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Generative AI systems have shown impressive capabilities in creating text, code, and images. Inspired by the rich history of research in industrial ''Design for Assembly'', we introduce a novel problem: Generative Design-for-Robot-Assembly (GDfRA). The task is to generate an assembly based on a natural language prompt (e.g., ''giraffe'') and an image of available physical components, such as 3D-printed blocks. The output is an assembly, a spatial arrangement of these components, and instructions for a robot to build this assembly. The output must 1) resemble the requested object and 2) be reliably assembled by a 6 DoF robot arm with a suction gripper. We then present Blox-Net, a GDfRA system that combines generative vision language models with well-established methods in computer vision, simulation, perturbation analysis, motion planning, and physical robot experimentation to solve a class of GDfRA problems with minimal human supervision. Blox-Net achieved a Top-1 accuracy of 63.5% in the ''recognizability'' of its designed assemblies (eg, resembling giraffe as judged by a VLM). These designs, after automated perturbation redesign, were reliably assembled by a robot, achieving near-perfect success across 10 consecutive assembly iterations with human intervention only during reset prior to assembly. Surprisingly, this entire design process from textual word (''giraffe'') to reliable physical assembly is performed with zero human intervention., Comment: 8 pages, 7 Figures

In logistics, the ability to quickly compute and execute pick-and-place motions from bins is critical to increasing productivity. We present Bin-Optimized Motion Planning (BOMP), a motion planning framework that plans arm motions for a six-axis industrial robot with a long-nosed suction tool to remove boxes from deep bins. BOMP considers robot arm kinematics, actuation limits, the dimensions of a grasped box, and a varying height map of a bin environment to rapidly generate time-optimized, jerk-limited, and collision-free trajectories. The optimization is warm-started using a deep neural network trained offline in simulation with 25,000 scenes and corresponding trajectories. Experiments with 96 simulated and 15 physical environments suggest that BOMP generates collision-free trajectories that are up to 58 % faster than baseline sampling-based planners and up to 36 % faster than an industry-standard Up-Over-Down algorithm, which has an extremely low 15 % success rate in this context. BOMP also generates jerk-limited trajectories while baselines do not. Website: https://sites.google.com/berkeley.edu/bomp.

Text-to-image (T2I) models are remarkable at generating realistic images based on textual descriptions. However, textual prompts are inherently underspecified: they do not specify all possible attributes of the required image. This raises two key questions: Do T2I models generate diverse outputs on underspecified prompts? How can we automatically measure diversity? We propose GRADE: Granular Attribute Diversity Evaluation, an automatic method for quantifying sample diversity. GRADE leverages the world knowledge embedded in large language models and visual question-answering systems to identify relevant concept-specific axes of diversity (e.g., ``shape'' and ``color'' for the concept ``cookie''). It then estimates frequency distributions of concepts and their attributes and quantifies diversity using (normalized) entropy. GRADE achieves over 90% human agreement while exhibiting weak correlation to commonly used diversity metrics. We use GRADE to measure the overall diversity of 12 T2I models using 400 concept-attribute pairs, revealing that all models display limited variation. Further, we find that these models often exhibit default behaviors, a phenomenon where the model consistently generates concepts with the same attributes (e.g., 98% of the cookies are round). Finally, we demonstrate that a key reason for low diversity is due to underspecified captions in training data. Our work proposes a modern, semantically-driven approach to measure sample diversity and highlights the stunning homogeneity in outputs by T2I models., Comment: For project page and code see https://royira.github.io/GRADE

While numerous planetary and asteroid satellites show evidence for non-trivial rotation states, none are as emblematic as Hyperion, which has long been held as the most striking example of chaotic spin-orbit evolution in the Solar System. Nevertheless, an analytically tractable theory of the full 3D spin-orbit dynamics of Hyperion has not been developed. We derive the Hamiltonian for a spinning axisymmetric satellite in the gravitational potential of a planet without assuming planar or principal axis rotation and without averaging over the spin period. Using this model, we demonstrate the emergence of resonances between the nutation and orbital frequencies that act as the primary drivers of the spin dynamics. This analysis reveals that, contrary to long-held belief, Hyperion is not tumbling chaotically. Instead, it lies near or in a nutation-orbit resonance that is first-order in eccentricity, allowing it to rotate quasi-regularly. The most reliable observations are consistent with either nonchaotic motion or chaos that is orders of magnitude smaller than originally claimed. A separate phenomenon, the so-called barrel instability, is shown to be related to a different set of nutation-orbit resonances that generalize the planar spin-orbit resonances. Finally, we show that changes in spin states over long timescales are best understood by considering chaotic diffusion of quasi-conserved quantities., Comment: Accepted to A&A, 18 pages, 9 figures

We compute several types of dimension for the bounded derived categories of coherent sheaves of orbifold curves. This completes the calculation of these dimensions for derived categories of noncommutative curves in the sense of Reiten-van den Bergh. Along the way we construct stability conditions for orbifold curves. We also obtain a characterisation of orbifold curves with hereditary tilting bundle in terms of diagonal dimension., Comment: 23 pages, comments welcome!

Running optimization across many parallel seeds leveraging GPU compute have relaxed the need for a good initialization, but this can fail if the problem is highly non-convex as all seeds could get stuck in local minima. One such setting is collision-free motion optimization for robot manipulation, where optimization converges quickly on easy problems but struggle in obstacle dense environments (e.g., a cluttered cabinet or table). In these situations, graph-based planning algorithms are used to obtain seeds, resulting in significant slowdowns. We propose DiffusionSeeder, a diffusion based approach that generates trajectories to seed motion optimization for rapid robot motion planning. DiffusionSeeder takes the initial depth image observation of the scene and generates high quality, multi-modal trajectories that are then fine-tuned with a few iterations of motion optimization. We integrate DiffusionSeeder to generate the seed trajectories for cuRobo, a GPU-accelerated motion optimization method, which results in 12x speed up on average, and 36x speed up for more complicated problems, while achieving 10% higher success rate in partially observed simulation environments. Our results show the effectiveness of using diverse solutions from a learned diffusion model. Physical experiments on a Franka robot demonstrate the sim2real transfer of DiffusionSeeder to the real robot, with an average success rate of 86% and planning time of 26ms, improving on cuRobo by 51% higher success rate while also being 2.5x faster.

While coreference resolution is traditionally used as a component in individual document understanding, in this work we take a more global view and explore what can we learn about a domain from the set of all document-level coreference relations that are present in a large corpus. We derive coreference chains from a corpus of 30 million biomedical abstracts and construct a graph based on the string phrases within these chains, establishing connections between phrases if they co-occur within the same coreference chain. We then use the graph structure and the betweeness centrality measure to distinguish between edges denoting hierarchy, identity and noise, assign directionality to edges denoting hierarchy, and split nodes (strings) that correspond to multiple distinct concepts. The result is a rich, data-driven ontology over concepts in the biomedical domain, parts of which overlaps significantly with human-authored ontologies. We release the coreference chains and resulting ontology under a creative-commons license, along with the code.

We consider the problem of sampling from the ferromagnetic $q$-state Potts model on the random $d$-regular graph with parameter $\beta>0$. A key difficulty that arises in sampling from the model is the existence of a metastability window $(\beta_u,\beta_u')$ where the distribution has two competing modes, the so-called disordered and ordered phases, causing MCMC-based algorithms to be slow mixing from worst-case initialisations. To this end, Helmuth, Jenssen and Perkins designed a sampling algorithm that works for all $\beta$ when $q$ is large, using cluster expansion methods; more recently, their analysis technique has been adapted to show that random-cluster dynamics mixes fast when initialised more judiciously. However, a bottleneck behind cluster-expansion arguments is that they inherently only work for large $q$, whereas it is widely conjectured that sampling is possible for all $q,d\geq 3$. The only result so far that applies to general $q,d\geq 3$ is by Blanca and Gheissari who showed that the random-cluster dynamics mixes fast for $\beta<\beta_u$. For $\beta>\beta_u$, certain correlation phenomena emerge because of the metastability which have been hard to handle, especially for small $q$ and $d$. Our main contribution is to perform a delicate analysis of the Potts distribution and the random-cluster dynamics that goes beyond the threshold $\beta_u$. We use planting as the main tool in our proofs, and combine it with the analysis of random-cluster dynamics. We are thus able to show that the random-cluster dynamics initialised from all-out mixes fast for all integers $q,d\geq 3$ beyond the uniqueness threshold $\beta_u$; our analysis works all the way up to the threshold $\beta_c\in (\beta_u,\beta_u')$ where the dominant mode switches from disordered to ordered. We also obtain an algorithm in the ordered regime $\beta>\beta_c$ that refines significantly the range of $q,d$., Comment: Abstract shortened to meet arXiv requirements

In this paper, we showed that for suitable $(\beta,p, s,\ell)$ the $\beta$-order fractional derivative with respect to the last coordinate of the Fourier transform of an $L^p(\mathbb{R}^n)$ function is in $H^{-s}$ after restricting to a graph of a function with non-vanishing Gaussian curvature provided that the restriction of the Fourier transform of such function to the surface is in $H^{\ell}$. This is a generalization of the result in \cite{GoldStol}*{Theorem 1.12}., Comment: Fixed the description of Figure 3 and 4

Cloud robotics enables robots to offload computationally intensive tasks to cloud servers for performance, cost, and ease of management. However, the network and cloud computing infrastructure are not designed for reliable timing guarantees, due to fluctuating Quality-of-Service (QoS). In this work, we formulate an impossibility triangle theorem for: Latency reliability, Singleton server, and Commodity hardware. The LSC theorem suggests that providing replicated servers with uncorrelated failures can exponentially reduce the probability of missing a deadline. We present FogROS2-Probabilistic Latency Reliability (PLR) that uses multiple independent network interfaces to send requests to replicated cloud servers and uses the first response back. We design routing mechanisms to discover, connect, and route through non-default network interfaces on robots. FogROS2-PLR optimizes the selection of interfaces to servers to minimize the probability of missing a deadline. We conduct a cloud-connected driving experiment with two 5G service providers, demonstrating FogROS2-PLR effectively provides smooth service quality even if one of the service providers experiences low coverage and base station handover. We use 99 Percentile (P99) latency to evaluate anomalous long-tail latency behavior. In one experiment, FogROS2-PLR improves P99 latency by up to 3.7x compared to using one service provider. We deploy FogROS2-PLR on a physical Stretch 3 robot performing an indoor human-tracking task. Even in a fully covered Wi-Fi and 5G environment, FogROS2-PLR improves the responsiveness of the robot reducing mean latency by 36% and P99 latency by 33%., Comment: Submitted to 2025 IEEE International Conference on Robotics & Automation

Humans can learn to manipulate new objects by simply watching others; providing robots with the ability to learn from such demonstrations would enable a natural interface specifying new behaviors. This work develops Robot See Robot Do (RSRD), a method for imitating articulated object manipulation from a single monocular RGB human demonstration given a single static multi-view object scan. We first propose 4D Differentiable Part Models (4D-DPM), a method for recovering 3D part motion from a monocular video with differentiable rendering. This analysis-by-synthesis approach uses part-centric feature fields in an iterative optimization which enables the use of geometric regularizers to recover 3D motions from only a single video. Given this 4D reconstruction, the robot replicates object trajectories by planning bimanual arm motions that induce the demonstrated object part motion. By representing demonstrations as part-centric trajectories, RSRD focuses on replicating the demonstration's intended behavior while considering the robot's own morphological limits, rather than attempting to reproduce the hand's motion. We evaluate 4D-DPM's 3D tracking accuracy on ground truth annotated 3D part trajectories and RSRD's physical execution performance on 9 objects across 10 trials each on a bimanual YuMi robot. Each phase of RSRD achieves an average of 87% success rate, for a total end-to-end success rate of 60% across 90 trials. Notably, this is accomplished using only feature fields distilled from large pretrained vision models -- without any task-specific training, fine-tuning, dataset collection, or annotation. Project page: https://robot-see-robot-do.github.io, Comment: CoRL 2024, Project page: https://robot-see-robot-do.github.io

Building semantic 3D maps is valuable for searching for objects of interest in offices, warehouses, stores, and homes. We present a mapping system that incrementally builds a Language-Embedded Gaussian Splat (LEGS): a detailed 3D scene representation that encodes both appearance and semantics in a unified representation. LEGS is trained online as a robot traverses its environment to enable localization of open-vocabulary object queries. We evaluate LEGS on 4 room-scale scenes where we query for objects in the scene to assess how LEGS can capture semantic meaning. We compare LEGS to LERF and find that while both systems have comparable object query success rates, LEGS trains over 3.5x faster than LERF. Results suggest that a multi-camera setup and incremental bundle adjustment can boost visual reconstruction quality in constrained robot trajectories, and suggest LEGS can localize open-vocabulary and long-tail object queries with up to 66% accuracy.

Multiphoton interference is crucial to many photonic quantum technologies. In particular, interference forms the basis of optical quantum information processing platforms and can lead to significant computational advantages. It is therefore interesting to study the interference arising from various states of light in large interferometric networks. Here, we implement a quantum walk in a highly stable, low-loss, multiport interferometer with up to 24 ultrafast time bins. This time-bin interferometer comprises a sequence of birefringent crystals which produce pulses separated by 4.3\,ps, all along a single optical axis. Ultrafast Kerr gating in an optical fiber is employed to time-demultiplex the output from the quantum walk. We measure one-, two-, and three-photon interference arising from various input state combinations, including a heralded single-photon state, a thermal state, and an attenuated coherent state at one or more input ports. Our results demonstrate that ultrafast time bins are a promising platform to observe large-scale multiphoton interference.

Scaling up robot learning requires large and diverse datasets, and how to efficiently reuse collected data and transfer policies to new embodiments remains an open question. Emerging research such as the Open-X Embodiment (OXE) project has shown promise in leveraging skills by combining datasets including different robots. However, imbalances in the distribution of robot types and camera angles in many datasets make policies prone to overfit. To mitigate this issue, we propose RoVi-Aug, which leverages state-of-the-art image-to-image generative models to augment robot data by synthesizing demonstrations with different robots and camera views. Through extensive physical experiments, we show that, by training on robot- and viewpoint-augmented data, RoVi-Aug can zero-shot deploy on an unseen robot with significantly different camera angles. Compared to test-time adaptation algorithms such as Mirage, RoVi-Aug requires no extra processing at test time, does not assume known camera angles, and allows policy fine-tuning. Moreover, by co-training on both the original and augmented robot datasets, RoVi-Aug can learn multi-robot and multi-task policies, enabling more efficient transfer between robots and skills and improving success rates by up to 30%. Project website: https://rovi-aug.github.io., Comment: CoRL 2024 (Oral). Project website: https://rovi-aug.github.io

At the start of the COVID-19 pandemic, home visiting programs were faced with an unprecedented challenge: How do you deliver home visiting services without visiting homes? One home visiting model--Child First--quickly pivoted to telehealth, offering caregivers the option to receive services virtually. Child First has since resumed delivering services primarily in person, but some pandemic-driven implementation changes remain. To understand the impact of the pandemic on the Child First model, the study team conducted surveys and interviews with Child First staff members, and interviews with caregivers who received Child First services, to answer the following research questions: (1) To what extent did the implementation of Child First services change since the start of the pandemic?; (2) How did Child First staff members report implementing core components of the model since the pandemic began?; and (3) How did Child First caregivers report on Child First services that they received since the pandemic began? Overall, the study team found that the implementation of the Child First model following the first three years of the pandemic remains largely consistent with pre-pandemic implementation, despite the unique challenges to home visiting posed by the pandemic.

We explore how to enhance next-token prediction models to perform in-context imitation learning on a real robot, where the robot executes new tasks by interpreting contextual information provided during the input phase, without updating its underlying policy parameters. We propose In-Context Robot Transformer (ICRT), a causal transformer that performs autoregressive prediction on sensorimotor trajectories without relying on any linguistic data or reward function. This formulation enables flexible and training-free execution of new tasks at test time, achieved by prompting the model with sensorimotor trajectories of the new task composing of image observations, actions and states tuples, collected through human teleoperation. Experiments with a Franka Emika robot demonstrate that the ICRT can adapt to new tasks specified by prompts, even in environment configurations that differ from both the prompt and the training data. In a multitask environment setup, ICRT significantly outperforms current state-of-the-art next-token prediction models in robotics on generalizing to unseen tasks. Code, checkpoints and data are available on https://icrt.dev/

The exponential growth of scientific literature necessitates advanced tools for effective knowledge exploration. We present Knowledge Navigator, a system designed to enhance exploratory search abilities by organizing and structuring the retrieved documents from broad topical queries into a navigable, two-level hierarchy of named and descriptive scientific topics and subtopics. This structured organization provides an overall view of the research themes in a domain, while also enabling iterative search and deeper knowledge discovery within specific subtopics by allowing users to refine their focus and retrieve additional relevant documents. Knowledge Navigator combines LLM capabilities with cluster-based methods to enable an effective browsing method. We demonstrate our approach's effectiveness through automatic and manual evaluations on two novel benchmarks, CLUSTREC-COVID and SCITOC. Our code, prompts, and benchmarks are made publicly available.

In the flavour of categorical quantum mechanics, we extend nonlocal games to allow quantum questions and answers, using quantum sets (special symmetric dagger Frobenius algebras) and the quantum functions of Musto, Reutter, and Verdon [arXiv:1711.07945]. Equations are presented using a diagrammatic calculus for tensor categories. To this quantum question and answer setting, we extend the standard definitions, including strategies, correlations, and synchronicity, and we use these definitions to extend results about synchronicity. We extend the graph homomorphism (isomorphism) game to quantum graphs, and show it is synchronous (bisynchronous) and that its perfect quantum-commuting (bi)strategies are quantum graph homomorphisms (isomorphisms). Our extended definitions agree with the existing quantum games literature, except in the case of synchronicity., Comment: 53 pages

In Gasket Assembly, a deformable gasket must be aligned and pressed into a narrow channel. This task is common for sealing surfaces in the manufacturing of automobiles, appliances, electronics, and other products. Gasket Assembly is a long-horizon, high-precision task and the gasket must align with the channel and be fully pressed in to achieve a secure fit. To compare approaches, we present 4 methods for Gasket Assembly: one policy from deep imitation learning and three procedural algorithms. We evaluate these methods with 100 physical trials. Results suggest that the Binary+ algorithm succeeds in 10/10 on the straight channel whereas the learned policy based on 250 human teleoperated demonstrations succeeds in 8/10 trials and is significantly slower. Code, CAD models, videos, and data can be found at https://berkeleyautomation.github.io/robot-gasket/, Comment: Content without Appendix accepted for IEEE CASE 2024

We predict the existence of $\alpha$ Ori B, a low-mass companion orbiting Betelgeuse. This is motivated by the presence of a 2170-day Long Secondary Period (LSP) in Betelgeuse's lightcurve, a periodicity $\approx5$ times longer than the star's 416 day fundamental radial pulsation mode. While binarity is currently the leading hypothesis for LSPs in general, the LSP and the radial velocity variation observed in Betelgeuse, taken together, necessitate a revision of the prevailing physical picture. The lightcurve-RV phase difference requires a companion to be behind Betelgeuse at the LSP luminosity minimum, 180 degrees out of phase with the system orientation associated with occultation. We demonstrate the consistency of this model with available observational constraints and identify tensions in all other proposed LSP hypotheses. Within this framework, we calculate a mass for $\alpha$ Ori B of $M\sin i =1.17\pm0.7\,M_\odot$ and an orbital separation of $1850\pm70\,R_\odot$, or $2.43^{+0.21}_{-0.32}$ times the radius of Betelgeuse. We then describe the features of the companion as constrained by the fundamental parameters of Betelgeuse and its orbital system, and discuss what would be required to confirm the companion's existence observationally., Comment: 18 pages, 4 figures, 3 tables, accepted to the Astrophysical Journal

We present and analyze the extensive optical broadband photometry of the Type II SN 2023ixf up to one year after explosion. We find that, when compared to two pre-existing model grids, the pseudo-bolometric light curve is consistent with drastically different combinations of progenitor and explosion properties. This may be an effect of known degeneracies in Type IIP light-curve models. We independently compute a large grid of ${\tt MESA+STELLA}$ single-star progenitor and light-curve models with various zero-age main-sequence masses, mass-loss efficiencies, and convective efficiencies. Using the observed progenitor variability as an additional constraint, we select stellar models consistent with the pulsation period and explode them according to previously established scaling laws to match plateau properties. Our hydrodynamic modeling indicates that SN 2023ixf is most consistent with a moderate-energy ($E_{\rm exp}\approx7\times10^{50}$ erg) explosion of an initially high-mass red supergiant progenitor ($\gtrsim 17\ M_{\odot}$) that lost a significant amount of mass in its prior evolution, leaving a low-mass hydrogen envelope ($\lesssim 3\ M_{\odot}$) at the time of explosion, with a radius $\gtrsim 950\ R_{\odot}$ and a synthesized $^{56}$Ni mass of $0.07\ M_{\odot}$. We posit that previous mass transfer in a binary system may have stripped the envelope of SN 2023ixf's progenitor. The analysis method with pulsation period presented in this work offers a way to break degeneracies in light-curve modeling in the future, particularly with the upcoming Vera C.~Rubin Observatory Legacy Survey of Space and Time, when a record of progenitor variability will be more common., Comment: 18 pages, 7 figures, submitted to ApJ. Comments welcome

The 1st Workshop on Data Contamination (CONDA 2024) focuses on all relevant aspects of data contamination in natural language processing, where data contamination is understood as situations where evaluation data is included in pre-training corpora used to train large scale models, compromising evaluation results. The workshop fostered a shared task to collect evidence on data contamination in current available datasets and models. The goal of the shared task and associated database is to assist the community in understanding the extent of the problem and to assist researchers in avoiding reporting evaluation results on known contaminated resources. The shared task provides a structured, centralized public database for the collection of contamination evidence, open to contributions from the community via GitHub pool requests. This first compilation paper is based on 566 reported entries over 91 contaminated sources from a total of 23 contributors. The details of the individual contamination events are available in the platform. The platform continues to be online, open to contributions from the community., Comment: https://huggingface.co/spaces/CONDA-Workshop/Data-Contamination-Database

The article further develops and formalizes a theory of friendly dialogue in an AI System of Dr. Watson type, as proposed in our previous publication[4],[19]. The main principle of this type of AI is to guide the user toward a solution in a friendly manner, using questions based on the analysis of user input and data collected in the system.

Relativistic jets from a Kerr black hole (BH) following the core collapse of a massive star ("collapsar") is a leading model for gamma-ray bursts (GRBs). However, the two key ingredients for a Blandford-Znajek powered jet $-$ rapid rotation and a strong magnetic field $-$ seem mutually exclusive. Strong fields in the progenitor star's core transport angular momentum outwards more quickly, slowing down the core before collapse. Through innovative multidisciplinary modeling, we first use MESA stellar evolution models followed to core collapse, to explicitly show that the small length-scale of the instabilities $-$ likely responsible for angular momentum transport in the core (e.g., Tayler-Spruit) $ - $ results in a low net magnetic flux fed to the BH horizon, far too small to power GRB jets. Instead, we propose a novel scenario in which collapsar BHs acquire their magnetic "hair" from their progenitor proto-neutron star (PNS), which is likely highly magnetized from an internal dynamo. We evaluate the conditions for the BH accretion disk to pin the PNS magnetosphere to its horizon immediately after the collapse. Our results show that the PNS spin-down energy released before collapse matches the kinetic energy of Type Ic-BL supernovae, while the nascent BH's spin and magnetic flux produce jets consistent with observed GRB characteristics. We map our MESA models to 3D general-relativistic magnetohydrodynamic simulations and confirm that accretion disks confine the strong magnetic flux initiated near a rotating BH, enabling the launch of successful GRB jets, whereas a slower-spinning BH or one without a disk fails to do so., Comment: For movies of the simulations, see https://oregottlieb.com/BH_field.html

We present detailed multi-band photometric and spectroscopic observations and analysis of a rare core-collapse supernova SN 2021wvw, that includes photometric evolution up to 250 d and spectroscopic coverage up to 100 d post-explosion. A unique event that does not fit well within the general trends observed for Type II-P supernovae, SN 2021wvw shows an intermediate luminosity with a short plateau phase of just about 75 d, followed by a very sharp (~10 d) transition to the tail phase. Even in the velocity space, it lies at a lower velocity compared to a larger Type II sample. The observed peak absolute magnitude is -16.1 mag in r-band, and the nickel mass is well constrained to 0.020(6) Msol. Detailed hydrodynamical modeling using MESA+STELLA suggests a radially compact, low-metallicity, high-mass Red Supergiant progenitor (ZAMS mass=18 Msol), which exploded with ~0.2e51 erg/s leaving an ejecta mass of Mej~5 Msol. Significant late-time fallback during the shock propagation phase is also seen in progenitor+explosion models consistent with the light curve properties. As the faintest short-plateau supernova characterized to date, this event adds to the growing diversity of transitional events between the canonical ~100 d plateau Type IIP and stripped-envelope events., Comment: Accepted for publication in the Astrophysical Journal (18 pages, 13 figures, 4 tables)

The language used by US courtroom actors in criminal trials has long been studied for biases. However, systematic studies for bias in high-stakes court trials have been difficult, due to the nuanced nature of bias and the legal expertise required. Large language models offer the possibility to automate annotation. But validating the computational approach requires both an understanding of how automated methods fit in existing annotation workflows and what they really offer. We present a case study of adding a computational model to a complex and high-stakes problem: identifying gender-biased language in US capital trials for women defendants. Our team of experienced death-penalty lawyers and NLP technologists pursue a three-phase study: first annotating manually, then training and evaluating computational models, and finally comparing expert annotations to model predictions. Unlike many typical NLP tasks, annotating for gender bias in months-long capital trials is complicated, with many individual judgment calls. Contrary to standard arguments for automation that are based on efficiency and scalability, legal experts find the computational models most useful in providing opportunities to reflect on their own bias in annotation and to build consensus on annotation rules. This experience suggests that seeking to replace experts with computational models for complex annotation is both unrealistic and undesirable. Rather, computational models offer valuable opportunities to assist the legal experts in annotation-based studies.

Current Text-to-Code models demonstrate impressive capabilities in generating executable code from natural language snippets. However, current studies focus on technical instructions and programmer-oriented language, and it is an open question whether these models can effectively translate natural language descriptions given by non-technical users and express complex goals, to an executable program that contains an intricate flow - composed of API access and control structures as loops, conditions, and sequences. To unlock the challenge of generating a complete program from a plain non-technical description we present NoviCode, a novel NL Programming task, which takes as input an API and a natural language description by a novice non-programmer and provides an executable program as output. To assess the efficacy of models on this task, we provide a novel benchmark accompanied by test suites wherein the generated program code is assessed not according to their form, but according to their functional execution. Our experiments show that, first, NoviCode is indeed a challenging task in the code synthesis domain, and that generating complex code from non-technical instructions goes beyond the current Text-to-Code paradigm. Second, we show that a novel approach wherein we align the NL utterances with the compositional hierarchical structure of the code, greatly enhances the performance of LLMs on this task, compared with the end-to-end Text-to-Code counterparts.

We derive the dimension-8 standard-model effective theory (SMEFT) contact terms relevant for vector-pair production at the LHC and lepton colliders. We first list the relevant dimension-8 massless SMEFT amplitudes, and then obtain the low-energy amplitudes using on-shell Higgsing. In all cases, the contributions we calculate are the leading-order contributions to 4-point contact-terms; the dimension-6 SMEFT merely corrects the three-point couplings entering the amplitudes. Since they are given in terms of physical quantities, namely momenta and polarizations, the results allow for a direct mapping of EFT effects to low-energy observables. The vector amplitudes are sensitive to both anomalous vector couplings and Higgs self-couplings. The left-handed fermion amplitudes feature SU(2) violating effects first generated at dimension-8. We also compare our results to HEFT predictions. Interestingly, the dimension-8 SMEFT populates almost all the novel structures generated by the dimension-8 HEFT.

Thermoelectric materials convert a temperature gradient into a voltage. This phenomenon is relatively well understood for inorganic materials, but much less so for organic semiconductors (OSs). These materials present a challenge because the strong thermal fluctuations of electronic coupling between the molecules result in partially delocalized charge carriers that cannot be treated with traditional theories for thermoelectricity. Here we develop a novel quantum dynamical simulation approach revealing in atomistic detail how the charge carrier wavefunction moves along a temperature gradient in an organic molecular crystal. We find that the wavefunction propagates from hot to cold in agreement with experiment and we obtain a Seebeck coefficient in good agreement with values obtained from experimental measurements that are also reported in this work. Detailed analysis of the dynamics reveals that the directional charge carrier motion is due to the gradient in thermal electronic disorder, more specifically in the spatial gradient of thermal fluctuations of electronic couplings. It causes an increase in the density of thermally accessible electronic states, the delocalization of states and the non-adiabatic coupling between states with decreasing temperature. As a result, the carrier wavefunction transitions with higher probability to a neighbouring electronic state towards the cold side compared to the hot side generating a thermoelectric current. Our dynamical perspective of thermoelectricity suggests that the temperature dependence of electronic disorder plays an important role in determining the magnitude of the Seebeck coefficient in this class of materials, opening new avenues for design of OSs with improved Seebeck coefficients.

The quantum Cram\'er-Rao bound for the joint estimation of the centroid and the separation between two incoherent point sources cannot be saturated. As such, the optimal measurements for extracting maximal information about both at the same time are not known. In this work, we ascertain these optimal measurements for an arbitrary point spread function, in the most relevant regime of a small separation between the sources. Our measurement can be adjusted within a set of tradeoffs, allowing more information to be extracted from the separation or the centroid while ensuring that the total information is the maximum possible., Comment: 5 pages; comments always welcome

Retrieval models are often evaluated on partially-annotated datasets. Each query is mapped to a few relevant texts and the remaining corpus is assumed to be irrelevant. As a result, models that successfully retrieve false negatives are punished in evaluation. Unfortunately, completely annotating all texts for every query is not resource efficient. In this work, we show that using partially-annotated datasets in evaluation can paint a distorted picture. We curate D-MERIT, a passage retrieval evaluation set from Wikipedia, aspiring to contain all relevant passages for each query. Queries describe a group (e.g., "journals about linguistics") and relevant passages are evidence that entities belong to the group (e.g., a passage indicating that "Language" is a journal about linguistics). We show that evaluating on a dataset containing annotations for only a subset of the relevant passages might result in misleading ranking of the retrieval systems and that as more relevant texts are included in the evaluation set, the rankings converge. We propose our dataset as a resource for evaluation and our study as a recommendation for balance between resource-efficiency and reliable evaluation when annotating evaluation sets for text retrieval., Comment: Accepted to EMNLP 2024 main track. Our dataset can be downloaded from https://D-MERIT.github.io

We investigate optimal decision making under imperfect recall, that is, when an agent forgets information it once held before. An example is the absentminded driver game, as well as team games in which the members have limited communication capabilities. In the framework of extensive-form games with imperfect recall, we analyze the computational complexities of finding equilibria in multiplayer settings across three different solution concepts: Nash, multiselves based on evidential decision theory (EDT), and multiselves based on causal decision theory (CDT). We are interested in both exact and approximate solution computation. As special cases, we consider (1) single-player games, (2) two-player zero-sum games and relationships to maximin values, and (3) games without exogenous stochasticity (chance nodes). We relate these problems to the complexity classes P, PPAD, PLS, $\Sigma_2^P$ , $\exists$R, and $\exists \forall$R., Comment: Long version of the paper that got accepted to the Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI 2024). 35 pages, 10 figures, 1 table

In this work, we develop a new hydrostatic reconstruction procedure to construct well-balanced schemes for one and multilayer shallow water flows, including wetting and drying. Initially, we derive the method for a path-conservative finite volume scheme and combine it with entropy conservative fluxes and suitable numerical dissipation to preserve an entropy inequality in the semi-discrete case. We then combine the novel hydrostatic reconstruction with a collocated nodal split-form discontinuous Galerkin spectral element method, extending the method to high-order and curvilinear meshes. The high-order method incorporates an additional positivity-limiter and is blended with a compatible subcell finite volume method to maintain well-balancedness at wet/dry fronts. We prove entropy stability, well-balancedness, and positivity-preservation for both methods. Numerical results for the high-order method validate the theoretical findings and demonstrate the robustness of the scheme., Comment: 28 pages, 10 figures, submitted to Journal of Computational Physics

Generative, multimodal artificial intelligence (GenAI) offers transformative potential across industries, but its misuse poses significant risks. Prior research has shed light on the potential of advanced AI systems to be exploited for malicious purposes. However, we still lack a concrete understanding of how GenAI models are specifically exploited or abused in practice, including the tactics employed to inflict harm. In this paper, we present a taxonomy of GenAI misuse tactics, informed by existing academic literature and a qualitative analysis of approximately 200 observed incidents of misuse reported between January 2023 and March 2024. Through this analysis, we illuminate key and novel patterns in misuse during this time period, including potential motivations, strategies, and how attackers leverage and abuse system capabilities across modalities (e.g. image, text, audio, video) in the wild.

Design fictions allow us to prototype the future. They enable us to interrogate emerging or non-existent technologies and examine their implications. We present three design fictions that probe the potential consequences of operationalizing a mutual theory of mind (MToM) between human users and one (or more) AI agents. We use these fictions to explore many aspects of MToM, including how models of the other party are shaped through interaction, how discrepancies between these models lead to breakdowns, and how models of a human's knowledge and skills enable AI agents to act in their stead. We examine these aspects through two lenses: a utopian lens in which MToM enhances human-human interactions and leads to synergistic human-AI collaborations, and a dystopian lens in which a faulty or misaligned MToM leads to problematic outcomes. Our work provides an aspirational vision for human-centered MToM research while simultaneously warning of the consequences when implemented incorrectly., Comment: 11 pages. Published in Proceedings of Workshop on Theory of Mind in Human-AI Interaction at CHI 2024

Multiple planets undergoing disk migration may be captured into a chain of mean-motion resonances with the innermost planet parked near the disk's inner edge. Subsequent dynamical evolution may disrupt these resonances, leading to the non-resonant configurations typically observed among {\it Kepler} planets that are Gyrs old. In this scenario, resonant configurations are expected to be more common in younger systems. This prediction can now be tested, thanks to recent discoveries of young planets, particularly those in stellar clusters, by NASA's {\it TESS} mission. We divided the known planetary systems into three age groups: young ($<$100-Myr-old), adolescent (0.1-1-Gyr-old), and mature ($>1$-Gyr-old). The fraction of neighboring planet pairs having period ratios within a few percent of a first-order commensurability (e.g.~4:3, 3:2, or 2:1) is 70$\pm$15\% for young pairs, 24$\pm$8\% for adolescent pairs, and 15$\pm$2\% for mature pairs. The fraction of systems with at least one nearly commensurable pair (either first or second-order) is 86$\pm13$\% among young systems, 38$\pm12$\% for adolescent systems, and 23$\pm3$\% for mature systems. First-order commensurabilities prevail across all age groups, with an admixture of second-order commensurabilities. Commensurabilities are more common in systems with high planet multiplicity and low mutual inclinations. Observed period ratios often deviate from perfect commensurability by $\sim$1\% even among young planets, too large to be explained by resonant repulsion with equilibrium eccentricity tides. We also find that super-Earths in the radius gap ($1.5-1.9R_\oplus$) are less likely to be near-resonant (11.9$\pm2.0\%$) compared to Earth-sized planets ($R_p<1R_\oplus$; 25.3$\pm4.4\%$) or mini-Neptunes ($1.9R_\oplus \leq R_p<2.5R_\oplus$; 14.4$\pm1.8\%$)., Comment: 17 pages, 9 figures, accepted to AAS

The spectral properties of electrons confined in a wire-like quasi-one-dimensional (1D) elongated quantum dot (EQD) coupler between silicon qubits, are investigated with a newly developed valley-augmented unrestricted Hartree-Fock (va-UHF) method, generalized to include the valley degree of freedom treated as an isospin, allowing calculations for a large number of electrons. The lower energy symmetry-broken solutions of the self-consistent generalized Pople-Nesbet equations exhibit, for a confinement that has been modeled after an experimentally fabricated one in silicon, formation of Wigner-molecular polymeric (longitudinal) chains, initiating through charge accumulation at the edges of the finite-length quasi-1D wire. An increasing number of parallel zig-zag chains form as the number of electrons loaded into the confinement is increased, with the formation of newly added chains determined by the strength of the transverse harmonic confinement. The broken-symmetry va-UHF solutions, subsequently augmented by the quantum-mechanically required parity-restoration, go beyond the va-UHF single-determinant solution, predicting formation of entangled Wigner-molecular chains whose charge distributions obliterate the zig-zag organization of the broken-symmetry solutions. The symmetry-restored va-UHF methodology enables systematic investigations of multi-electron complex nano-scale confined structures that could be targeted for future imaging microscopy experiments in silicon and other materials (e.g., 1D domain walls in TMD materials), and quantum information utilization., Comment: 10 pages, 10 color figures. Accepted for publication in Physical Review Applied. For related papers, see https://sites.gatech.edu/cyannouleas/

Optical surfaces represented by second-degree polynomials (quadratic or conics) are ubiquitous in optics. We revisit the equations of the conic shapes in the context of grazing incidence optics, gathering together the curves commonly used in x-ray instruments and synchrotron beamlines. We present the equations for paraboloids, ellipsoids, and hyperboloids in a common and consistent notation. We develop the transformations from centered systems that are commonly used to describe conics and their axes of symmetry, to local coordinate systems centered on the off-axis mirror surfaces. The equations presented are directly applicable to ray tracing, fabrication, and metrology calculations. They can also be used to study misalignments, movement tolerances, and aberrations of optical surfaces., Comment: 16 pages, 6 figures, 5 Tables

Earlier, we introduced Partial Quantifier Elimination (PQE). It is a $\mathit{generalization}$ of regular quantifier elimination where one can take a $\mathit{part}$ of the formula out of the scope of quantifiers. We apply PQE to CNF formulas of propositional logic with existential quantifiers. The appeal of PQE is that many problems like equivalence checking and model checking can be solved in terms of PQE and the latter can be very efficient. The main flaw of current PQE solvers is that they do not $\mathit{reuse}$ learned information. The problem here is that these PQE solvers are based on the notion of clause redundancy and the latter is a $\mathit{structural}$ rather than $\mathit{semantic}$ property. In this paper, we provide two important theoretical results that enable reusing the information learned by a PQE solver. Such reusing can dramatically boost the efficiency of PQE like conflict clause learning boosts SAT solving., Comment: arXiv admin note: text overlap with arXiv:2403.05928

Core collapse initial conditions are a bottleneck in understanding the explosion mechanism(s) of massive stars. Stellar evolution codes struggle after carbon burning, and either stop or adopt numerical simplifications missing crucial physics. The use of small nuclear reaction networks (NRN) that account for energy production but bypass weak reactions is typical, but insufficient to study the dynamics of the collapse. We advise against the use of progenitors computed with small NRN in expensive multi-dimensional simulations of core collapse, bounce, (jet formation), and explosion., Comment: accepted by RNAAS, input/output available at https://doi.org/10.5281/zenodo.11375523, code at https://github.com/mathren/CHE_net_comparison