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AI programming tools enable powerful code generation, and recent prototypes attempt to reduce user effort with proactive AI agents, but their impact on programming workflows remains unexplored. We introduce and evaluate Codellaborator, a design probe LLM agent that initiates programming assistance based on editor activities and task context. We explored three interface variants to assess trade-offs between increasingly salient AI support: prompt-only, proactive agent, and proactive agent with presence and context (Codellaborator). In a within-subject study (N=18), we find that proactive agents increase efficiency compared to prompt-only paradigm, but also incur workflow disruptions. However, presence indicators and \revise{interaction context support} alleviated disruptions and improved users' awareness of AI processes. We underscore trade-offs of Codellaborator on user control, ownership, and code understanding, emphasizing the need to adapt proactivity to programming processes. Our research contributes to the design exploration and evaluation of proactive AI systems, presenting design implications on AI-integrated programming workflow.

We present an approach to model-based RL that achieves a new state of the art performance on the challenging Craftax-classic benchmark, an open-world 2D survival game that requires agents to exhibit a wide range of general abilities -- such as strong generalization, deep exploration, and long-term reasoning. With a series of careful design choices aimed at improving sample efficiency, our MBRL algorithm achieves a reward of 67.4% after only 1M environment steps, significantly outperforming DreamerV3, which achieves 53.2%, and, for the first time, exceeds human performance of 65.0%. Our method starts by constructing a SOTA model-free baseline, using a novel policy architecture that combines CNNs and RNNs. We then add three improvements to the standard MBRL setup: (a) "Dyna with warmup", which trains the policy on real and imaginary data, (b) "nearest neighbor tokenizer" on image patches, which improves the scheme to create the transformer world model (TWM) inputs, and (c) "block teacher forcing", which allows the TWM to reason jointly about the future tokens of the next timestep.

We present an analytical model for density-split correlation functions, that probe galaxy clustering in different density environments. Specifically, we focus on the cross-correlation between density-split regions and the tracer density field. We show that these correlation functions can be expressed in terms of the two-point probability density function (PDF) of the density field, or equivalently, its bias function. We derive analytical predictions using three levels of approximation for the two-point PDF: a bivariate Gaussian distribution, a bivariate shifted log-normal distribution, and a prediction based on the Large Deviation Theory framework. Under spherical symmetry $\unicode{x2013}$ where spherical collapse dynamics apply, such as for count-in-cell densities with spherical top-hat smoothing $\unicode{x2013}$ LDT predicts the density two-point PDF in the large-separation regime relative to the smoothing radius. We validate our model against dark matter N-body simulations in real space, incorporating Poisson shot noise. Our results show that the LDT predictions outperform the log-normal approximation, and agrees with simulations on large scales within the cosmic variance of a typical DESI DR1 sample, despite relying on only one degree of freedom., Comment: 28 pages, 12 figures

Foundation models are becoming increasingly popular due to their strong generalization capabilities resulting from being trained on huge datasets. These generalization capabilities are attractive in areas such as NIR Iris Presentation Attack Detection (PAD), in which databases are limited in the number of subjects and diversity of attack instruments, and there is no correspondence between the bona fide and attack images because, most of the time, they do not belong to the same subjects. This work explores an iris PAD approach based on two foundation models, DinoV2 and VisualOpenClip. The results show that fine-tuning prediction with a small neural network as head overpasses the state-of-the-art performance based on deep learning approaches. However, systems trained from scratch have still reached better results if bona fide and attack images are available.

For the first time, we develop a simulation-based model for the Minkowski functionals (MFs) of large-scale structure, which allows us to extract the full information available from the MFs (including both the Gaussian and non-Gaussian part), and apply it to the BOSS DR12 CMASS galaxy sample. Our model is based on high-fidelity mock galaxy catalogs constructed from the \textsc{Abacus}\textsc{Summit} simulations using the halo occupation distribution (HOD) framework, which include the redshift-space distortions and Alcock-Paczynski distortions, incorporate survey realism, including survey geometry and veto masks, and account for angular plus radial selection effects. The cosmological and HOD parameter dependence of the MFs is captured with a neural network emulator trained from the galaxy mocks with various cosmological and HOD parameters. To benchmark the constraining power of the MFs, we also train an emulator for the galaxy 2-point correlation function (2PCF) using the same pipeline. Having validated our approach through successful parameter recovery tests on both internal and external mocks, including non-HOD forward models of the halo-galaxy connection, we apply our forward model to analyze the CMASS data in the redshift range $0.45

CuInS2 quantum dots have been studied in a broad range of applications, but despite this, the fine details of their charge carrier dynamics remain a subject of intense debate. Two of the most relevant points of discussion are the hole dynamics and the influence of Cu:In synthesis stoichiometry on them. It has been proposed that Cu-deficiency leads to the formation of Cu2+, affecting the localization of holes into Cu defects. Importantly, it is precisely these confined hole states which are used to explain the interesting photoluminescence properties of CuInS2 quantum dots. We use static X-ray spectroscopy to reveal no evidence for a measurable amount of native Cu2+ states in Cu-deficient samples. Instead, the improved properties of these samples are explained by an increase of crystallinity, reducing the concentration of mid gap states. Furthermore, to understand the charge carrier dynamics, herein we employ ultrafast optical transient absorption, and fluorescence up-conversion spectroscopies in combination with ultrafast X-ray absorption spectroscopy using a hard X-ray free electron laser. We demonstrate that in non-passivated samples, holes are transferred from Cu atoms in sub-picosecond timescales. We assign this transfer to occur towards the thiol-based ligands. Finally, we observe that Cu-deficient samples are more robust against the photothermal heating effects of using higher laser fluences. This is not the case for the stoichiometric sample, where heating effects on the structure are directly observed.

Cosmological analyses of redshift space clustering data are primarily based on using luminous ``red'' galaxies (LRGs) and ``blue'' emission line galaxies (ELGs) to trace underlying dark matter. Using the large high-fidelity high-resolution MillenniumTNG (MTNG) and Astrid simulations, we study these galaxies with the effective field theory (EFT)-based field level forward model. We confirm that both red and blue galaxies can be accurately modeled with EFT at the field level and their parameters match those of the phenomenological halo-based models. Specifically, we consider the state of the art Halo Occupation Distribution (HOD) and High Mass Quenched (HMQ) models for the red and blue galaxies, respectively. Our results explicitly confirm the validity of the halo-based models on large scales beyond the two-point statistics. In addition, we validate the field-level HOD/HMQ-based priors for EFT full-shape analysis. We find that the local bias parameters of the ELGs are in tension with the predictions of the LRG-like HOD models and present a simple analytic argument explaining this phenomenology. We also confirm that ELGs exhibit weaker non-linear redshift-space distortions (``fingers-of-God''), suggesting that a significant fraction of their data should be perturbative. We find that the response of EFT parameters to galaxy selection is sensitive to assumptions about baryonic feedback, suggesting that a detailed understanding of feedback processes is necessary for robust predictions of EFT parameters. Finally, using neural density estimation based on paired HOD-EFT parameter samples, we obtain optimal HOD models that reproduce the clustering of Astrid and MTNG galaxies., Comment: 27 pages, 16 figures

Understanding how viral mutant spectra organize and explore genotype space is essential for unraveling the mechanisms driving evolution at the finest scale. Here we use deep-sequencing data of an amplicon in the A2 protein of the RNA bacteriophage Q$\beta$ to reconstruct genotype networks with tens of thousands of different haplotypes. The study of populations evolved under different temperature regimes uncovers generic topological features conditioned by fundamental structural motifs of genotype networks -- tetrahedrons, triangles, and squares -- that govern their local architecture. Mutant swarms display a hierarchical structure where sequences cluster around a highly connected and abundant sequence core that sustains population diversity. The immediate neighborhood of this core is comprehensively sampled, with no signs of selection, while a few mutations away sampling becomes dynamical and sparse, showing signs of purifying selection. By aggregating genotype networks from populations adapted to different temperatures, we capture the early stages of evolutionary divergence, with overlapping populations that remain connected through short mutational paths. Even at the time scale of these experiments, evolutionary pathways might be multiple, preventing the backward reconstruction of unique trajectories once mutations have been fixed. This analysis provides a detailed view of the local, fine-scale processes shaping viral quasispecies evolution and underscores the usefulness of genotype networks as an enlightening visualization of the organization of mutant swarms., Comment: 15 pages (main), 8 pages (appendixes), 4 figures (main), 6 figures (appendixes)

Interoperability is the key to implementing a wide range of energy systems applications. It involves the seamless cooperation of different methods and components. With smart energy systems, interoperability faces challenges due to integrating differ-ent approaches and technologies. This includes dealing with heterogeneous approaches with various communication proto-cols and data formats. However, it is essential for smart energy systems to carry out thorough interoperability tests. They are usually diverse, and challenging, thus requiring careful consideration of compatibility issues and complex integration scenari-os. Overcoming these challenges requires a systematic approach that includes thorough test planning, rigorous testing, and continuous test monitoring. Although numerous testing approaches exist, most are more developed at the component/device level than at the system level. Consequently, there are few approaches and related facilities to test the interoperability of smart energy approaches and solutions at the system level. This work analyses existing interoperability test concepts, identi-fies enablers and the potential for harmonisation of procedures, and proposes further developments of these approaches., Comment: 14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion (MED POWER 2024)

The dynamics of wild-type (wt) RNA viruses and their defective viral genomes (DVGs) have been extensively studied both experimentally and theoretically. This research has paid special attention to the interference effects of DVGs on wt accumulation, transmission, disease severity, and induction of immunological responses. This subject is currently a highly active. However, viral infections involving wt, DVGs and other subviral genetic elements, like viral RNA satellites (satRNAs) have received scarce attention. Satellites are molecular parasites genetically different from the wt virus, which exploit the products of the latter for their own replication in as much as DVGs do, and thus they need to coinfect host cells along with the wt virus to complete their replication cycle. Here, we analyze a mathematical model describing the initial replication phase of a wt virus producing DVGs and coinfecting with a satRNA. The model has three different dynamical regimes depending upon the wt replication rate ($\alpha$), the fraction of DVGs produced during replication ($\omega$), and the replication rate of the satRNA ($\beta$): ($i$) full extinction when $\beta > \alpha (1 - \omega)$; ($ii$) a bistable regime with full coexistence governed by a quasi-neutral curve of equilibria and full extinction when $\beta = \alpha (1 - \omega)$; and ($iii$) a scenario of bistability separating full extinction from wt-DVGs coexistence with no satRNA when $\beta < \alpha (1 - \omega)$. The transition from scenarios ($i$) to ($iii$) occurs through the creation and destruction of a quasi-neutral curve of equilibria in a global bifurcation that we name as \textit{quasi-neutral nullcline confluence} (QNC) bifurcation: at the bifurcation value, two nullcline hypersurfaces coincide, giving rise to the curve of equilibria.

Efficiently processing structured point cloud data while preserving multiscale information is a key challenge across domains, from graphics to atomistic modeling. Using a curated dataset of simulated galaxy positions and properties, represented as point clouds, we benchmark the ability of graph neural networks to simultaneously capture local clustering environments and long-range correlations. Given the homogeneous and isotropic nature of the Universe, the data exhibits a high degree of symmetry. We therefore focus on evaluating the performance of Euclidean symmetry-preserving ($E(3)$-equivariant) graph neural networks, showing that they can outperform non-equivariant counterparts and domain-specific information extraction techniques in downstream performance as well as simulation-efficiency. However, we find that current architectures fail to capture information from long-range correlations as effectively as domain-specific baselines, motivating future work on architectures better suited for extracting long-range information., Comment: 19 pages, 3 figures; To appear at the NeurReps Workshop @ NeurIPS 2024

We report a novel approach based on the non-equilibrium self-consistent generalized Langevin equation (NESCGLE) theory that allows for the first principles prediction of the zero-shear viscosity in glass- and- gel-forming materials. This new modulus of the NESCGLE theory facilitates the theoretical description and interpretation of experimental data concerning out-of-equilibrium rheological properties of viscous liquids during their amorphous solidification. The predictive capability of our approach is illustrated here by means of a quantitative comparison between theoretical and experimental results for the zero shear viscosity in suspensions of oligomer-tethered nanoparticles in a polymeric host, finding an almost perfect correspondence between experiments and theory. This comparison also highlights the crucial relevance of including a kinetic perspective, such as that provided by the NESCGLE theory, in the description of dynamic and viscoelastic properties of amorphous states of matter.

We propose Diffusion Model Predictive Control (D-MPC), a novel MPC approach that learns a multi-step action proposal and a multi-step dynamics model, both using diffusion models, and combines them for use in online MPC. On the popular D4RL benchmark, we show performance that is significantly better than existing model-based offline planning methods using MPC and competitive with state-of-the-art (SOTA) model-based and model-free reinforcement learning methods. We additionally illustrate D-MPC's ability to optimize novel reward functions at run time and adapt to novel dynamics, and highlight its advantages compared to existing diffusion-based planning baselines., Comment: Preprint

In this work, we present a comprehensive method for determining the dispersion relation in one-dimensional waveguides, applicable to a wide range of structures, regardless of their specific characteristics. This general framework employs a unified mathematical model, referred to as *generalized beams*, where varying types of waveguides and scatterers are incorporated by adjusting certain parameters. The generalized beam consists of a host waveguide, which is a homogeneous elastic structure with physical properties represented in matrix form. The model also accounts for an arbitrary set of scatterers within the unit cell, including resonators (discrete and continuous), small material inclusions, or variations in cross-sectional area. Based on general assumptions, we develop a matrix-based algorithm utilizing the plane wave expansion method to derive the solution. Additionally, we propose an iterative procedure that provides analytical expressions for the first- and second-order terms, particularly useful in the context of weak scattering. The convergence conditions of the method are rigorously defined. The approach's effectiveness is demonstrated through several numerical examples, highlighting its versatility in different waveguide configurations and scattering scenarios.

This work reports the conditions under which weak scattering assumptions can be applied in a beam loaded by multiple resonators supporting both longitudinal and flexural waves. The work derives the equations of motion of a one-dimensional elastic waveguide with several point resonators by utilizing the Green's matrix approach. The derivations include any resonator morphology, either with a discrete or continuous distribution of resonances. The method employed is based on applying multiple scattering theory. The response can be expressed as an infinite series whose convergence is closely linked to the scattering intensity provided by the resonators. The convergence conditions are reduced to studying the spectral radius of the scattering matrix. Furthermore, the the leading order of the multiple scattering expansion is associated with the Born approximation. The work also provides approximate expressions for the spectral radius, offering a physical interpretation to the concept of weak scattering in beams. Several numerical examples are presented to validate the proposed methodology.

Learning from previously collected data via behavioral cloning or offline reinforcement learning (RL) is a powerful recipe for scaling generalist agents by avoiding the need for expensive online learning. Despite strong generalization in some respects, agents are often remarkably brittle to minor visual variations in control-irrelevant factors such as the background or camera viewpoint. In this paper, we present theDeepMind Control Visual Benchmark (DMC-VB), a dataset collected in the DeepMind Control Suite to evaluate the robustness of offline RL agents for solving continuous control tasks from visual input in the presence of visual distractors. In contrast to prior works, our dataset (a) combines locomotion and navigation tasks of varying difficulties, (b) includes static and dynamic visual variations, (c) considers data generated by policies with different skill levels, (d) systematically returns pairs of state and pixel observation, (e) is an order of magnitude larger, and (f) includes tasks with hidden goals. Accompanying our dataset, we propose three benchmarks to evaluate representation learning methods for pretraining, and carry out experiments on several recently proposed methods. First, we find that pretrained representations do not help policy learning on DMC-VB, and we highlight a large representation gap between policies learned on pixel observations and on states. Second, we demonstrate when expert data is limited, policy learning can benefit from representations pretrained on (a) suboptimal data, and (b) tasks with stochastic hidden goals. Our dataset and benchmark code to train and evaluate agents are available at: https://github.com/google-deepmind/dmc_vision_benchmark., Comment: NeurIPS 2024 Datasets and Benchmarks Track. Dataset available at: https://github.com/google-deepmind/dmc_vision_benchmark

We prove that under some extra hypothesis, given an \'etale endomorphism of a normal irreducible Noetherian and simply connected scheme, if the endomorphism is surjective then it is injective. The additional assumption concerns the possibility of constructing an \'etale cover out of a surjective \'etale morphism. We show that in some cases the surjectivity hypothesis can be removed if the intended \'etale cover is Galois., Comment: Theorem 2 of the previous version is incorrect

We explore full-shape analysis with simulation-based priors, which is the simplest approach to galaxy clustering data analysis that combines effective field theory (EFT) on large scales and numerical simulations on small scales. The core ingredient of our approach is the prior density of EFT parameters which we extract from a suite of 10500 galaxy simulations based on the halo occupation distribution (HOD) model. We measure the EFT parameters with the field-level forward model, which enables us to cancel cosmic variance. On the theory side, we develop a new efficient approach to calculate field-level transfer functions using time-sliced perturbation theory and the logarithmic fast Fourier transform. We study cosmology dependence of EFT parameters of dark matter halos and HOD galaxies and find that it can be ignored for the purpose of prior generation. We use neural density estimation to model the measured distribution of EFT parameters. Our distribution model is then used as a prior in a reanalysis of the BOSS full-shape galaxy power spectrum data. Assuming the $\Lambda$CDM model, we find significant ($\approx 30\%$ and $\approx 60\%$) improvements for the matter density fraction and the mass fluctuation amplitude, which are constrained to $\Omega_m= 0.315 \pm 0.010$ and $\sigma_8 = 0.671 \pm 0.027$. The value of the Hubble constant does not change, $H_0= 68.7\pm 1.1$ km/s/Mpc. This reaffirms earlier reports of the structure growth tension from the BOSS data. Finally, we use the measured EFT parameters to constrain galaxy formation physics., Comment: 40 pages, 14 figs