pwc-archive/papers-with-abstracts huggingface dataset:
Solving Online Resource-Constrained Scheduling for Follow-Up Observation in Astronomy: a Reinforcement Learning Approach
Authors: YaJie Zhang, Ce Yu, Chao Sun, Jizeng Wei, Junhan Ju, Shanjiang Tang
Date: 2025-02-16 00:00:00
Tasks: Astronomy, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Scheduling, scientific discovery

In the astronomical observation field, determining the allocation of observation resources of the telescope array and planning follow-up observations for targets of opportunity (ToOs) are indispensable components of astronomical scientific discovery. This problem is computationally challenging, given the online observation setting and the abundance of time-varying factors that can affect whether an observation can be conducted. This paper presents ROARS, a reinforcement learning approach for online astronomical resource-constrained scheduling. To capture the structure of the astronomical observation scheduling, we depict every schedule using a directed acyclic graph (DAG), illustrating the dependency of timing between different observation tasks within the schedule. Deep reinforcement learning is used to learn a policy that can improve the feasible solution by iteratively local rewriting until convergence. It can solve the challenge of obtaining a complete solution directly from scratch in astronomical observation scenarios, due to the high computational complexity resulting from numerous spatial and temporal constraints. A simulation environment is developed based on real-world scenarios for experiments, to evaluate the effectiveness of our proposed scheduling approach. The experimental results show that ROARS surpasses 5 popular heuristics, adapts to various observation scenarios and learns effective strategies with hindsight.
Theory of enhanced-by-coincidence neural information transmission
Authors: Miguel Ibáñez-Berganza, Giulio Bondanelli, Stefano Panzeri
Date: 2025-02-14 00:00:00

The activity of neurons within brain circuits has been ubiquitously reported to be correlated. The impact of these correlations on brain function has been extensively investigated. Correlations can in principle increase or decrease the information that neural populations carry about sensory stimuli, but experiments in cortical areas have mostly reported information-limiting correlations, which decrease the information encoded in the population. However, a second stream of evidence suggests that temporal correlations between the spiking activity of different neurons may increase the impact of neural activity downstream, implying that temporal correlations affect both the encoding of information and its downstream readout. The principle of how encoding and readout combine are still unclear. Here, we consider a model of transmission of stimulus information encoded in pre-synaptic input spike trains with information-limiting time-correlations to the output firing of a post-synaptic biologicaly-plausible leaky integrate and fire (LIF) readout neuron. We derive an analytical solution of the model in the diffusion approximation, in which the encoding spiking activity is treated as a continuous-time stochastic variable. An ansatz based on a separation of timescales allows us compute the stimulus information transmitted to the readout over a broad range of parameters. Our analytical results reveal that, for sufficiently low input firing rates, large enough difference in input stimulus-specific activity, and moderately large input temporal correlations, the stimulus discriminability of the firing of the LIF readout neuron can be enhanced by the presence of input time correlations, despite they decrease the stimulus information encoded in its inputs.
Iterative quantum optimisation with a warm-started quantum state
Authors: Haomu Yuan, Songqinghao Yang, Crispin H. W. Barnes
Date: 2025-02-13 00:00:00

We provide a method to prepare a warm-started quantum state from measurements with an iterative framework to enhance the quantum approximate optimisation algorithm (QAOA). The numerical simulations show the method can effectively address the "stuck issue" of the standard QAOA using a single-string warm-started initial state described in [Cain et al., 2023]. When applied to the $3$-regular MaxCut problem, our approach achieves an improved approximation ratio, with a lower bound that iteratively converges toward the best classical algorithms for $p=1$ standard QAOA. Additionally, in the context of the discrete global minimal variance portfolio (DGMVP) model, simulations reveal a more favourable scaling of identifying the global minimal compared to the QAOA standalone, the single-string warm-started QAOA and a classical constrained sampling approach.
Agentic End-to-End De Novo Protein Design for Tailored Dynamics Using a Language Diffusion Model
Authors: Bo Ni, Markus J. Buehler
Date: 2025-02-14 00:00:00
Tasks: Protein Design

Proteins are dynamic molecular machines whose biological functions, spanning enzymatic catalysis, signal transduction, and structural adaptation, are intrinsically linked to their motions. Designing proteins with targeted dynamic properties, however, remains a challenge due to the complex, degenerate relationships between sequence, structure, and molecular motion. Here, we introduce VibeGen, a generative AI framework that enables end-to-end de novo protein design conditioned on normal mode vibrations. VibeGen employs an agentic dual-model architecture, comprising a protein designer that generates sequence candidates based on specified vibrational modes and a protein predictor that evaluates their dynamic accuracy. This approach synergizes diversity, accuracy, and novelty during the design process. Via full-atom molecular simulations as direct validation, we demonstrate that the designed proteins accurately reproduce the prescribed normal mode amplitudes across the backbone while adopting various stable, functionally relevant structures. Notably, generated sequences are de novo, exhibiting no significant similarity to natural proteins, thereby expanding the accessible protein space beyond evolutionary constraints. Our work integrates protein dynamics into generative protein design, and establishes a direct, bidirectional link between sequence and vibrational behavior, unlocking new pathways for engineering biomolecules with tailored dynamical and functional properties. This framework holds broad implications for the rational design of flexible enzymes, dynamic scaffolds, and biomaterials, paving the way toward dynamics-informed AI-driven protein engineering.
Physics-Informed Gaussian Process Classification for Constraint-Aware Alloy Design
Authors: Christofer Hardcastle, Ryan O Mullan, Raymundo Arroyave, Brent Vela
Date: 2025-02-17 00:00:00
Tasks: Active Learning

Alloy design can be framed as a constraint-satisfaction problem. Building on previous methodologies, we propose equipping Gaussian Process Classifiers (GPCs) with physics-informed prior mean functions to model the boundaries of feasible design spaces. Through three case studies, we highlight the utility of informative priors for handling constraints on continuous and categorical properties. (1) Phase Stability: By incorporating CALPHAD predictions as priors for solid-solution phase stability, we enhance model validation using a publicly available XRD dataset. (2) Phase Stability Prediction Refinement: We demonstrate an in silico active learning approach to efficiently correct phase diagrams. (3) Continuous Property Thresholds: By embedding priors into continuous property models, we accelerate the discovery of alloys meeting specific property thresholds via active learning. In each case, integrating physics-based insights into the classification framework substantially improved model performance, demonstrating an efficient strategy for constraint-aware alloy design.
The Vendiscope: An Algorithmic Microscope For Data Collections
Authors: Amey P. Pasarkar, Adji Bousso Dieng
Date: 2025-02-15 00:00:00
Tasks: Diversity, Formation Energy, Memorization

The evolution of microscopy, beginning with its invention in the late 16th century, has continuously enhanced our ability to explore and understand the microscopic world, enabling increasingly detailed observations of structures and phenomena. In parallel, the rise of data-driven science has underscored the need for sophisticated methods to explore and understand the composition of complex data collections. This paper introduces the Vendiscope, the first algorithmic microscope designed to extend traditional microscopy to computational analysis. The Vendiscope leverages the Vendi scores -- a family of differentiable diversity metrics rooted in ecology and quantum mechanics -- and assigns weights to data points based on their contribution to the overall diversity of the collection. These weights enable high-resolution data analysis at scale. We demonstrate this across biology, materials science, and machine learning (ML). We analyzed the $250$ million protein sequences in the protein universe, discovering that over $200$ million are near-duplicates and that AlphaFold fails on proteins with Gene Ontology (GO) functions that contribute most to diversity. Applying the Vendiscope to the Materials Project database led to similar findings: more than $85\%$ of the crystals with formation energy data are near-duplicates and ML models perform poorly on materials that enhance diversity. Additionally, the Vendiscope can be used to study phenomena such as memorization in generative models. We used the Vendiscope to identify memorized training samples from $13$ different generative models and found that the best-performing ones often memorize the training samples that contribute least to diversity. Our findings demonstrate that the Vendiscope can serve as a powerful tool for data-driven science.
Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors
Authors: Hongwei Du, Jian Hui, Lanting Zhang, Hong Wang
Date: 2025-02-14 00:00:00

With the rapid development of energy storage technology, high-performance solid-state electrolytes (SSEs) have become critical for next-generation lithium-ion batteries. These materials require high ionic conductivity, excellent electrochemical stability, and good mechanical properties to meet the demands of electric vehicles and portable electronics. However, traditional methods like density functional theory (DFT) and empirical force fields face challenges such as high computational costs, poor scalability, and limited accuracy across material systems. Universal machine learning interatomic potentials (uMLIPs) offer a promising solution with their efficiency and near-DFT-level accuracy.This study systematically evaluates six advanced uMLIP models (MatterSim, MACE, SevenNet, CHGNet, M3GNet, and ORBFF) in terms of energy, forces, thermodynamic properties, elastic moduli, and lithium-ion diffusion behavior. The results show that MatterSim outperforms others in nearly all metrics, particularly in complex material systems, demonstrating superior accuracy and physical consistency. Other models exhibit significant deviations due to issues like energy inconsistency or insufficient training data coverage.Further analysis reveals that MatterSim achieves excellent agreement with reference values in lithium-ion diffusivity calculations, especially at room temperature. Studies on Li3YCl6 and Li6PS5Cl uncover how crystal structure, anion disorder levels, and Na/Li arrangements influence ionic conductivity. Appropriate S/Cl disorder levels and optimized Na/Li arrangements enhance diffusion pathway connectivity, improving overall ionic transport performance.
Atom identification in bilayer moire materials with Gomb-Net
Authors: Austin C. Houston, Sumner B. Harris, Hao Wang, Yu-Chuan Lin, David B. Geohegan, Kai Xiao, Gerd Duscher
Date: 2025-02-13 00:00:00

Moire patterns in van der Waals bilayer materials complicate the analysis of atomic-resolution images, hindering the atomic-scale insight typically attainable with scanning transmission electron microscopy. Here, we report a method to detect the positions and identities of atoms in each of the individual layers that compose twisted bilayer heterostructures. We developed a deep learning model, Gomb-Net, which identifies the coordinates and atomic species in each layer, effectively deconvoluting the moire pattern. This enables layer-specific mapping of quantities like strain and dopant distributions, unlike other commonly used segmentation models which struggle with moire-induced complexity. Using this approach, we explored the Se atom substitutional site distribution in a twisted fractional Janus WS2-WS2(1-x)Se2x heterostructure and found that layer-specific implantation sites are unaffected by the moire pattern's local energetic or electronic modulation. This advancement enables atom identification within material regimes where it was not possible before, opening new insights into previously inaccessible material physics.
Active gel theory for cell migration with two myosin isoforms
Authors: Nils O. Winkler, Oliver M. Drozdowski, Falko Ziebert, Ulrich S. Schwarz
Date: 2025-02-17 00:00:00

Myosin II molecular motors slide actin filaments relatively to each other and are essential for force generation, motility and mechanosensing in animal cells. For non-muscle cells, evolution has resulted in three different isoforms, which have different properties concerning the motor cycle and also occur in different abundances in the cells, but their respective biological and physical roles are not fully understood. Here we use active gel theory to demonstrate the complementary roles of isoforms A and B for cell migration. We first show that our model can be derived both from coarse-graining kinetic equations and from nonequilibrium thermodynamics as the macroscopic limit of a two-component Tonks gas. We then parametrize the model and show that motile solutions exist, in which the more abundant and more dynamic isoform A is localized to the front and the stronger isoform B to the rear, in agreement with experiments. We then explore the parameter space of the model and find a general pull-and-push mechanism that can produce different migratory modes, including cell oscillations in length and velocity. We also describe an analytical solution for the stiff limit. Our findings highlight the importance of including isoform-specific molecular details to describe whole cell behavior.
Enhancing anomaly detection with topology-aware autoencoders
Authors: Vishal S. Ngairangbam, Błażej Rozwoda, Kazuki Sakurai, Michael Spannowsky
Date: 2025-02-14 00:00:00
Tasks: Anomaly Detection

Anomaly detection in high-energy physics is essential for identifying new physics beyond the Standard Model. Autoencoders provide a signal-agnostic approach but are limited by the topology of their latent space. This work explores topology-aware autoencoders, embedding phase-space distributions onto compact manifolds that reflect energy-momentum conservation. We construct autoencoders with spherical ($S^n$), product ($S^2 \otimes S^2$), and projective ($\mathbb{RP}^2$) latent spaces and compare their anomaly detection performance against conventional Euclidean embeddings. Our results show that autoencoders with topological priors significantly improve anomaly separation by preserving the global structure of the data manifold and reducing spurious reconstruction errors. Applying our approach to simulated hadronic top-quark decays, we show that latent spaces with appropriate topological constraints enhance sensitivity and robustness in detecting anomalous events. This study establishes topology-aware autoencoders as a powerful tool for unsupervised searches for new physics in particle-collision data.
Emergent functions of noise-driven spontaneous activity: Homeostatic maintenance of criticality and memory consolidation
Authors: Marumitsu Ikeda, Dai Akita, Hirokazu Takahashi
Date: 2025-02-16 00:00:00
Tasks: Computational Efficiency

Unlike digital computers, the brain exhibits spontaneous activity even during complete rest, despite the evolutionary pressure for energy efficiency. Inspired by the critical brain hypothesis, which proposes that the brain operates optimally near a critical point of phase transition in the dynamics of neural networks to improve computational efficiency, we postulate that spontaneous activity plays a homeostatic role in the development and maintenance of criticality. Criticality in the brain is associated with the balance between excitatory and inhibitory synaptic inputs (EI balance), which is essential for maintaining neural computation performance. Here, we hypothesize that both criticality and EI balance are stabilized by appropriate noise levels and spike-timing-dependent plasticity (STDP) windows. Using spiking neural network (SNN) simulations and in vitro experiments with dissociated neuronal cultures, we demonstrated that while repetitive stimuli transiently disrupt both criticality and EI balance, spontaneous activity can develop and maintain these properties and prolong the fading memory of past stimuli. Our findings suggest that the brain may achieve self-optimization and memory consolidation as emergent functions of noise-driven spontaneous activity. This noise-harnessing mechanism provides insights for designing energy-efficient neural networks, and may explain the critical function of sleep in maintaining homeostasis and consolidating memory.
Skillful Nowcasting of Convective Clouds With a Cascade Diffusion Model
Authors: Haoming Chen, Xiaohui Zhong, Qiang Zhai, Xiaomeng Li, Ying Wa Chan, Pak Wai Chan, Yuanyuan Huang, Hao Li, Xiaoming Shi
Date: 2025-02-16 00:00:00
Tasks: Video Prediction

Accurate nowcasting of convective clouds from satellite imagery is essential for mitigating the impacts of meteorological disasters, especially in developing countries and remote regions with limited ground-based observations. Recent advances in deep learning have shown promise in video prediction; however, existing models frequently produce blurry results and exhibit reduced accuracy when forecasting physical fields. Here, we introduce SATcast, a diffusion model that leverages a cascade architecture and multimodal inputs for nowcasting cloud fields in satellite imagery. SATcast incorporates physical fields predicted by FuXi, a deep-learning weather model, alongside past satellite observations as conditional inputs to generate high-quality future cloud fields. Through comprehensive evaluation, SATcast outperforms conventional methods on multiple metrics, demonstrating its superior accuracy and robustness. Ablation studies underscore the importance of its multimodal design and the cascade architecture in achieving reliable predictions. Notably, SATcast maintains predictive skill for up to 24 hours, underscoring its potential for operational nowcasting applications.
To Bin or not to Bin: Alternative Representations of Mass Spectra
Authors: Niek de Jonge, Justin J. J. van der Hooft, Daniel Probst
Date: 2025-02-15 00:00:00
Tasks: Diversity, Graph Neural Network

Mass spectrometry, especially so-called tandem mass spectrometry, is commonly used to assess the chemical diversity of samples. The resulting mass fragmentation spectra are representations of molecules of which the structure may have not been determined. This poses the challenge of experimentally determining or computationally predicting molecular structures from mass spectra. An alternative option is to predict molecular properties or molecular similarity directly from spectra. Various methodologies have been proposed to embed mass spectra for further use in machine learning tasks. However, these methodologies require preprocessing of the spectra, which often includes binning or sub-sampling peaks with the main reasoning of creating uniform vector sizes and removing noise. Here, we investigate two alternatives to the binning of mass spectra before down-stream machine learning tasks, namely, set-based and graph-based representations. Comparing the two proposed representations to train a set transformer and a graph neural network on a regression task, respectively, we show that they both perform substantially better than a multilayer perceptron trained on binned data.
Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction
Authors: Xiang Fu, Brandon M. Wood, Luis Barroso-Luque, Daniel S. Levine, Meng Gao, Misko Dzamba, C. Lawrence Zitnick
Date: 2025-02-17 00:00:00
Tasks: Prediction, Property Prediction

Machine learning interatomic potentials (MLIPs) have become increasingly effective at approximating quantum mechanical calculations at a fraction of the computational cost. However, lower errors on held out test sets do not always translate to improved results on downstream physical property prediction tasks. In this paper, we propose testing MLIPs on their practical ability to conserve energy during molecular dynamic simulations. If passed, improved correlations are found between test errors and their performance on physical property prediction tasks. We identify choices which may lead to models failing this test, and use these observations to improve upon highly-expressive models. The resulting model, eSEN, provides state-of-the-art results on a range of physical property prediction tasks, including materials stability prediction, thermal conductivity prediction, and phonon calculations.
PreAdaptFWI: Pretrained-Based Adaptive Residual Learning for Full-Waveform Inversion Without Dataset Dependency
Authors: Xintong Dong, Zhengyi Yuan, Jun Lin, Shiqi Dong, Xunqian Tong, Yue Li
Date: 2025-02-17 00:00:00
Tasks: Transfer Learning

Full-waveform inversion (FWI) is a method that utilizes seismic data to invert the physical parameters of subsurface media by minimizing the difference between simulated and observed waveforms. Due to its ill-posed nature, FWI is susceptible to getting trapped in local minima. Consequently, various research efforts have attempted to combine neural networks with FWI to stabilize the inversion process. This study presents a simple yet effective training framework that is independent of dataset reliance and requires only moderate pre-training on a simple initial model to stabilize network outputs. During the transfer learning phase, the conventional FWI gradients will simultaneously update both the neural network and the proposed adaptive residual learning module, which learns the residual mapping of large-scale distribution features in the network's output, rather than directly fitting the target mapping. Through this synergistic training paradigm, the proposed algorithm effectively infers the physically-informed prior knowledge into a global representation of stratigraphic distribution, as well as capturing subtle variations in inter-layer velocities within local details, thereby escaping local optima. Evaluating the method on two benchmark models under various conditions, including absent low-frequency data, noise interference, and differing initial models, along with corresponding ablation experiments, consistently demonstrates the superiority of the proposed approach.