Lowest Rated Papers
Rotating five-dimensional electrically charged Bardeen regular black holes
Muhammed Amir, Md Sabir Ali, Sunil D. Maharaj
arXiv·2020
We derive a rotating counterpart of the five-dimensional electrically charged Bardeen regular black holes spacetime by employing the Giampieri algorithm on static one. The associated nonlinear electrodynamics source is computed in order to justify the rotating solution. We thoroughly discuss the energy conditions and the other properties of the rotating spacetime. The black hole thermodynamics of the rotating spacetime is also presented. In particular, the thermodynamic quantities such as the Hawking temperature and the heat capacity are calculated and plotted to see the thermal behavior. The Hawking temperature profile of the black hole implies that the regular black holes are thermally colder than its singular counterpart. On the other hand, we find that the heat capacity has two branches: the negative branch corresponds to the unstable phase and the positive branch corresponds to that of the stable phase for a suitable choice of the physical parameters characterizing the black holes.
No ratings yet
View paper →Nonlinear magnetically charged black holes in 4D Einstein-Gauss-Bonnet gravity
Kimet Jusufi
arXiv·2020
In this letter we present an exact spherically symmetric and magnetically charged black hole solution with exponential model of nonlinear electrodynamics [S. Kruglov, Annals Phys. 378, 59-70 (2017)] in the context of 4D Einstein-Gauss-Bonnet (EGB) gravity. We show that our $-$ve branch, in the limit of GB coupling coefficient $α\rightarrow 0$ and the nonlinear parameter $β\to 0$, reduces to the magnetically charged black hole of Einstein-Maxwell gravity in GR. In addition we study the embedding diagram of the black hole geometry and the thermodynamic properties such as the Hawking temperature and the heat capacity of our black hole solution.
No ratings yet
View paper →Constraining the tidal charge of brane black holes using their shadows
Juliano C. S. Neves
arXiv·2020
A constraint on the tidal charge generated within a brane world is shown. Using the shadow of a rotating black hole in a brane context in order to describe the M87* parameters recently announced by the Event Horizon Telescope Collaboration, the deviation from circularity of the reported shadow produces an upper bound on the bulk's nonlocal effect, which is conceived of as a tidal charge in the four-dimensional brane induced by the five-dimensional bulk. Therefore, a deviation from circularity $\lesssim 10\%$ leads to an upper bound on the tidal charge $\lesssim 0.004M^2$.
No ratings yet
View paper →Introducing Claude 4
Anthropic
Anthropic Blog·2025
Discover Claude 4's breakthrough AI capabilities. Experience more reliable, interpretable assistance for complex tasks across work and learning.
No ratings yet
View paper →Leveraging the Bias‐Variance Tradeoff in Quantum Chemistry for Accurate Negative Singlet‐Triplet Gap Predictions: A Case for Double‐Hybrid DFT
Atreyee Majumdar, Raghunathan Ramakrishnan
Journal of Computational Chemistry·2025
<jats:title>ABSTRACT</jats:title><jats:p>Molecules that have been suggested to violate the Hund's rule, having a first excited singlet state () energetically below the triplet state (), are rare. Yet, they hold the promise to be efficient light emitters. Their high‐throughput identification demands exceptionally accurate excited‐state modeling to minimize qualitatively wrong predictions. We benchmark twelve energy gaps to find that the local‐correlated versions of ADC(2) and CC2 excited state methods deliver excellent accuracy and speed for screening medium‐sized molecules. Notably, we find that double‐hybrid DFT approximations (e.g., B2GP‐PLYP and PBE‐QIDH) exhibit high mean absolute errors () despite very low standard deviations (). Exploring their parameter space reveals that a configuration with 75% exchange and 55% correlation, which reduces the mean absolute error to below 5 meV, but with an increased variance. Using this low‐bias parameterization as an internal reference, we correct the systematic error while maintaining low variance, effectively combining the strengths of both low‐bias and low‐variance DFT parameterizations to enhance overall accuracy. Our findings suggest that low‐variance DFT methods, often overlooked due to their high bias, can serve as reliable tools for predictive modeling in first‐principles molecular design. The bias‐correction data‐fitting procedure can be applied to any general problem where two flavors of a method, one with low bias and another with low variance, have been identified a priori.</jats:p>
No ratings yet
View paper →From binary to synergy in precision mRNA therapeutics: Dual-microRNA-sensing mRNAs for modular logic control of protein expression
Chermaine Tan, Sherry Aw
Molecular Therapy Nucleic Acids·2025
No abstract available
No ratings yet
View paper →On Evolution-Based Models for Experimentation Under Interference
Sadegh Shirani, Mohsen Bayati
arXiv·2025
Causal effect estimation in networked systems is central to data-driven decision making. In such settings, interventions on one unit can spill over to others, and in complex physical or social systems, the interaction pathways driving these interference structures remain largely unobserved. We argue that for identifying population-level causal effects, it is not necessary to recover the exact network structure; instead, it suffices to characterize how those interactions contribute to the evolution of outcomes. Building on this principle, we study an evolution-based approach that investigates how outcomes change across observation rounds in response to interventions, hence compensating for missing network information. Using an exposure-mapping perspective, we give an axiomatic characterization of when the empirical distribution of outcomes follows a low-dimensional recursive equation, and identify minimal structural conditions under which such evolution mappings exist. We frame this as a distributional counterpart to difference-in-differences. Rather than assuming parallel paths for individual units, it exploits parallel evolution patterns across treatment scenarios to estimate counterfactual trajectories. A key insight is that treatment randomization plays a role beyond eliminating latent confounding; it induces an implicit sampling from hidden interference channels, enabling consistent learning about heterogeneous spillover effects. We highlight causal message passing as an instantiation of this method in dense networks while extending to more general interference structures, including influencer networks where a small set of units drives most spillovers. Finally, we discuss the limits of this approach, showing that strong temporal trends or endogenous interference can undermine identification.
No ratings yet
View paper →Can We Validate Counterfactual Estimations in the Presence of General Network Interference?
Sadegh Shirani, Yuwei Luo, William Overman, Ruoxuan Xiong, Mohsen Bayati
arXiv·2025
Randomized experiments have become a cornerstone of evidence-based decision-making in contexts ranging from online platforms to public health. However, in experimental settings with network interference, a unit's treatment can influence outcomes of other units, challenging both causal effect estimation and its validation. Classic validation approaches fail as outcomes are only observable under a single treatment scenario and exhibit complex correlation patterns due to interference. To address these challenges, we introduce a framework that facilitates the use of machine learning tools for both estimation and validation in causal inference. Central to our approach is the new distribution-preserving network bootstrap, a theoretically-grounded technique that generates multiple statistically-valid subpopulations from a single experiment's data. This amplification of experimental samples enables our second contribution: a counterfactual cross-validation procedure. This procedure adapts the principles of model validation to the unique constraints of causal settings, providing a rigorous, data-driven method for selecting and evaluating estimators. We extend recent causal message-passing developments by incorporating heterogeneous unit-level characteristics and varying local interactions, ensuring reliable finite-sample performance through non-asymptotic analysis. Additionally, we develop and publicly release a comprehensive benchmark toolbox featuring diverse experimental environments, from networks of interacting AI agents to ride-sharing applications. These environments provide known ground truth values while maintaining realistic complexities, enabling systematic evaluation of causal inference methods. Extensive testing across these environments demonstrates our method's robustness to diverse forms of network interference.
No ratings yet
View paper →Higher-Order Causal Message Passing for Experimentation with Complex Interference
Mohsen Bayati, Yuwei Luo, William Overman, Sadegh Shirani, Ruoxuan Xiong
arXiv·2024
Accurate estimation of treatment effects is essential for decision-making across various scientific fields. This task, however, becomes challenging in areas like social sciences and online marketplaces, where treating one experimental unit can influence outcomes for others through direct or indirect interactions. Such interference can lead to biased treatment effect estimates, particularly when the structure of these interactions is unknown. We address this challenge by introducing a new class of estimators based on causal message-passing, specifically designed for settings with pervasive, unknown interference. Our estimator draws on information from the sample mean and variance of unit outcomes and treatments over time, enabling efficient use of observed data to estimate the evolution of the system state. Concretely, we construct non-linear features from the moments of unit outcomes and treatments and then learn a function that maps these features to future mean and variance of unit outcomes. This allows for the estimation of the treatment effect over time. Extensive simulations across multiple domains, using synthetic and real network data, demonstrate the efficacy of our approach in estimating total treatment effect dynamics, even in cases where interference exhibits non-monotonic behavior in the probability of treatment.
No ratings yet
View paper →Causal Message Passing for Experiments with Unknown and General Network Interference
Sadegh Shirani, Mohsen Bayati
arXiv·2023
Randomized experiments are a powerful methodology for data-driven evaluation of decisions or interventions. Yet, their validity may be undermined by network interference. This occurs when the treatment of one unit impacts not only its outcome but also that of connected units, biasing traditional treatment effect estimations. Our study introduces a new framework to accommodate complex and unknown network interference, moving beyond specialized models in the existing literature. Our framework, termed causal message-passing, is grounded in high-dimensional approximate message passing methodology. It is tailored for multi-period experiments and is particularly effective in settings with many units and prevalent network interference. The framework models causal effects as a dynamic process where a treated unit's impact propagates through the network via neighboring units until equilibrium is reached. This approach allows us to approximate the dynamics of potential outcomes over time, enabling the extraction of valuable information before treatment effects reach equilibrium. Utilizing causal message-passing, we introduce a practical algorithm to estimate the total treatment effect, defined as the impact observed when all units are treated compared to the scenario where no unit receives treatment. We demonstrate the effectiveness of this approach across five numerical scenarios, each characterized by a distinct interference structure.
No ratings yet
View paper →Deep Learning: A Critical Appraisal
Gary Marcus
arXiv·2018
Although deep learning has historical roots going back decades, neither the term "deep learning" nor the approach was popular just over five years ago, when the field was reignited by papers such as Krizhevsky, Sutskever and Hinton's now classic (2012) deep network model of Imagenet. What has the field discovered in the five subsequent years? Against a background of considerable progress in areas such as speech recognition, image recognition, and game playing, and considerable enthusiasm in the popular press, I present ten concerns for deep learning, and suggest that deep learning must be supplemented by other techniques if we are to reach artificial general intelligence.
No ratings yet
View paper →AI Assertion
Patrick Butlin, Emanuel Viebahn
2023
Modern generative AI systems have shown the capacity to produce remarkably fluent language, prompting debates both about their semantic understanding and, less prominently, about whether they can perform speech acts. This paper addresses the latter question, focusing on assertion. We argue that to be capable of assertion, an entity must meet two requirements: it must produce outputs with descriptive functions, and it must be capable of being sanctioned by agents with which it interacts. The second requirement arises from the nature of assertion as a norm-governed social practice. Pre-trained large language models that have not been subject to fine-tuning fail to meet the first requirement. Language models that have been fine-tuned for ‘groundedness’ or ‘correctness’ may meet the first requirement, but fail the second. We also consider the significance of the point that AI systems can be used to generate proxy assertions on behalf of human agents.
No ratings yet
View paper →