finite word is a lower bound for several repetitiveness measures associated with the most common compression schemes, including BWT-based and LZ-based compressors. The combinatorial properties of the measure $\gamma^*$ have been studied in [Mantaci et al., 2021]. Very recently, a complexity measure, called string attractor profile function, has been introduced for infinite words, by evaluating $\gamma^*$ on each prefix. Such a measure has been studied for automatic sequences and linearly recurrent infinite words [Schaeffer and Shallit, 2021]. In this paper, we study the relationship between such a complexity measure and other well-known combinatorial notions related to repetitiveness in the context of infinite words, such as the factor complexity and the recurrence. Furthermore, we introduce new string attractor-based complexity measures, in which the structure and the distribution of positions in a string attractor of the prefixes of infinite words are considered. We show that such measures provide a finer classification of some infinite families of words.
Efficient Scheduling of Data Augmentation for Deep Reinforcement Learning

In deep reinforcement learning (RL), data augmentation is widely considered as a tool to induce a set of useful priors about semantic consistency and improve sample efficiency and generalization performance. However, even when the prior is useful for generalization, distilling it to RL agent often interferes with RL training and degenerates sample efficiency. Meanwhile, the agent is forgetful of the prior due to the non-stationary nature of RL. These observations suggest two extreme schedules of distillation: (i) over the entire training; or (ii) only at the end. Hence, we devise a stand-alone network distillation method to inject the consistency prior at any time (even after RL), and a simple yet efficient framework to automatically schedule the distillation. Specifically, the proposed framework first focuses on mastering train environments regardless of generalization by adaptively deciding which {\it or no} augmentation to be used for the training. After this, we add the distillation to extract the remaining benefits for generalization from all the augmentations, which requires no additional new samples. In our experiments, we demonstrate the utility of the proposed framework, in particular, that considers postponing the augmentation to the end of RL training.
A Near-Optimal Best-of-Both-Worlds Algorithm for Online Learning with Feedback Graphs

We consider online learning with feedback graphs, a sequential decision-making framework where the learner's feedback is determined by a directed graph over the action set. We present a computationally efficient algorithm for learning in this framework that simultaneously achieves near-optimal regret bounds in both stochastic and adversarial environments. The bound against oblivious adversaries is $\tilde{O} (\sqrt{\alpha T})$, where $T$ is the time horizon and $\alpha$ is the independence number of the feedback graph. The bound against stochastic environments is $O\big( (\ln T)^2 \max_{S\in \mathcal I(G)} \sum_{i \in S} \Delta_i^{-1}\big)$ where $\mathcal I(G)$ is the family of all independent sets in a suitably defined undirected version of the graph and $\Delta_i$ are the suboptimality gaps. The algorithm combines ideas from the EXP3++ algorithm for stochastic and adversarial bandits and the EXP3.G algorithm for feedback graphs with a novel exploration scheme. The scheme, which exploits the structure of the graph to reduce exploration, is key to obtain best-of-both-worlds guarantees with feedback graphs. We also extend our algorithm and results to a setting where the feedback graphs are allowed to change over time.
Logic-Based Ethical Planning (short paper)

In this paper we propose a framework for ethical decision making in the context of planning, with intended application to robotics. We put forward a compact but highly expressive language for ethical planning that combines linear temporal logic with lexicographic preference modelling. This original combination allows us to assess plans both with respect to an agent's values and their desires, introducing the novel concept of the morality level of an agent and moving towards multigoal, multivalue planning. We initiate the study of computational complexity of planning tasks in our setting, and we discuss potential applications to robotics.
Realistic Deep Learning May Not Fit Benignly

Studies on benign overﬁtting provide insights for the success of overparameterized deep learning models. In this work, we examine the benign overﬁtting phenomena in real-world settings. We found that for tasks such as training a ResNet model on ImageNet dataset, the model does not ﬁt benignly. To understand why benign overﬁtting fails in the ImageNet experiment, we analyze previous benign over-ﬁtting models under a more restrictive setup where the number of parameters is not signiﬁcantly larger than the number of data points. Under this mild overpa-rameterization setup, our analysis identiﬁes a phase change: unlike in the heavy overparameterization setting, benign overﬁtting can now fail in the presence of label noise. Our study explains our empirical observations, and naturally leads to a simple technique known as self-training that can boost the model’s generalization performances. Furthermore, our work highlights the importance of understanding implicit bias in underﬁtting regimes as a future direction.
The Size–Mass Relation of Post-starburst Galaxies in the Local Universe

We present a study of the size–mass relation for local post-starburst (PSB) galaxies at z ≲ 0.33 selected from the Sloan Digital Sky Survey Data Release 8. We find that PSB galaxies with stellar mass (M *) at 109 M ☉ < M * < 1012 M ☉ have a galaxy size smaller than or comparable to those of quiescent galaxies (QGs). After controlling redshift and stellar mass, the sizes of PSBs are ∼13% smaller on average than those of QGs; such differences become larger and significant toward the low-M * end, especially at 109.5 M ☉ ≲ M * ≲ 1010.5 M ☉ where PSBs can be on average ∼19% smaller than QGs. By comparing predictions of possible PSB evolutionary pathways from cosmological simulations, we suggest that a fast quenching of star formation following a short-lived starburst event (which might be induced by a major merger) should be the dominant pathway of our PSB sample. Furthermore, by cross-matching with group catalogs, we confirm that local PSBs at M * ≲ 1010 M ☉ are more clustered than more massive ones. PSBs residing in groups are found to be slightly larger in galaxy size and more disk-like compared to field PSBs, which is qualitatively consistent with and thus hints at the environment-driven fast quenching pathway for group PSBs. Taken together, our results support multiple evolutionary pathways for local PSB galaxies: while massive PSBs are thought of as products of fast quenching following a major merger-induced starburst, environment-induced fast quenching should play a role in the evolution of less massive PSBs, especially at M * ≲ 1010 M ☉.
An Atlas of Convection in Main-sequence Stars

Convection is ubiquitous in stars and occurs under many different conditions. Here we explore convection in main-sequence stars through two lenses: dimensionless parameters arising from stellar structure and parameters that emerge from the application of mixing length theory. We first define each quantity in terms familiar to both the 1D stellar evolution community and the hydrodynamics community. We then explore the variation of these quantities across different convection zones, different masses, and different stages of main-sequence evolution. We find immense diversity across stellar convection zones. Convection occurs in thin shells, deep envelopes, and nearly spherical cores; it can be efficient or inefficient, rotationally constrained or not, transsonic or deeply subsonic. This atlas serves as a guide for future theoretical and observational investigations by indicating which regimes of convection are active in a given star, and by describing appropriate model assumptions for numerical simulations.
Open-environment machine learning

Abstract Conventional machine learning studies generally assume close-environment scenarios where important factors of the learning process hold invariant. With the great success of machine learning, nowadays, more and more practical tasks, particularly those involving open-environment scenarios where important factors are subject to change, called open-environment machine learning in this article, are present to the community. Evidently, it is a grand challenge for machine learning turning from close environment to open environment. It becomes even more challenging since, in various big data tasks, data are usually accumulated with time, like streams, while it is hard to train the machine learning model after collecting all data as in conventional studies. This article briefly introduces some advances in this line of research, focusing on techniques concerning emerging new classes, decremental/incremental features, changing data distributions and varied learning objectives, and discusses some theoretical issues.
Metriplectic geometry for gravitational subsystems

In general relativity, it is diﬃcult to localise observables such as energy, angular momentum, or centre of mass in a bounded region. The diﬃculty is that there is dissipation. A self-gravitating system, conﬁned by its own gravity to a bounded region, radiates some of the charges away into the environment. At a formal level, dissipation implies that some diﬀeomorphisms are not Hamiltonian. In fact, there is no Hamiltonian on phase space that would move the region relative to the ﬁelds. Recently, an extension of the covariant phase space has been introduced to resolve the issue. On the extended phase space, the Komar charges are Hamiltonian. They are generators of dressed diﬀeomorphisms . While the construction is sound, the physical signiﬁcance is unclear. We provide a critical review before developing a geometric approach that takes into account dissipation in a novel way. Our approach is based on metriplectic geometry, a framework used in the description of dissipative systems. Instead of the Poisson bracket, we introduce a Leibniz bracket —a sum of a skew-symmetric and a symmetric bracket. The symmetric term accounts for the loss of charge due to radiation. On the metriplectic space, the charges are Hamiltonian, yet they are not conserved under their own ﬂow.
Provably and Practically Efficient Neural Contextual Bandits

We consider the neural contextual bandit problem. In contrast to the existing work which primarily focuses on ReLU neural nets, we consider a general set of smooth activation functions. Under this more general setting, (i) we derive non-asymptotic error bounds on the difference between an overparameterized neural net and its corresponding neural tangent kernel, (ii) we propose an algorithm with a provably sublinear regret bound that is also efficient in the finite regime as demonstrated by empirical studies. The non-asymptotic error bounds may be of broader interest as a tool to establish the relation between the smoothness of the activation functions in neural contextual bandits and the smoothness of the kernels in kernel bandits.