Miracle-dz/UniFAR-FTUs huggingface dataset:
AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers

For effective human-robot interaction, robots need to understand, plan, and execute complex, long-horizon tasks described by natural language. Recent advances in large language models (LLMs) have shown promise for translating natural language into robot action sequences for complex tasks. However, existing approaches either translate the natural language directly into robot trajectories or factor the inference process by decomposing language into task sub-goals and relying on a motion planner to execute each sub-goal. When complex environmental and temporal constraints are involved, inference over planning tasks must be performed jointly with motion plans using traditional task-and-motion planning (TAMP) algorithms, making factorization into subgoals untenable. Rather than using LLMs to directly plan task sub-goals, we instead perform few-shot translation from natural language task descriptions to an intermediate task representation that can then be consumed by a TAMP algorithm to jointly solve the task and motion plan. To improve translation, we automatically detect and correct both syntactic and semantic errors via autoregressive re-prompting, resulting in significant improvements in task completion. We show that our approach outperforms several methods using LLMs as planners in complex task domains. See our project website https://yongchao98.github. io/MIT-REALM-AutoTAMP/ for prompts, videos, and code.
On the Dynamics of Learning Time-Aware Behavior with Recurrent Neural Networks

Recurrent Neural Networks (RNNs) have shown great success in modeling time-dependent patterns, but there is limited research on their learned representations of latent temporal features and the emergence of these representations during training. To address this gap, we use timed automata (TA) to introduce a family of supervised learning tasks modeling behavior dependent on hidden temporal variables whose complexity is directly controllable. Building upon past studies from the perspective of dynamical systems, we train RNNs to emulate temporal flipflops, a new collection of TA that emphasizes the need for time-awareness over long-term memory. We find that these RNNs learn in phases: they quickly perfect any time-independent behavior, but they initially struggle to discover the hidden time-dependent features. In the case of periodic"time-of-day"aware automata, we show that the RNNs learn to switch between periodic orbits that encode time modulo the period of the transition rules. We subsequently apply fixed point stability analysis to monitor changes in the RNN dynamics during training, and we observe that the learning phases are separated by a bifurcation from which the periodic behavior emerges. In this way, we demonstrate how dynamical systems theory can provide insights into not only the learned representations of these models, but also the dynamics of the learning process itself. We argue that this style of analysis may provide insights into the training pathologies of recurrent architectures in contexts outside of time-awareness.
Evolving Testing Scenario Generation Method and Intelligence Evaluation Framework for Automated Vehicles

Interaction between the background vehicles (BVs) and automated vehicles (AVs) in scenario-based testing plays a critical role in evaluating the intelligence of the AVs. Current testing scenarios typically employ predefined or scripted BVs, which inadequately reflect the complexity of human-like social behaviors in real-world driving scenarios, and also lack a systematic metric for evaluating the comprehensive intelligence of AVs. Therefore, this paper proposes an evolving scenario generation method that utilizes deep reinforcement learning (DRL) to create human-like BVs for testing and intelligence evaluation of AVs. Firstly, a class of driver models with human-like competitive, cooperative, and mutual driving motivations is designed. Then, utilizing an improved"level-k"training procedure, the three distinct driver models acquire game-based interactive driving policies. And these models are assigned to BVs for generating evolving scenarios in which all BVs can interact continuously and evolve diverse contents. Next, a framework including safety, driving efficiency, and interaction utility are presented to evaluate and quantify the intelligence performance of 3 systems under test (SUTs), indicating the effectiveness of the evolving scenario for intelligence testing. Finally, the complexity and fidelity of the proposed evolving testing scenario are validated. The results demonstrate that the proposed evolving scenario exhibits the highest level of complexity compared to other baseline scenarios and has more than 85% similarity to naturalistic driving data. This highlights the potential of the proposed method to facilitate the development and evaluation of high-level AVs in a realistic and challenging environment.
Qubit Number Optimization for Restriction Terms of QUBO Hamiltonians

In usual restriction terms of the Quadratic Unconstrained Binary Optimization (QUBO) hamiltonians, a integer number of logical qubits R, called the Integer Restriction Coefficient (IRC), are forced to stay active. In this paper we gather the well-known methods of implementing these restrictions, as well as some novel methods that show to be more efficient in some frequently implemented cases. Moreover, it is mathematically allowed to ask for fractional values of $R$. For these Fractionary Restriction Coefficients (FRC) we show how they can reduce the number of qubits needed to implement the restriction hamiltonian even further. Lastly, we characterize the response of DWave's Advantage$\_$system4.1 Quantum Annealer (QA) when faced with the implementation of FRCs, and offer a summary guide of the presented methods and the situations each of them is to be used.
Boosting GUI Prototyping with Diffusion Models

GUI (graphical user interface) prototyping is a widely-used technique in requirements engineering for gathering and refining requirements, reducing development risks and increasing stakeholder engagement. However, GUI prototyping can be a time-consuming and costly process. In recent years, deep learning models such as Stable Diffusion have emerged as a powerful text-to-image tool capable of generating detailed images based on text prompts. In this paper, we propose UI-Diffuser, an approach that leverages Stable Diffusion to generate mobile UIs through simple textual descriptions and UI components. Preliminary results show that UI-Diffuser provides an efficient and cost-effective way to generate mobile GUI designs while reducing the need for extensive prototyping efforts. This approach has the potential to significantly improve the speed and efficiency of GUI prototyping in requirements engineering.
$p$-adic Holography from the Hyperbolic Fracton Model

We reveal a low-temperature duality between the hyperbolic lattice model featuring fractons and infinite decoupled copies of Zabrodin's $p$-adic model of AdS/CFT. The core of the duality is the subsystem symmetries of the hyperbolic fracton model, which always act on both the boundary and the bulk. These subsystem symmetries are associated with fractal trees embedded in the hyperbolic lattice, which have the same geometry as Zabrodin's model. The fracton model, rewritten as electrostatics theory on these trees, matches the equation of motion of Zabrodin's model. The duality extends from the action to lattice defects as $p$-adic black holes.
GTC Follow-up Observations of Very Metal-poor Star Candidates from DESI

The observations from the Dark Energy Spectroscopic Instrument (DESI) will significantly increase the numbers of known extremely metal-poor stars by a factor of ∼10, improving the sample statistics to study the early chemical evolution of the Milky Way and the nature of the first stars. In this paper we report follow-up observations with high signal-to-noise ratio of nine metal-poor stars identified during the DESI commissioning with the Optical System for Imaging and Low-Resolution Integrated Spectroscopy (OSIRIS) instrument on the 10.4 m Gran Telescopio Canarias. The analysis of the data using a well-vetted methodology confirms the quality of the DESI spectra and the performance of the pipelines developed for the data reduction and analysis of DESI data.
Programming tunable active dynamics in a self-propelled robot

We present a scheme for producing tunable active dynamics in a self-propelled robotic device. The robot moves using the differential drive mechanism where two wheels can vary their instantaneous velocities independently. This velocity data is first calculated by equating its equations of motion in two dimensions with well-established active particle models and later encoded into the robot's microcontroller. We demonstrate that the robot can depict active Brownian, run and tumble, and Brownian dynamics with a wide range of parameters. The resulting motion analyzed using particle tracking shows excellent agreement with the theoretically predicted trajectories. Moreover, as a function of external signals received through hardware components like light intensity and infrared sensors, the robot can modify its dynamics providing spatiotemporal control over its motion. We demonstrate our claims with an experiment where the robot searches for an object and brings it back to its origin point, reminiscent of foraging and homing behaviour observed in living organisms. This work opens an avenue for designing tunable active systems with the potential of revealing the physics of active matter and its application for bio- and nature-inspired robotics.
Signature of Correlated Insulator in Electric Field Controlled Superlattice

The Bloch electron energy spectrum of a crystalline solid is determined by the underlying lattice structure at the atomic level. In a 2-dimensional (2d) crystal it is possible to impose a superlattice with nanometer-scale periodicity, allowing to tune the fundamental Bloch electron spectrum, and enabling novel physical properties which are not accessible in the original crystal. In recent years, a top-down approach for creating 2d superlattices on monolayer graphene by means of nanopatterned electric gates has been studied, which allows the formation of isolated energy bands and Hofstadter Butterfly physics in quantizing magnetic fields. Within this approach, however, evidence of electron correlations which drive many problems at the forefront of physics research remains to be uncovered. In this work we demonstrate signatures of a correlated insulator phase in Bernal-stacked bilayer graphene (BLG) modulated by a gate-defined superlattice potential, manifested as a set of resistance peaks centered at carrier densities of integer multiples of a single electron per unit cell of the superlattice potential. We associate the correlated insulator phase to the formation of flat energy bands due to the superlattice potential combined with inversion symmetry breaking. Inducing correlated electron phases with nanopatterning defined electric gates paves the way to custom-designed superlattices with arbitrary geometries and symmetries for studying band structure engineering and strongly correlated electrons in 2d materials.
Counting Distinct Elements in the Turnstile Model with Differential Privacy under Continual Observation

Privacy is a central challenge for systems that learn from sensitive data sets, especially when a system's outputs must be continuously updated to reflect changing data. We consider the achievable error for differentially private continual release of a basic statistic -- the number of distinct items -- in a stream where items may be both inserted and deleted (the turnstile model). With only insertions, existing algorithms have additive error just polylogarithmic in the length of the stream $T$. We uncover a much richer landscape in the turnstile model, even without considering memory restrictions. We show that every differentially private mechanism that handles insertions and deletions has worst-case additive error at least $T^{1/4}$ even under a relatively weak, event-level privacy definition. Then, we identify a parameter of the input stream, its maximum flippancy, that is low for natural data streams and for which we give tight parameterized error guarantees. Specifically, the maximum flippancy is the largest number of times that the contribution of a single item to the distinct elements count changes over the course of the stream. We present an item-level differentially private mechanism that, for all turnstile streams with maximum flippancy $w$, continually outputs the number of distinct elements with an $O(\sqrt{w} \cdot poly\log T)$ additive error, without requiring prior knowledge of $w$. We prove that this is the best achievable error bound that depends only on $w$, for a large range of values of $w$. When $w$ is small, the error of our mechanism is similar to the polylogarithmic in $T$ error in the insertion-only setting, bypassing the hardness in the turnstile model.
Metallicity Distribution Functions of 13 Ultra-faint Dwarf Galaxy Candidates from Hubble Space Telescope Narrowband Imaging

We present uniformly measured stellar metallicities of 463 stars in 13 Milky Way (MW) ultra-faint dwarf galaxies (UFDs; M V = −7.1 to −0.8) using narrowband CaHK (F395N) imaging taken with the Hubble Space Telescope. This represents the largest homogeneous set of stellar metallicities in UFDs, increasing the number of metallicities in these 13 galaxies by a factor of 5 and doubling the number of metallicities in all known MW UFDs. We provide the first well-populated MDFs for all galaxies in this sample, with 〈[Fe/H]〉 ranging from −3.0 to −2.0 dex, and σ [Fe/H] ranging from 0.3–0.7 dex. We find a nearly constant [Fe/H]∼ −2.6 over 3 decades in luminosity (∼102–105 L ⊙), suggesting that the mass–metallicity relationship does not hold for such faint systems. We find a larger fraction (24%) of extremely metal-poor ([Fe/H]< −3) stars across our sample compared to the literature (14%), but note that uncertainties in our most metal-poor measurements make this an upper limit. We find 19% of stars in our UFD sample to be metal-rich ([Fe/H] > −2), consistent with the sum of literature spectroscopic studies. MW UFDs are known to be predominantly >13 Gyr old, meaning that all stars in our sample are truly ancient, unlike metal-poor stars in the MW, which have a range of possible ages. Our UFD metallicities are not well matched to known streams in the MW, providing further evidence that known MW substructures are not related to UFDs. We include a catalog of our stars to encourage community follow-up studies, including priority targets for ELT-era observations.
A CO funnel in the Galactic centre: Molecular counterpart of the northern Galactic chimney

We report the discovery of a velocity coherent, funnel-shaped 13CO emission feature in the Galactic centre (GC) using data from the SEDIGISM survey. The molecular cloud appears as a low-velocity structure (VLSR = [ − 3.5, + 3.5] km s−1) with an angular extent of 0.95° ×1°, extending towards positive Galactic latitudes. The structure is offset from Sgr A* towards negative Galactic longitudes; it spatially and morphologically correlates well with the northern lobe of the 430 pc radio bubble, believed to be the radio counterpart of the multiwavelength GC chimney. Spectral line observations in the frequency range of 85–116 GHz have been carried out using the IRAM 30-m telescope towards 12 positions along the funnel-shaped emission. We examine the 12C/13C isotopic ratios using various molecules and their isotopologues. The mean 12C/13C isotope ratio (30.6 ± 2.9) is consistent with the structure located within inner 3 kpc of the Galaxy and possibly in the GC. The velocity of the molecular funnel is consistent with previous radio recombination line measurements of the northern lobe of radio bubble. Our multiwavelength analysis suggests that the funnel-shaped structure extending over 100 pc above the Galactic plane is the molecular counterpart of the northern GC chimney.
A potential third-generation gravitational-wave detector based on autocorrelative weak-value amplification

Reducing noises and enhancing signal-to-noise ratios (SNRs) have become critical for designing third-generation gravitational-wave (GW) detectors with a GW strain of less than 10−23 Hz−1 . In this paper, we propose a potential third-generation GW detector based on autocorrelative weak-value amplification (AWVA) for GW detection with a strain of hg= 4×10−25 Hz−1 . In our scheme, a GW event induces a phase difference Δϕ by passing through a 11-bounce delay line, 10-km arm-length, zero-area Sagnac interferometer illuminated with a 1064-nm laser. Subsequently, Δϕ is amplified as the parameter of post-selection by choosing the appropriate pre-selected state and coupling strength in AWVA. In particular, we theoretically investigate the AWVA measurements for GW detection within the frequency band of 200 Hz ⩽ fg ⩽ 800 Hz, considering Gaussian noises with negative-decibel SNRs. The peak response of the AWVA sensitivity κ(fg) occurs at frequency fg,max = 500 Hz, which falls within the frequency band of interest of the current third-generation GW detectors. Our simulation results indicate that AWVA can demonstrate a measurable sensitivity of the autocorrelation coefficient Θ(fg) within the frequency band of interest. Moreover, the robustness of WVA shows promising potential in mitigating the effects of Gaussian noises.
List distinguishing index of graphs

We say that an edge colouring breaks an automorphism if some edge is mapped to an edge of a different colour. We say that the colouring is distinguishing if it breaks every non-identity automorphism. We show that such colouring can be chosen from any set of lists associated to the edges of a graph G, whenever the size of each list is at least $\Delta-1$, where $\Delta$ is the maximum degree of G, apart from a few exceptions. This holds both for finite and infinite graphs. The bound is optimal for every $\Delta\ge 3$, and it is the same as in the non-list version.
Riemannian Laplace approximations for Bayesian neural networks

Bayesian neural networks often approximate the weight-posterior with a Gaussian distribution. However, practical posteriors are often, even locally, highly non-Gaussian, and empirical performance deteriorates. We propose a simple parametric approximate posterior that adapts to the shape of the true posterior through a Riemannian metric that is determined by the log-posterior gradient. We develop a Riemannian Laplace approximation where samples naturally fall into weight-regions with low negative log-posterior. We show that these samples can be drawn by solving a system of ordinary differential equations, which can be done efficiently by leveraging the structure of the Riemannian metric and automatic differentiation. Empirically, we demonstrate that our approach consistently improves over the conventional Laplace approximation across tasks. We further show that, unlike the conventional Laplace approximation, our method is not overly sensitive to the choice of prior, which alleviates a practical pitfall of current approaches.
On Improving the Cohesiveness of Graphs by Merging Nodes: Formulation, Analysis, and Algorithms

Graphs are a powerful mathematical model, and they are used to represent real-world structures in various fields. In many applications, real-world structures with high connectivity and robustness are preferable. For enhancing the connectivity and robustness of graphs, two operations, adding edges and anchoring nodes, have been extensively studied. However, merging nodes, which is a realistic operation in many scenarios (e.g., bus station reorganization, multiple team formation), has been overlooked. In this work, we study the problem of improving graph cohesiveness by merging nodes. First, we formulate the problem mathematically using the size of the k-truss, for a given k, as the objective. Then, we prove the NP-hardness and non-modularity of the problem. After that, we develop BATMAN, a fast and effective algorithm for choosing sets of nodes to be merged, based on our theoretical findings and empirical observations. Lastly, we demonstrate the superiority of BATMAN over several baselines, in terms of speed and effectiveness, through extensive experiments on fourteen real-world graphs.
Cancellation-Free Regret Bounds for Lagrangian Approaches in Constrained Markov Decision Processes

Constrained Markov Decision Processes (CMDPs) are one of the common ways to model safe reinforcement learning problems, where constraint functions model the safety objectives. Lagrangian-based dual or primal-dual algorithms provide efficient methods for learning in CMDPs. For these algorithms, the currently known regret bounds in the finite-horizon setting allow for a"cancellation of errors"; one can compensate for a constraint violation in one episode with a strict constraint satisfaction in another. However, we do not consider such a behavior safe in practical applications. In this paper, we overcome this weakness by proposing a novel model-based dual algorithm OptAug-CMDP for tabular finite-horizon CMDPs. Our algorithm is motivated by the augmented Lagrangian method and can be performed efficiently. We show that during $K$ episodes of exploring the CMDP, our algorithm obtains a regret of $\tilde{O}(\sqrt{K})$ for both the objective and the constraint violation. Unlike existing Lagrangian approaches, our algorithm achieves this regret without the need for the cancellation of errors.
Minimal model of quasi-cyclic behaviour in turbulence driven by Taylor–Green forcing

We attempt to formulate the simplest possible model mimicking turbulent dynamics, such as quasi-cyclic behaviour (QCB), using only three variables. To this end, we first conduct direct numerical simulations of three-dimensional flow driven by the steady Taylor–Green forcing to find a similarity between a stable periodic orbit (SPO) at a small Reynolds number (Re) and turbulent QCB at higher Re. A close examination of the SPO allows the heuristic formulation of a three-equation model, representing the evolution of Fourier modes in three distinct scales. The model reproduces the continuous bifurcation from SPO to turbulence with QCB when Re is varied. We also demonstrate that, by changing model parameters, the proposed model exhibits a discontinuous transition from steady to chaotic solutions without going through an SPO.
Characterization of Secondary Drops Using Digital In-line Holography.

Visualizing the liquid breakup process and quantifying drop morphology, sizes, spatial distribution and threedimensional (3D) velocity are crucial for characterization and modeling of atomization processes. Digital in-line holography (DIH) is uniquely capable of imaging the breakup geometry and measuring droplet size and 3D velocity distributions. In the present study, a DIH system is configured to record sequential holograms by synchronizing a CCD camera operating in double-exposure mode and a double-pulsed Nd:YAG laser. A recently proposed hybrid method is used to process the holograms to extract the particle information. Particle matching is conducted to extract the 3D three-component velocities of droplets. The DIH system, along with the hybrid method, is applied to characterize the secondary atomization processes due to aerodynamic breakup of drop and drop impact on a thin film. In both applications, the size distribution, 3D spatial distribution and velocity of secondary drops are determined quantitatively. In particular, 3D morphology of the intermediate rim in the bag breakup mode is extracted. Coincidence with the results obtained from phase Doppler anemometry (PDA) measurement is found, demonstrating the accuracy of the measurement by DIH and the hybrid method. ∗Corresponding Author: junchen@purdue.edu