Miracle-dz/UniFAR-FTUs huggingface dataset:
{\pi}-{\pi} Interaction-facilitated formation of interwoven trimeric cage-catenanes with topological chirality

Catenanes as interlocked molecules with a nonplanar graph have gained increasing attention for their unique features such as topological chirality. To date, the majority of research in this field has been focusing on catenanes comprising monocyclic rings. Due to the lack of rational synthetic strategy, catenanes of cage-like monomers are hardly accessible. Here we report on the construction of an interwoven trimeric catenane that is composed of achiral organic cages, which exhibits topological chirality. Our rational design begins with a pure mathematical analysis, revealing that the formation probability of the interwoven trimeric catenane surpasses that of its chain-like analogue by 20%; while driven by efficient template effect provided by strong {\pi}-{\pi} stacking of aromatic panels, the interwoven structure emerges as the dominant species, almost ruling out the formation of the chain-like isomer. Its topological chirality is unambiguously unravelled by chiral-HPLC, CD spectroscopy and X-ray diffraction. Our probability analysis-aided rational design strategy would pave a new venue for the efficient synthesis of topologically sophisticated structures in one pot.
Joint Deep Image Restoration and Unsupervised Quality Assessment

Deep learning techniques have revolutionized the fields of image restoration and image quality assessment in recent years. While image restoration methods typically utilize synthetically distorted training data for training, deep quality assessment models often require expensive labeled subjective data. However, recent studies have shown that activations of deep neural networks trained for visual modeling tasks can also be used for perceptual quality assessment of images. Following this intuition, we propose a novel attention-based convolutional neural network capable of simultaneously performing both image restoration and quality assessment. We achieve this by training a JPEG deblocking network augmented with"quality attention"maps and demonstrating state-of-the-art deblocking accuracy, achieving a high correlation of predicted quality with human opinion scores.
High-contrast JWST-MIRI Spectroscopy of Planet-forming Disks for the JDISC Survey

The JWST Disk Infrared Spectral Chemistry Survey (JDISCS) aims to understand the evolution of the chemistry of inner protoplanetary disks using the Mid-InfraRed Instrument (MIRI) on the James Webb Space Telescope (JWST). With a growing sample of >30 disks, the survey implements a custom method to calibrate the MIRI Medium Resolution Spectrometer (MRS) to contrasts of better than 1:300 across its 4.9–28 μm spectral range. This is achieved using observations of Themis family asteroids as precise empirical reference sources. The high spectral contrast enables precise retrievals of physical parameters, searches for rare molecular species and isotopologues, and constraints on the inventories of carbon- and nitrogen-bearing species. JDISCS also offers significant improvements to the MRS wavelength and resolving power calibration. We describe the JDISCS calibrated data and demonstrate their quality using observations of the disk around the solar-mass young star FZ Tau. The FZ Tau MIRI spectrum is dominated by strong emission from warm water vapor. We show that the water and CO line emission originates from the disk surface and traces a range of gas temperatures of ∼500–1500 K. We retrieve parameters for the observed CO and H2O lines and show that they are consistent with a radial distribution represented by two temperature components. A high water abundance of n(H2O) ∼ 10−4 fills the disk surface at least out to the 350 K isotherm at 1.5 au. We search the FZ Tau environs for extended emission, detecting a large (radius of ∼300 au) ring of emission from H2 gas surrounding FZ Tau, and discuss its origin.
An Investigation of Time Reversal Symmetry in Reinforcement Learning

One of the fundamental challenges associated with reinforcement learning (RL) is that collecting sufficient data can be both time-consuming and expensive. In this paper, we formalize a concept of time reversal symmetry in a Markov decision process (MDP), which builds upon the established structure of dynamically reversible Markov chains (DRMCs) and time-reversibility in classical physics. Specifically, we investigate the utility of this concept in reducing the sample complexity of reinforcement learning. We observe that utilizing the structure of time reversal in an MDP allows every environment transition experienced by an agent to be transformed into a feasible reverse-time transition, effectively doubling the number of experiences in the environment. To test the usefulness of this newly synthesized data, we develop a novel approach called time symmetric data augmentation (TSDA) and investigate its application in both proprioceptive and pixel-based state within the realm of off-policy, model-free RL. Empirical evaluations showcase how these synthetic transitions can enhance the sample efficiency of RL agents in time reversible scenarios without friction or contact. We also test this method in more realistic environments where these assumptions are not globally satisfied. We find that TSDA can significantly degrade sample efficiency and policy performance, but can also improve sample efficiency under the right conditions. Ultimately we conclude that time symmetry shows promise in enhancing the sample efficiency of reinforcement learning and provide guidance when the environment and reward structures are of an appropriate form for TSDA to be employed effectively.
High-spectral-resolution Observations of the Optical Filamentary Nebula Surrounding NGC 1275

We present new high-spectral-resolution observations (R = λ/Δλ = 7000) of the filamentary nebula surrounding NGC 1275, the central galaxy of the Perseus cluster. These observations have been obtained with SITELLE, an imaging Fourier transform spectrometer installed on the Canada–France–Hawai Telescope with a field of view of 11 ′ × 11 ′ , encapsulating the entire filamentary structure of ionized gas despite its large size of 80 kpc × 50 kpc. Here, we present renewed fluxes, velocities, and velocity dispersion maps that show in great detail the kinematics of the optical nebula at [S ii] λ6716, [S ii] λ6731, [N ii] λ6584, Hα (6563 Å), and [N ii] λ6548. These maps reveal the existence of a bright flattened disk-shaped structure in the core extending to r ∼10 kpc and dominated by a chaotic velocity field. This structure is located in the wake of X-ray cavities and characterized by a high mean velocity dispersion of 134 km s−1. The disk-shaped structure is surrounded by an extended array of filaments spread out to r ∼ 50 kpc that are 10 times fainter in flux, remarkably quiescent, and have a uniform mean velocity dispersion of 44 km s−1. This stability is puzzling given that the cluster core exhibits several energetic phenomena. Based on these results, we argue that there are two mechanisms that form multiphase gas in clusters of galaxies: a first triggered in the wake of X-ray cavities leading to more turbulent multiphase gas and a second, distinct mechanism, that is gentle and leads to large-scale multiphase gas spreading throughout the core.
Reexamining aspects of spacetime-symmetry breaking with CMB polarization

The linear polarization of the Cosmic Microwave Background (CMB) is highly sensitive to parity-violating physics at the surface of last scattering, which might cause mixing of E and B modes, an effect known as {\it cosmic birefringence}. This has until recently been problematic to detect due to its degeneracy with the instrument polarization miscalibration angle. Recently, a possible detection of a non-zero cosmic-birefringence angle was reported at $\beta={0.35^\circ}\pm 0.14^\circ$, where the miscalibration angle was simultaneously determined and subtracted from the analysis. Starting from this claim, we exploit a simple map of $\beta$ to the coupling constant of a parity-violating term in a generic effective-field theory for Lorentz and CPT violation. We show that the reported constraint on $\beta$ is consistent with current one-sided upper bounds from CMB studies of spacetime-symmetry breaking, and we discuss the implications and interpretation of this detection.
Bulk-measurement-induced boundary phase transition in toric code and gauge Higgs model

Study of boundary phase transition in toric code under cylinder geometry via bulk projective measurement is reported. As the frequency of local measurement for bulk qubits is increased, spin-glass type long-range order on the boundaries emerges indicating spontaneous-symmetry breaking (SSB) of $Z_2$ symmetry. From the lattice-gauge-theory viewpoint, this SSB is a signal of a transition to Higgs phase with symmetry protected topological order. We numerically elucidate the properties of this phase transition in detail, especially its criticality, and give a physical picture using non-local gauge-invariant symmetry operators. Phase transition in the bulk is also studied and its relationship to the boundary transition is discussed.
Improving Lane Detection Generalization: A Novel Framework using HD Maps for Boosting Diversity

Lane detection is a vital task for vehicles to navigate and localize their position on the road. To ensure reliable results, lane detection algorithms must have robust generalization performance in various road environments. However, despite the significant performance improvement of deep learning-based lane detection algorithms, their generalization performance in response to changes in road environments still falls short of expectations. In this paper, we present a novel framework for single-source domain generalization (SSDG) in lane detection. By decomposing data into lane structures and surroundings, we enhance diversity using High-Definition (HD) maps and generative models. Rather than expanding data volume, we strategically select a core subset of data, maximizing diversity and optimizing performance. Our extensive experiments demonstrate that our framework enhances the generalization performance of lane detection, comparable to the domain adaptation-based method.
Influence Scores at Scale for Efficient Language Data Sampling

Modern ML systems ingest data aggregated from diverse sources, such as synthetic, human-annotated, and live customer traffic. Understanding \textit{which} examples are important to the performance of a learning algorithm is crucial for efficient model training. Recently, a growing body of literature has given rise to various"influence scores,"which use training artifacts such as model confidence or checkpointed gradients to identify important subsets of data. However, these methods have primarily been developed in computer vision settings, and it remains unclear how well they generalize to language-based tasks using pretrained models. In this paper, we explore the applicability of influence scores in language classification tasks. We evaluate a diverse subset of these scores on the SNLI dataset by quantifying accuracy changes in response to pruning training data through random and influence-score-based sampling. We then stress-test one of the scores --"variance of gradients"(VoG) from Agarwal et al. (2022) -- in an NLU model stack that was exposed to dynamic user speech patterns in a voice assistant type of setting. Our experiments demonstrate that in many cases, encoder-based language models can be finetuned on roughly 50% of the original data without degradation in performance metrics. Along the way, we summarize lessons learned from applying out-of-the-box implementations of influence scores, quantify the effects of noisy and class-imbalanced data, and offer recommendations on score-based sampling for better accuracy and training efficiency.
Surf-D: Generating High-Quality Surfaces of Arbitrary Topologies Using Diffusion Models

We present Surf-D, a novel method for generating high-quality 3D shapes as Surfaces with arbitrary topologies using Diffusion models. Previous methods explored shape generation with different representations and they suffer from limited topologies and poor geometry details. To generate high-quality surfaces of arbitrary topologies, we use the Unsigned Distance Field (UDF) as our surface representation to accommodate arbitrary topologies. Furthermore, we propose a new pipeline that employs a point-based AutoEncoder to learn a compact and continuous latent space for accurately encoding UDF and support high-resolution mesh extraction. We further show that our new pipeline significantly outperforms the prior approaches to learning the distance fields, such as the grid-based AutoEncoder, which is not scalable and incapable of learning accurate UDF. In addition, we adopt a curriculum learning strategy to efficiently embed various surfaces. With the pretrained shape latent space, we employ a latent diffusion model to acquire the distribution of various shapes. Extensive experiments are presented on using Surf-D for unconditional generation, category conditional generation, image conditional generation, and text-to-shape tasks. The experiments demonstrate the superior performance of Surf-D in shape generation across multiple modalities as conditions. Visit our project page at https://yzmblog.github. io/projects/SurfD/.
Bounds on the Pythagoras number and indecomposables in biquadratic fields

We show that for all real biquadratic fields not containing $\sqrt{2}$, $\sqrt{3}$, $\sqrt{5}$, $\sqrt{6}$, $\sqrt{7},$ and $\sqrt{13}$, the Pythagoras number of the ring of algebraic integers is at least $6$. We will also provide an upper bound on the norm and the minimal (codifferent) trace of additively indecomposable integers in some families of these fields.
Entanglement Hamiltonian of a nonrelativistic Fermi gas

We study the entanglement Hamiltonian for a spherical domain in the ground state of a nonrelativistic free-fermion gas in arbitrary dimensions. Decomposed into a set of radial entanglement Hamiltonians, we show that the entanglement spectrum in each sector is identical to that of a hopping chain in a linear potential, with the angular momentum playing the role of the subsystem boundary. Furthermore, the eigenfunctions follow from a commuting differential operator that has exactly the form predicted by conformal field theory. Rescaled by the radial Fermi velocity, this operator gives a perfect approximation of the entanglement Hamiltonian, except for large angular momenta that belong to the edge regime in the analogous gradient chain. One thus finds that the conformal field theory result becomes asymptotically exact only in one dimension.
Semantic Generative Augmentations for Few-Shot Counting

With the availability of powerful text-to-image diffusion models, recent works have explored the use of synthetic data to improve image classification performances. These works show that it can effectively augment or even replace real data. In this work, we investigate how synthetic data can benefit few-shot class-agnostic counting. This requires to generate images that correspond to a given input number of objects. However, text-to-image models struggle to grasp the notion of count. We propose to rely on a double conditioning of Stable Diffusion with both a prompt and a density map in order to augment a training dataset for few-shot counting. Due to the small dataset size, the fine-tuned model tends to generate images close to the training images. We propose to enhance the diversity of synthesized images by exchanging captions between images thus creating unseen configurations of object types and spatial layout. Our experiments show that our diversified generation strategy significantly improves the counting accuracy of two recent and performing few-shot counting models on FSC147 and CARPK.
Increasing Transparency of Reinforcement Learning using Shielding for Human Preferences and Explanations

The adoption of Reinforcement Learning (RL) in several human-centred applications provides robots with autonomous decision-making capabilities and adaptability based on the observations of the operating environment. In such scenarios, however, the learning process can make robots' behaviours unclear and unpredictable to humans, thus preventing a smooth and effective Human-Robot Interaction (HRI). As a consequence, it becomes crucial to avoid robots performing actions that are unclear to the user. In this work, we investigate whether including human preferences in RL (concerning the actions the robot performs during learning) improves the transparency of a robot's behaviours. For this purpose, a shielding mechanism is included in the RL algorithm to include human preferences and to monitor the learning agent's decisions. We carried out a within-subjects study involving 26 participants to evaluate the robot's transparency in terms of Legibility, Predictability, and Expectability in different settings. Results indicate that considering human preferences during learning improves Legibility with respect to providing only Explanations, and combining human preferences with explanations elucidating the rationale behind the robot's decisions further amplifies transparency. Results also confirm that an increase in transparency leads to an increase in the safety, comfort, and reliability of the robot. These findings show the importance of transparency during learning and suggest a paradigm for robotic applications with human in the loop.
The homotopy decomposition of the suspension of a non-simply-connected $5$-manifold

In this paper we determine the homotopy types of the reduced suspension space of certain connected orientable closed smooth $5$-manifolds. As applications, we compute the reduced $K$-groups of $M$ and show that the suspension map between the third cohomotopy set $\pi^3(M)$ and the fourth cohomotopy set $\pi^4(\Sigma M)$ is a bijection.
Learning how to find targets in the micro-world: the case of intermittent active Brownian particles

Finding the best strategy to minimize the time needed to find a given target is a crucial task both in nature and in reaching decisive technological advances. By considering learning agents able to switch their dynamics between standard and active Brownian motion, here we focus on developing effective target-search behavioral policies for microswimmers navigating a homogeneous environment and searching for targets of unknown position. We exploit projective simulation, a reinforcement learning algorithm, to acquire an efficient stochastic policy represented by the probability of switching the phase, i.e. the navigation mode, in response to the type and the duration of the current phase. Our findings reveal that the target-search efficiency increases with the particle's self-propulsion during the active phase and that, while the optimal duration of the passive case decreases monotonically with the activity, the optimal duration of the active phase displays a non-monotonic behavior.
SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution

Owe to the powerful generative priors, the pre-trained text-to-image (T2I) diffusion models have become increasingly popular in solving the real-world image super-resolution problem. However, as a consequence of the heavy quality degradation of input low-resolution (LR) images, the destruction of local structures can lead to ambiguous image semantics. As a result, the content of reproduced high-resolution image may have semantic errors, deteriorating the super-resolution performance. To address this issue, we present a semantics-aware approach to better preserve the semantic fidelity of generative real-world image super-resolution. First, we train a degradation-aware prompt extractor, which can generate accurate soft and hard semantic prompts even under strong degradation. The hard semantic prompts refer to the image tags, aiming to enhance the local perception ability of the T2I model, while the soft semantic prompts compensate for the hard ones to provide additional representation information. These semantic prompts can encourage the T2I model to generate detailed and semantically accurate results. Furthermore, during the inference process, we integrate the LR images into the initial sampling noise to mitigate the diffusion model's tendency to generate excessive random details. The experiments show that our method can reproduce more realistic image details and hold better the semantics.
Graph Pre-training and Prompt Learning for Recommendation

GNN-based recommenders have excelled in modeling intricate user-item interactions through multi-hop message passing. However, existing methods often overlook the dynamic nature of evolving user-item interactions, which impedes the adaption to changing user preferences and distribution shifts in newly arriving data. Thus, their scalability and performances in real-world dynamic environments are limited. In this study, we propose GraphPro, a framework that incorporates parameter-efficient and dynamic graph pre-training with prompt learning. This novel combination empowers GNNs to effectively capture both long-term user preferences and short-term behavior dynamics, enabling the delivery of accurate and timely recommendations. Our GraphPro framework addresses the challenge of evolving user preferences by seamlessly integrating a temporal prompt mechanism and a graph-structural prompt learning mechanism into the pre-trained GNN model. The temporal prompt mechanism encodes time information on user-item interaction, allowing the model to naturally capture temporal context, while the graph-structural prompt learning mechanism enables the transfer of pre-trained knowledge to adapt to behavior dynamics without the need for continuous incremental training. We further bring in a dynamic evaluation setting for recommendation to mimic real-world dynamic scenarios and bridge the offline-online gap to a better level. Our extensive experiments including a large-scale industrial deployment showcases the lightweight plug-in scalability of our GraphPro when integrated with various state-of-the-art recommenders, emphasizing the advantages of GraphPro in terms of effectiveness, robustness and efficiency. The implementation details and source code of our GraphPro are available in the repository at https://github.com/HKUDS/GraphPro
SERENADE II: An ALMA Multi-Band Dust-Continuum Analysis of 28 Galaxies at $5

We present an analysis of ALMA multi-band dust-continuum observations for 28 spectroscopically-confirmed bright Lyman-break galaxies at $5<z<8$. Our sample consists of 11 galaxies at $z\sim6$ newly observed in our ALMA program, which substantially increases the number of $5<z<8$ galaxies with both rest-frame 88 and 158 $\mu{\rm m}$ continuum observations, allowing us to simultaneously measure the IR luminosity and dust temperature for a statistical sample of $z\gtrsim5$ galaxies for the first time. We derive the relationship between the UV slope ($\beta_{\rm UV}$) and infrared excess (IRX) for the $z\sim6$ galaxies, and find a shallower IRX-$\beta_{\rm UV}$ relation compared to the previous results at $z\sim2$--4. Based on the IRX-$\beta_{\rm UV}$ relation consistent with our results and the $\beta_{\rm UV}$-$M_{\rm UV}$ relation including fainter galaxies in the literature, we find a limited contribution of the dust-obscured star formation to the total SFR density, $\sim30\%$ at $z\sim6$. Our measurements of the dust temperature at $z\sim6-7$, $T_{\rm dust}=40. 9_{-9.1}^{+10.0}\,{\rm K}$ on average, supports a gentle increase of $T_{\rm dust}$ from $z=0$ to $z\sim6$--7. Using an analytic model with parameters consistent with recent {\it{JWST}} results, we discuss that the observed redshift evolution of the dust temperature can be reproduced by an $\sim0.6\,{\rm dex}$ increase in the gas depletion timescale and $\sim0.4\,{\rm dex}$ decrease of the metallicity. The variety of $T_{\rm dust}$ observed at high redshifts can also be naturally explained by scatters around the star-formation main sequence and average mass-metallicity relation, including an extremely high dust temperature of $T_{\rm dust}>80\,{\rm K}$ observed in a galaxy at $z=8.3$.