Miracle-dz/UniFAR-FTUs huggingface dataset:
R&B: Region and Boundary Aware Zero-shot Grounded Text-to-image Generation

Recent text-to-image (T2I) diffusion models have achieved remarkable progress in generating high-quality images given text-prompts as input. However, these models fail to convey appropriate spatial composition specified by a layout instruction. In this work, we probe into zero-shot grounded T2I generation with diffusion models, that is, generating images corresponding to the input layout information without training auxiliary modules or finetuning diffusion models. We propose a Region and Boundary (R&B) aware cross-attention guidance approach that gradually modulates the attention maps of diffusion model during generative process, and assists the model to synthesize images (1) with high fidelity, (2) highly compatible with textual input, and (3) interpreting layout instructions accurately. Specifically, we leverage the discrete sampling to bridge the gap between consecutive attention maps and discrete layout constraints, and design a region-aware loss to refine the generative layout during diffusion process. We further propose a boundary-aware loss to strengthen object discriminability within the corresponding regions. Experimental results show that our method outperforms existing state-of-the-art zero-shot grounded T2I generation methods by a large margin both qualitatively and quantitatively on several benchmarks.
Investigation into the nature behind the interesting half levitation behavior of claimed superconductor LK-99

A recent article published by Lee et.al. claimed to have successfully achieved superconductivity at room temperature (RT) has become a topical issue. Besides the research paper, Lee and his team provided a demonstration video of LK-99 half levitating (HL) on a magnet. Such interesting HL appearance has drawn tremendous sensation both in academia and the network. However, the true identity of LK-99 still remains unclear, i.e., whether the HL behavior can necessarily indicate the diamagnetism behavior of the sample. Here, we fabricated our own LK-99 samples following the procedures reported by Lee et al. We found quite a few sample pieces showing the typical HL that is similar to those reported. Meanwhile, oxidation during the sample preparation was found to deleterious to acquiring HL in the sample, while furnace cooling or water quenching in the last step revealed little effect. However, our careful observations indicated that those HL pieces are more likely simple ferromagnetic. Then we conducted a comprehensive study on the behavior patterns of typical diamagnetism and ferromagnetic substances interacting with a Nd2Fe14B magnet, and provided instructions to distinguish the characteristics between ferromagnetic and diamagnetic to prevent misunderstanding of LK-99 like levitation behavior.
Flow of temporal network properties under local aggregation and time shuffling: a tool for characterizing, comparing and classifying temporal networks

Although many tools have been developed and employed to characterize temporal networks, the issue of how to compare them remains largely open. It depends indeed on what features are considered as relevant, and on the way the differences in these features are quantified. In this paper, we propose to characterize temporal networks through their behaviour under general transformations that are local in time: (i) a local time shuffling, which destroys correlations at time scales smaller than a given scale $b$, while preserving large time scales, and (ii) a local temporal aggregation on time windows of length $n$. By varying $b$ and $n$, we obtain a flow of temporal networks, and flows of observable values, which encode the phenomenology of the temporal network on multiple time scales. We use a symbolic approach to summarize these flows into labels (strings of characters) describing their trends. These labels can then be used to compare temporal networks, validate models, or identify groups of networks with similar labels. Our procedure can be applied to any temporal network and with an arbitrary set of observables, and we illustrate it on an ensemble of data sets describing face-to-face interactions in various contexts, including both empirical and synthetic data.
Pay Attention to How You Drive: Safe and Adaptive Model-Based Reinforcement Learning for Off-Road Driving

Autonomous off-road driving is challenging as risky actions taken by the robot may lead to catastrophic damage. As such, developing controllers in simulation is often desirable as it provides a safer and more economical alternative. However, accurately modeling robot dynamics is difficult due to the complex robot dynamics and terrain interactions in unstructured environments. Domain randomization addresses this problem by randomizing simulation dynamics parameters, however this approach sacrifices performance for robustness leading to policies that are sub-optimal for any target dynamics. We introduce a novel model-based reinforcement learning approach that aims to balance robustness with adaptability. Our approach trains a System Identification Transformer (SIT) and an Adaptive Dynamics Model (ADM) under a variety of simulated dynamics. The SIT uses attention mechanisms to distill state-transition observations from the target system into a context vector, which provides an abstraction for its target dynamics. Conditioned on this, the ADM probabilistically models the system's dynamics. Online, we use a Risk-Aware Model Predictive Path Integral controller (MPPI) to safely control the robot under its current understanding of the dynamics. We demonstrate in simulation as well as in multiple real-world environments that this approach enables safer behaviors upon initialization and becomes less conservative (i.e. faster) as its understanding of the target system dynamics improves with more observations. In particular, our approach results in an approximately 41% improvement in lap-time over the non-adaptive baseline while remaining safe across different environments.
Spin–orbit coupling and Jahn–Teller effect in Td symmetry: an ab initio study on the substitutional nickel defect in diamond

We analyse the spin–orbit and Jahn–Teller interactions in Td symmetry that are relevant for substitutional transition metal defects in semiconductors. We apply our theory to the substitutional nickel defect in diamond and compute the appropriate fine-level structure and magneto-optical parameters by means of hybrid density functional theory. Our calculations confirm the intepretations of previous experimental findings that the 2.56 and 2.51 eV optical centres are associated with this defect. Our analysis of the electronic structure unravels possible mechanisms behind the observed optical transitions and the optically detected magnetic resonance signal, too. This article is part of the Theo Murphy meeting issue ‘Diamond for quantum applications’.
Goodhart's Law in Reinforcement Learning

Implementing a reward function that perfectly captures a complex task in the real world is impractical. As a result, it is often appropriate to think of the reward function as a proxy for the true objective rather than as its definition. We study this phenomenon through the lens of Goodhart's law, which predicts that increasing optimisation of an imperfect proxy beyond some critical point decreases performance on the true objective. First, we propose a way to quantify the magnitude of this effect and show empirically that optimising an imperfect proxy reward often leads to the behaviour predicted by Goodhart's law for a wide range of environments and reward functions. We then provide a geometric explanation for why Goodhart's law occurs in Markov decision processes. We use these theoretical insights to propose an optimal early stopping method that provably avoids the aforementioned pitfall and derive theoretical regret bounds for this method. Moreover, we derive a training method that maximises worst-case reward, for the setting where there is uncertainty about the true reward function. Finally, we evaluate our early stopping method experimentally. Our results support a foundation for a theoretically-principled study of reinforcement learning under reward misspecification.
Cross-correlation image analysis for real-time particle tracking

Accurately measuring the translations of objects between images is essential in many fields, including biology, medicine, chemistry, and physics. One important application is tracking one or more particles by measuring their apparent displacements in a series of images. Popular methods, such as the center-of-mass, often require idealized scenarios to reach the shot-noise limit of particle tracking and are, therefore, not generally applicable to multiple image types. More general methods, like maximum likelihood estimation, reliably approach the shot-noise limit, but are too computationally intense for use in real-time applications. These limitations are significant, as real-time, shot-noise-limited particle tracking is of paramount importance for feedback control systems. To fill this gap, we introduce a new cross-correlation-based algorithm that approaches shot-noise-limited displacement detection and a GPU-based implementation for real-time image analysis of a single particle.
METRA: Scalable Unsupervised RL with Metric-Aware Abstraction

Unsupervised pre-training strategies have proven to be highly effective in natural language processing and computer vision. Likewise, unsupervised reinforcement learning (RL) holds the promise of discovering a variety of potentially useful behaviors that can accelerate the learning of a wide array of downstream tasks. Previous unsupervised RL approaches have mainly focused on pure exploration and mutual information skill learning. However, despite the previous attempts, making unsupervised RL truly scalable still remains a major open challenge: pure exploration approaches might struggle in complex environments with large state spaces, where covering every possible transition is infeasible, and mutual information skill learning approaches might completely fail to explore the environment due to the lack of incentives. To make unsupervised RL scalable to complex, high-dimensional environments, we propose a novel unsupervised RL objective, which we call Metric-Aware Abstraction (METRA). Our main idea is, instead of directly covering the entire state space, to only cover a compact latent space $Z$ that is metrically connected to the state space $S$ by temporal distances. By learning to move in every direction in the latent space, METRA obtains a tractable set of diverse behaviors that approximately cover the state space, being scalable to high-dimensional environments. Through our experiments in five locomotion and manipulation environments, we demonstrate that METRA can discover a variety of useful behaviors even in complex, pixel-based environments, being the first unsupervised RL method that discovers diverse locomotion behaviors in pixel-based Quadruped and Humanoid. Our code and videos are available at https://seohong.me/projects/metra/
Flagellum Pumping Efficacy in Shear-Thinning Viscoelastic Fluids

Microorganism motility often takes place within complex, viscoelastic fluid environments, e.g., sperm in cervicovaginal mucus and bacteria in biofilms. In such complex fluids, strains and stresses generated by the microorganism are stored and relax across a spectrum of length and time scales and the complex fluid can be driven out of its linear response regime. Phenomena not possible in viscous media thereby arise from feedback between the"swimmer"and the complex fluid, making swimming efficiency co-dependent on the propulsion mechanism and fluid properties. Here we parameterize a flagellar motor and filament properties together with elastic relaxation and nonlinear shear-thinning properties of the fluid in a computational immersed boundary model. We then explore swimming efficiency over this parameter space. One exemplary insight is that motor efficiency (measured by the volumetric flow rate) can be boosted vs.\ degraded by moderate vs.\ strong shear-thinning of the viscoelastic environment.
Non-smoothable homeomorphisms of $4$-manifolds with boundary

We construct the first examples of non-smoothable self-homeomorphisms of smooth $4$-manifolds with boundary that fix the boundary and act trivially on homology. As a corollary, we construct self-diffeomorphisms of $4$-manifolds with boundary that fix the boundary and act trivially on homology but cannot be isotoped to any self-diffeomorphism supported in a collar of the boundary and, in particular, are not isotopic to any generalised Dehn twist.
From CLIP to DINO: Visual Encoders Shout in Multi-modal Large Language Models

Multi-modal Large Language Models (MLLMs) have made significant strides in expanding the capabilities of Large Language Models (LLMs) through the incorporation of visual perception interfaces. Despite the emergence of exciting applications and the availability of diverse instruction tuning data, existing approaches often rely on CLIP or its variants as the visual branch, and merely extract features from the deep layers. However, these methods lack a comprehensive analysis of the visual encoders in MLLMs. In this paper, we conduct an extensive investigation into the effectiveness of different vision encoders within MLLMs. Our findings reveal that the shallow layer features of CLIP offer particular advantages for fine-grained tasks such as grounding and region understanding. Surprisingly, the vision-only model DINO, which is not pretrained with text-image alignment, demonstrates promising performance as a visual branch within MLLMs. By simply equipping it with an MLP layer for alignment, DINO surpasses CLIP in fine-grained related perception tasks. Building upon these observations, we propose a simple yet effective feature merging strategy, named COMM, that integrates CLIP and DINO with Multi-level features Merging, to enhance the visual capabilities of MLLMs. We evaluate COMM through comprehensive experiments on a wide range of benchmarks, including image captioning, visual question answering, visual grounding, and object hallucination. Experimental results demonstrate the superior performance of COMM compared to existing methods, showcasing its enhanced visual capabilities within MLLMs.
Definition of the Polar Vortex Edge by LIDAR Data of Stratospheric Aerosol: A Comparison with Values of Potential Vorticity.

In this paper LIDAR data of stratospheric aerosol measured during four long range flights (during Northern Hemisphere winter) by the German research aircraft TRANSALL are employed to describe the edge of the polar vortex. All flights directly crossed the polar vortex edge. The position of the boundary of the vortex is clearly identified in the data. Values of potential vorticity on isentropic surfaces often used to describe the structure and edge of the polar vortex are calculated as well. They are interpreted with respect to the aerosol data. A comparison with previously published diagnostics of the edges of the polar vortices shows good agreement.
JWST-TST DREAMS: Quartz Clouds in the Atmosphere of WASP-17b

Clouds are prevalent in many of the exoplanet atmospheres that have been observed to date. For transiting exoplanets, we know if clouds are present because they mute spectral features and cause wavelength-dependent scattering. While the exact composition of these clouds is largely unknown, this information is vital to understanding the chemistry and energy budget of planetary atmospheres. In this work, we observe one transit of the hot Jupiter WASP-17b with JWST’s Mid-Infrared Instrument Low Resolution Spectrometer and generate a transmission spectrum from 5 to 12 μm. These wavelengths allow us to probe absorption due to the vibrational modes of various predicted cloud species. Our transmission spectrum shows additional opacity centered at 8.6 μm, and detailed atmospheric modeling and retrievals identify this feature as SiO2(s) (quartz) clouds. The SiO2(s) clouds model is preferred at 3.5–4. 2σ versus a cloud-free model and at 2. 6σ versus a generic aerosol prescription. We find the SiO2(s) clouds are composed of small ∼0.01 μm particles, which extend to high altitudes in the atmosphere. The atmosphere also shows a depletion of H2O, a finding consistent with the formation of high-temperature aerosols from oxygen-rich species. This work is part of a series of studies by our JWST Telescope Scientist Team (JWST-TST), in which we will use Guaranteed Time Observations to perform Deep Reconnaissance of Exoplanet Atmospheres through Multi-instrument Spectroscopy (DREAMS).
$^{13}$C NMR observation of a nonmagnetic charge-ordered state in the organic conductor $\kappa$-(ET)$_{2}$Hg(SCN)$_{2}$Cl

We investigated the local magnetism of the organic conductor, $\kappa$-(ET)$_{2}$Hg(SCN)$_{2}$Cl, with a quasi-triangular lattice of weakly dimerized molecules through $^{13}$C NMR spectroscopy. The NMR spectra and nuclear relaxation show that charge disproportionation occurs, associated with the metal-insulator transition at 31 K. The relaxation rate indicates that the paramagnetic spins in the insulating phase undergo a transition into a spin-singlet ground state with the emergence of orphan spins, a possible valence-bond-glass state. The present results are in high contrast to the spin-cluster paramagnetism of the electric dipole-liquid candidate, $\kappa$--(ET)$_{2}$Hg(SCN)$_{2}$Br, having nearly identical material parameters. This fact indicates that these two systems are on the verge between distinct phases in both charge and spin degrees of freedom; a spin-singlet charge-ordered state versus a spin-active Mott insulating state, competing with each other on a triangular lattice of dimerized sites.
The California Legacy Survey. IV. Lonely, Poor, and Eccentric: A Comparison between Solitary and Neighborly Gas Giants

We compare systems with single giant planets to systems with multiple giant planets using a catalog of planets from a high-precision radial velocity survey of FGKM stars. Our comparison focuses on orbital properties, planet masses, and host-star properties. We use hierarchical methods to model the orbital eccentricity distributions of giant singles and giant multiples, and find that the distributions are distinct. The multiple giant planets typically have moderate eccentricities and their eccentricity distribution extends to e = 0.47 (90th percentile), while the single giant planets have a pileup of nearly circular orbits and a long tail that extends to e = 0.77. We determine that the stellar hosts of multiple giants are distinctly more metal rich than the hosts of solitary giants, with respective mean metallicities of 0.228 ± 0.027 versus 0.129 ± 0.019 dex. We measure the distinct occurrence distributions of single and multiple giants with respect to orbital separation, and find that single gas giants have a ∼2. 3σ significant hot Jupiter (a < 0.06) pileup not seen among multigiant systems. We find that the median mass ( Msini ) of giants in multiples is nearly double that of single giants (1.71 M J versus 0.92 M J). We find that giant planets in the same system have correlated masses, analogous to the “peas in a pod” effect seen among less-massive planets.
A Survey of Multi-Robot Motion Planning

Multi-robot Motion Planning (MRMP) is an active research field which has gained attention over the years. MRMP has significant roles to improve the efficiency and reliability of multi-robot system in a wide range of applications from delivery robots to collaborative assembly lines. This survey provides an overview of MRMP taxonomy, state-of-the-art algorithms, and approaches which have been developed for multi-robot systems. This study also discusses the strengths and limitations of each algorithm and their applications in various scenarios. Moreover, based on this, we can draw out open problems for future research.
Transformer-based Multimodal Change Detection with Multitask Consistency Constraints

Change detection plays a fundamental role in Earth observation for analyzing temporal iterations over time. However, recent studies have largely neglected the utilization of multimodal data that presents significant practical and technical advantages compared to single-modal approaches. This research focuses on leveraging digital surface model (DSM) data and aerial images captured at different times for detecting change beyond 2D. We observe that the current change detection methods struggle with the multitask conflicts between semantic and height change detection tasks. To address this challenge, we propose an efficient Transformer-based network that learns shared representation between cross-dimensional inputs through cross-attention. It adopts a consistency constraint to establish the multimodal relationship, which involves obtaining pseudo change through height change thresholding and minimizing the difference between semantic and pseudo change within their overlapping regions. A DSM-to-image multimodal dataset encompassing three cities in the Netherlands was constructed. It lays a new foundation for beyond-2D change detection from cross-dimensional inputs. Compared to five state-of-the-art change detection methods, our model demonstrates consistent multitask superiority in terms of semantic and height change detection. Furthermore, the consistency strategy can be seamlessly adapted to the other methods, yielding promising improvements.
Multinational AGI Consortium (MAGIC): A Proposal for International Coordination on AI

This paper proposes a Multinational Artificial General Intelligence Consortium (MAGIC) to mitigate existential risks from advanced artificial intelligence (AI). MAGIC would be the only institution in the world permitted to develop advanced AI, enforced through a global moratorium by its signatory members on all other advanced AI development. MAGIC would be exclusive, safety-focused, highly secure, and collectively supported by member states, with benefits distributed equitably among signatories. MAGIC would allow narrow AI models to flourish while significantly reducing the possibility of misaligned, rogue, breakout, or runaway outcomes of general-purpose systems. We do not address the political feasibility of implementing a moratorium or address the specific legislative strategies and rules needed to enforce a ban on high-capacity AGI training runs. Instead, we propose one positive vision of the future, where MAGIC, as a global governance regime, can lay the groundwork for long-term, safe regulation of advanced AI.
Coexistence of Anomalous Hall Effect and Weak Net Magnetization in Collinear Antiferromagnet MnTe

Anomalous Hall effect (AHE) plays important role in the rapidly developing field of antiferromagnetic spintronics. It has been recently discussed that it can be a feature of not only uncompensated magnetic systems but also in altermagnetic materials. Hexagonal MnTe belongs to this appealing group of compounds exhibiting AHE and is commonly perceived as magnetically compensated. Here, we demonstrate that bulk form of MnTe exhibits small but detectable magnetic moment correlating with hysteretic behaviour of the AHE. We formulate a phenomenological model which explains how this feature allows to create a disbalance between states with opposite N\'eel vector and prevent the AHE signal from averaging out to zero. Moreover, we show how the dependence of AHE on the N\'eel vector arises on microscopical level and highlight the differences in Berry curvature between magnetically compensated and uncompensated systems.