Miracle-dz/UniFAR-FTUs huggingface dataset:
The Tail of Late-forming Dwarf Galaxies in ΛCDM

We use a robust analytical model together with a high-resolution hydrodynamical cosmological simulation to demonstrate that in a Lambda cold dark matter (ΛCDM) universe, a small fraction of dwarf galaxies inhabiting dark matter (DM) halos in the mass range 3 × 109 ≲ M 200/M ⊙ ≲ 1010 form unusually late (z < 3) compared to the bulk population of galaxies. These galaxies originate from the interplay between the stochastic growth of DM halos and the existence of a time-dependent DM halo mass below which galaxies do not form. The formation epoch of the simulated late-forming galaxies traces remarkably well the time when their host DM halos first exceeded a nontrivial (but well-understood) time-dependent critical mass, thus making late-forming dwarfs attractive cosmological probes with constraining power over the past growth history of their host halos. The agreement between our model and the simulation results demonstrates that the population of simulated late-forming dwarfs is a robust cosmological outcome and largely independent of the specific galaxy formation model included in the simulations provided: (1) the universe underwent cosmic reionization before zre∼8; (2) star formation proceeds in gas that self-gravitates; and (3) galaxy formation is largely restricted to atomic-cooling halos before z re. The scarcity of massive late-forming dwarfs expected in ΛCDM implies that the great majority of bright, metal-poor, and actively star-forming dwarfs observed in our local universe—the most obvious candidates for these late-forming galaxies—cannot be undergoing their formation for the first time at the present day in a ΛCDM universe.
Rethinking Modern Communication from Semantic Coding to Semantic Communication

Modern communications are usually designed to pursue a higher bit-level precision and fewer bits while transmitting a message. This article rethinks these two major features and introduces the concept and advantage of semantics that characterizes a new kind of semantics-aware communication framework, incorporating both the semantic encoding and the semantic communication problem. After analyzing the underlying defects of existing semantics-aware techniques, we establish a confidence-based distillation mechanism for the joint semantics-noise coding (JSNC) problem and a reinforcement learning (RL) powered semantic communication paradigm that endows a system the ability to convey the semantics instead of pursuing the bit level accuracy. On top of these technical contributions, this work provides a new insight to understand how the semantics are processed and represented in a semantics-aware coding and communication system, and verifies the significant benefits of doing so. Targeted on the next generation's semantics-aware communication, some critical concerns and open challenges, such as the information overhead, semantic security, and implementation cost are also discussed and envisioned.
Learning Cooperation and Online Planning Through Simulation and Graph Convolutional Network

Multi-agent Markov Decision Process (MMDP) has been an effective way of modelling sequential decision making algorithms for multi-agent cooperative environments. A number of algorithms based on centralized and decentralized planning have been developed in this domain. However, dynamically changing environment, coupled with exponential size of the state and joint action space, make it difficult for these algorithms to provide both efficiency and scalability. Recently, Centralized planning algorithm FV-MCTS-MP and decentralized planning algorithm \textit{Alternate maximization with Behavioural Cloning} (ABC) have achieved notable performance in solving MMDPs. However, they are not capable of adapting to dynamically changing environments and accounting for the lack of communication among agents, respectively. Against this background, we introduce a simulation based online planning algorithm, that we call SiCLOP, for multi-agent cooperative environments. Specifically, SiCLOP tailors Monte Carlo Tree Search (MCTS) and uses Coordination Graph (CG) and Graph Neural Network (GCN) to learn cooperation and provides real time solution of a MMDP problem. It also improves scalability through an effective pruning of action space. Additionally, unlike FV-MCTS-MP and ABC, SiCLOP supports transfer learning, which enables learned agents to operate in different environments. We also provide theoretical discussion about the convergence property of our algorithm within the context of multi-agent settings. Finally, our extensive empirical results show that SiCLOP significantly outperforms the state-of-the-art online planning algorithms.
Fine-Grained Opinion Summarization with Minimal Supervision

Opinion summarization aims to profile a target by extracting opinions from multiple documents. Most existing work approaches the task in a semi-supervised manner due to the difficulty of obtaining high-quality annotation from thousands of documents. Among them, some use aspect and sentiment analysis as a proxy for identifying opinions. In this work, we propose a new framework, FineSum, which advances this frontier in three aspects: (1) minimal supervision, where only aspect names and a few aspect/sentiment keywords are available; (2) fine-grained opinion analysis, where sentiment analysis drills down to the sub-aspect level; and (3) phrase-based summarization, where opinion is summarized in the form of phrases. FineSum automatically identifies opinion phrases from the raw corpus, classifies them into different aspects and sentiments, and constructs multiple fine-grained opinion clusters under each aspect/sentiment. Each cluster consists of semantically coherent phrases, expressing uniform opinions towards certain sub-aspect or characteristics (e.g., positive feelings for ``burgers'' in the ``food'' aspect). An opinion-oriented spherical word embedding space is trained to provide weak supervision for the phrase classifier, and phrase clustering is performed using the aspect-aware contextualized embedding generated from the phrase classifier. Both automatic evaluation on the benchmark and quantitative human evaluation validate the effectiveness of our approach.
Prospect of Detecting X-Ray Halos Around Middle-Aged Pulsars with eROSITA

The detection of extendedTeV γ-ray emission (dubbed “TeVhalos”) fromGeminga andMonogempulsars byHAWCcollaboration implies that the halo-like morphologies around middle-aged pulsars may be common. The γ-rays above 10 TeV are thought to arise from inverse Compton (IC) scattering of relativistic electrons/positrons in the pulsar halos off cosmicmicrowave background photons. In the meanwhile, these electrons and positrons can produce X-ray synchrotron emission in the interstellar magnetic field, resulting in a diffuse emission in the X-ray band (namely X-ray halos). Here, we study the prospect of detecting X-ray halos with eROSITA from 10 middle-aged pulsars with characteristic age τc larger than tens of thousands of years in the ATNF pulsar catalog. Assuming a benchmark value (i.e., B = 3 μG) for the magnetic field, most of the X-ray halos are found to be bright enough to be detectable by eROSITA in the energy range of 0.5 − 2 keV during its four-year all-sky survey. Among these pulsar halos, three are supposed to produce X-ray fluxes above the eROSITA sensitivity of the first all-sky survey. Given the good angular resolution and the large field of view, eROSITA is expected to be able to measure the spatial distribution of the X-ray halos from sub-pc scale up to tens of pc scale. The intensity profiles of the X-ray halos are very useful to constrain the magnetic field and the energy-dependence of the diffusion coefficient in the pulsar halos.
Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Review

Scenario-based approaches have been receiving a huge amount of attention in research and engineering of automated driving systems. Due to the complexity and uncertainty of the driving environment, and the complexity of the driving task itself, the number of possible driving scenarios that an ADS or ADAS may encounter is virtually infinite. Therefore it is essential to be able to reason about the identification of scenarios and in particular critical ones that may impose unacceptable risk if not considered. Critical scenarios are particularly important to support design, verification and validation efforts, and as a basis for a safety case. In this paper, we present the results of a systematic literature review in the context of autonomous driving. The main contributions are: (i) introducing a comprehensive taxonomy for critical scenario identification methods; (ii) giving an overview of the state-of-the-art research based on the taxonomy encompassing 86 papers between 2017 and 2020; and (iii) identifying open issues and directions for further research. The provided taxonomy comprises three main perspectives encompassing the problem definition (the why), the solution (the methods to derive scenarios), and the assessment of the established scenarios. In addition, we discuss open research issues considering the perspectives of coverage, practicability, and scenario space explosion.
3D-RETR: End-to-End Single and Multi-View 3D Reconstruction with Transformers

3D reconstruction aims to reconstruct 3D objects from 2D views. Previous works for 3D reconstruction mainly focus on feature matching between views or using CNNs as backbones. Recently, Transformers have been shown effective in multiple applications of computer vision. However, whether or not Transformers can be used for 3D reconstruction is still unclear. In this paper, we fill this gap by proposing 3D-RETR, which is able to perform end-to-end 3D REconstruction with TRansformers. 3D-RETR first uses a pretrained Transformer to extract visual features from 2D input images. 3D-RETR then uses another Transformer Decoder to obtain the voxel features. A CNN Decoder then takes as input the voxel features to obtain the reconstructed objects. 3D-RETR is capable of 3D reconstruction from a single view or multiple views. Experimental results on two datasets show that 3DRETR reaches state-of-the-art performance on 3D reconstruction. Additional ablation study also demonstrates that 3D-DETR benefits from using Transformers.
Centroid Approximation for Bootstrap

Bootstrap is a principled and powerful frequentist statistical tool for uncertainty quantification. Unfortunately, standard bootstrap methods are computationally intensive due to the need of drawing a large i.i. d. bootstrap sample to approximate the ideal bootstrap distribution; this largely hinders their application in large-scale machine learning, especially deep learning problems. In this work, we propose an efficient method to explicitly \emph{optimize} a small set of high quality ``centroid'' points to better approximate the ideal bootstrap distribution. We achieve this by minimizing a simple objective function that is asymptotically equivalent to the Wasserstein distance to the ideal bootstrap distribution. This allows us to provide an accurate estimation of uncertainty with a small number of bootstrap centroids, outperforming the naive i.i. d. sampling approach. Empirically, we show that our method can boost the performance of bootstrap in a variety of applications.
Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs

Q-learning is a popular Reinforcement Learning (RL) algorithm which is widely used in practice with function approximation (Mnih et al., 2015). In contrast, existing theoretical results are pessimistic about Q-learning. For example, (Baird, 1995) shows that Q-learning does not converge even with linear function approximation for linear MDPs. Furthermore, even for tabular MDPs with synchronous updates, Q-learning was shown to have sub-optimal sample complexity (Li et al., 2021;Azar et al., 2013). The goal of this work is to bridge the gap between practical success of Q-learning and the relatively pessimistic theoretical results. The starting point of our work is the observation that in practice, Q-learning is used with two important modifications: (i) training with two networks, called online network and target network simultaneously (online target learning, or OTL) , and (ii) experience replay (ER) (Mnih et al., 2015). While they have been observed to play a significant role in the practical success of Q-learning, a thorough theoretical understanding of how these two modifications improve the convergence behavior of Q-learning has been missing in literature. By carefully combining Q-learning with OTL and reverse experience replay (RER) (a form of experience replay), we present novel methods Q-Rex and Q-RexDaRe (Q-Rex + data reuse). We show that Q-Rex efficiently finds the optimal policy for linear MDPs (or more generally for MDPs with zero inherent Bellman error with linear approximation (ZIBEL)) and provide non-asymptotic bounds on sample complexity -- the first such result for a Q-learning method for this class of MDPs under standard assumptions. Furthermore, we demonstrate that Q-RexDaRe in fact achieves near optimal sample complexity in the tabular setting, improving upon the existing results for vanilla Q-learning.
Discovering and Achieving Goals via World Models

How can artificial agents learn to solve many diverse tasks in complex visual environments in the absence of any supervision? We decompose this question into two problems: discovering new goals and learning to reliably achieve them. We introduce Latent Explorer Achiever (LEXA), a unified solution to these that learns a world model from image inputs and uses it to train an explorer and an achiever policy from imagined rollouts. Unlike prior methods that explore by reaching previously visited states, the explorer plans to discover unseen surprising states through foresight, which are then used as diverse targets for the achiever to practice. After the unsupervised phase, LEXA solves tasks specified as goal images zero-shot without any additional learning. LEXA substantially outperforms previous approaches to unsupervised goal-reaching, both on prior benchmarks and on a new challenging benchmark with a total of 40 test tasks spanning across four standard robotic manipulation and locomotion domains. LEXA further achieves goals that require interacting with multiple objects in sequence. Finally, to demonstrate the scalability and generality of LEXA, we train a single general agent across four distinct environments. Code and videos at https://orybkin.github. io/lexa/
Isotope-selective laser ablation ion-trap loading of 
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The $^{133}\mathrm{Ba}^+$ ion is a promising candidate as a high-fidelity qubit, and the $^{137}\mathrm{Ba}^+$ isotope is promising as a high-fidelity qudit ($d>2$). Barium metal is very reactive, and $^{133}\mathrm{Ba}^+$ is radioactive and can only be sourced in small quantities, so the most commonly used loading method, oven heating, is less suited for barium, and is currently not possible for $^{133}\mathrm{Ba}^+$. Pulsed laser ablation solves both of these problems by utilizing compound barium sources, while also giving some distinct advantages, such as fast loading, less displaced material, and lower heat load near the ion trap. Because of the relatively low abundances of the isotopes of interest, a two-step photoionization technique is used, which gives us the ability to selectively load isotopes. Characterization of the ablation process for our $\mathrm{BaCl}_2$ targets are presented, including observation of neutral and ion ablation-fluence regimes, preparation/conditioning and lifetimes of ablation spots, and plume velocity distributions.We show that using laser ablation on $\mathrm{BaCl}_2$ salt targets with a two-step photoionization method, we can produce and trap barium ions reliably. Further, we demonstrate that with our photoionization method, we can trap $^{137}\mathrm{Ba}^+$ with an enhanced selectivity compared to its natural abundance.
Carbon Neutrality in Data Center

—Data centers are experiencing unprecedented growth as the fourth industrial revolution’s supporting pillars and the engine for the future digitalized world. However, data centers are carbon-intensive enterprises due to their massive energy consumption, and it is estimated that data center industry will account for 8% of global carbon emissions by 2030. Mean-while, both technological and policy instruments for reducing or even neutralizing data center carbon emissions have not been thoroughly investigated, despite the fact that several global cloud providers including Google and Facebook, have pledged to achieve carbon neutrality in their hyperscale data centers. To bridge this gap, this survey paper proposes a roadmap towards carbon-neutral data centers that takes into account both policy instruments and technological methodologies. We begin by presenting the carbon footprint of data centers, as well as some insights into the major sources of carbon emissions. Following that, carbon neutrality plans for major global cloud providers are discussed to summarize current industrial efforts in this direction. In what follows, we introduce the carbon market as a policy instrument to explain how to offset data center carbon emissions in a cost-efﬁcient manner. On the technological front, we propose achieving carbon-neutral data centers by increasing renewable energy penetration, improving energy efﬁciency, and boosting energy circulation simultaneously. A comprehensive review of existing technologies on these three topics is elaborated subsequently. Based on this, a multi-pronged approach towards carbon neutrality is envisioned and a digital twin-powered industrial artiﬁcial intelligence (AI) framework is proposed to make this solution a reality. Furthermore, three key scientiﬁc challenges for putting such a framework in place are discussed. Finally, several applications for this framework are presented to demonstrate its enormous potential.
Dynamic Graph Echo State Networks

Dynamic temporal graphs represent evolving relations between entities, e.g. interactions between social network users or infection spreading. We propose an extension of graph echo state networks for the efficient processing of dynamic temporal graphs, with a sufficient condition for their echo state property, and an experimental analysis of reservoir layout impact. Compared to temporal graph kernels that need to hold the entire history of vertex interactions, our model provides a vector encoding for the dynamic graph that is updated at each time-step without requiring training. Experiments show accuracy comparable to approximate temporal graph kernels on twelve dissemination process classification tasks.
Training Deep Neural Networks with Joint Quantization and Pruning of Weights and Activations

Quantization and pruning are core techniques used to reduce the inference costs of deep neural networks. State-of-the-art quantization techniques are currently applied to both the weights and activations; however, pruning is most often applied to only the weights of the network. In this work, we jointly apply novel uniform quantization and unstructured pruning methods to both the weights and activations of deep neural networks during training. Using our methods, we empirically evaluate the currently accepted prune-then-quantize paradigm across a wide range of computer vision tasks and observe a non-commutative nature when applied to both the weights and activations of deep neural networks. Informed by these observations, we articulate the non-commutativity hypothesis: for a given deep neural network being trained for a specific task, there exists an exact training schedule in which quantization and pruning can be introduced to optimize network performance. We identify that this optimal ordering not only exists, but also varies across discriminative and generative tasks. Using the optimal training schedule within our training framework, we demonstrate increased performance per memory footprint over existing solutions.
Neural-Adaptive Stochastic Attitude Filter on SO(3)

This letter proposes a novel stochastic nonlinear neural-adaptive-based filter on $SO(3)$ for the attitude estimation problem. The proposed filter produces good results given measurements extracted from low-cost sensing units (e.g., IMU or MARG sensor modules). The filter is guaranteed to be almost semi-globally uniformly ultimately bounded in the mean square. In addition to Lie Group formulation, quaternion representation of the proposed filter is provided. The effectiveness of the proposed neural-adaptive filter is tested and evaluated in its discrete form under the conditions of large initialization error and high measurement uncertainties.
Decoherence factor as a convolution: an interplay between a Gaussian and an exponential coherence loss

We identify and investigate the origin and nature of the transition between Gaussian and exponential forms of decoherence: the decoherence factor (that controls the time dependence of the off-diagonal terms of the density matrix expressed in the pointer basis representation) is the convolution of the Fourier transforms of the spectral density and of the overlap (between the eigenstates the environment with and without couplings to the system). Spectral density alone tends to lead to the (approximately) Gaussian decay of coherence while the overlap alone results in a (largely) exponential decay. We show that these two contributions combine as a convolution, their relative importance controlled by the strength of the system-environment coupling. The resulting decoherence factor in the strong and weak coupling limits leads to predominantly Gaussian or exponential decay, respectively, as is demonstrated with two paradigmatic examples of decoherence—a spin-bath model and the quantum Brownian motion.
Semantic network analysis of abstract and concrete word associations

In recent years, a new interest for the use of graph-theory based networks has emerged within the field of cognitive science. This has played a key role in mining the large amount of data generated by word association norms. In the present work, we applied semantic network analyses to explore norms of French word associations for concrete and abstract concepts (Lakhzoum et al., 2021). Graph analyses have shown that the network exhibits high clustering coefficient, sparse density, and small average shortest path length for both the concrete and abstract networks. These characteristics are consistent with a small-world structure. Comparisons between local node statistics and global structural topology showed that abstract and concrete concepts present a similar local connectivity but different overall patterns of structural organisation with concrete concepts presenting an organisation in densely connected communities compared to abstract concepts. These patterns confirm previously acquired knowledge about the dichotomy of abstract and concrete concepts on a larger scale. To the best of our knowledge, this is the first attempt to confirm the generalisability of these properties to the French language and with an emphasis on abstract and concrete concepts.
Orthogonal Transforms for Signals on Directed Graphs

In this paper we consider the problem of defining transforms for signals on directed graphs, with a specific focus on defective graphs where the corresponding graph operator cannot be diagonalized. Our proposed method is based on the Schur decomposition and leads to a series of embedded invariant subspaces for which orthogonal basis are available. As compared to diffusion wavelets, our method is more flexible in the generation of subspaces, but these subspaces can only be approximately orthogonal.