TY - JOUR AB - Reaching a high cavity population with a coherent pump in the strong-coupling regime of a single-atom laser is impossible due to the photon blockade effect. In this Letter, we experimentally demonstrate that in a single-atom maser based on a transmon strongly coupled to two resonators, it is possible to pump over a dozen photons into the system. The first high-quality resonator plays the role of a usual lasing cavity, and the second one presents a controlled dissipation channel, bolstering population inversion, and modifies the energy-level structure to lift the blockade. As confirmation of the lasing action, we observe conventional laser features such as a narrowing of the emission linewidth and external signal amplification. Additionally, we report unique single-atom features: self-quenching and several lasing thresholds. AU - Sokolova, Alesya AU - Kalacheva, D. A. AU - Fedorov, G. P. AU - Astafiev, O. V. ID - 12819 IS - 3 JF - Physical Review A SN - 2469-9926 TI - Overcoming photon blockade in a circuit-QED single-atom maser with engineered metastability and strong coupling VL - 107 ER - TY - JOUR AB - The field of indirect reciprocity investigates how social norms can foster cooperation when individuals continuously monitor and assess each other’s social interactions. By adhering to certain social norms, cooperating individuals can improve their reputation and, in turn, receive benefits from others. Eight social norms, known as the “leading eight," have been shown to effectively promote the evolution of cooperation as long as information is public and reliable. These norms categorize group members as either ’good’ or ’bad’. In this study, we examine a scenario where individuals instead assign nuanced reputation scores to each other, and only cooperate with those whose reputation exceeds a certain threshold. We find both analytically and through simulations that such quantitative assessments are error-correcting, thus facilitating cooperation in situations where information is private and unreliable. Moreover, our results identify four specific norms that are robust to such conditions, and may be relevant for helping to sustain cooperation in natural populations. AU - Schmid, Laura AU - Ekbatani, Farbod AU - Hilbe, Christian AU - Chatterjee, Krishnendu ID - 12861 JF - Nature Communications TI - Quantitative assessment can stabilize indirect reciprocity under imperfect information VL - 14 ER - TY - JOUR AB - Despite the considerable progress of in vivo neural recording techniques, inferring the biophysical mechanisms underlying large scale coordination of brain activity from neural data remains challenging. One obstacle is the difficulty to link high dimensional functional connectivity measures to mechanistic models of network activity. We address this issue by investigating spike-field coupling (SFC) measurements, which quantify the synchronization between, on the one hand, the action potentials produced by neurons, and on the other hand mesoscopic “field” signals, reflecting subthreshold activities at possibly multiple recording sites. As the number of recording sites gets large, the amount of pairwise SFC measurements becomes overwhelmingly challenging to interpret. We develop Generalized Phase Locking Analysis (GPLA) as an interpretable dimensionality reduction of this multivariate SFC. GPLA describes the dominant coupling between field activity and neural ensembles across space and frequencies. We show that GPLA features are biophysically interpretable when used in conjunction with appropriate network models, such that we can identify the influence of underlying circuit properties on these features. We demonstrate the statistical benefits and interpretability of this approach in various computational models and Utah array recordings. The results suggest that GPLA, used jointly with biophysical modeling, can help uncover the contribution of recurrent microcircuits to the spatio-temporal dynamics observed in multi-channel experimental recordings. AU - Safavi, Shervin AU - Panagiotaropoulos, Theofanis I. AU - Kapoor, Vishal AU - Ramirez Villegas, Juan F AU - Logothetis, Nikos K. AU - Besserve, Michel ID - 12862 IS - 4 JF - PLoS Computational Biology TI - Uncovering the organization of neural circuits with Generalized Phase Locking Analysis VL - 19 ER - TY - JOUR AB - Machine learning (ML) has been widely applied to chemical property prediction, most prominently for the energies and forces in molecules and materials. The strong interest in predicting energies in particular has led to a ‘local energy’-based paradigm for modern atomistic ML models, which ensures size-extensivity and a linear scaling of computational cost with system size. However, many electronic properties (such as excitation energies or ionization energies) do not necessarily scale linearly with system size and may even be spatially localized. Using size-extensive models in these cases can lead to large errors. In this work, we explore different strategies for learning intensive and localized properties, using HOMO energies in organic molecules as a representative test case. In particular, we analyze the pooling functions that atomistic neural networks use to predict molecular properties, and suggest an orbital weighted average (OWA) approach that enables the accurate prediction of orbital energies and locations. AU - Chen, Ke AU - Kunkel, Christian AU - Cheng, Bingqing AU - Reuter, Karsten AU - Margraf, Johannes T. ID - 12879 JF - Chemical Science SN - 2041-6520 TI - Physics-inspired machine learning of localized intensive properties ER - TY - JOUR AB - Motivation: The problem of model inference is of fundamental importance to systems biology. Logical models (e.g. Boolean networks; BNs) represent a computationally attractive approach capable of handling large biological networks. The models are typically inferred from experimental data. However, even with a substantial amount of experimental data supported by some prior knowledge, existing inference methods often focus on a small sample of admissible candidate models only. Results: We propose Boolean network sketches as a new formal instrument for the inference of Boolean networks. A sketch integrates (typically partial) knowledge about the network’s topology and the update logic (obtained through, e.g. a biological knowledge base or a literature search), as well as further assumptions about the properties of the network’s transitions (e.g. the form of its attractor landscape), and additional restrictions on the model dynamics given by the measured experimental data. Our new BNs inference algorithm starts with an ‘initial’ sketch, which is extended by adding restrictions representing experimental data to a ‘data-informed’ sketch and subsequently computes all BNs consistent with the data-informed sketch. Our algorithm is based on a symbolic representation and coloured model-checking. Our approach is unique in its ability to cover a broad spectrum of knowledge and efficiently produce a compact representation of all inferred BNs. We evaluate the method on a non-trivial collection of real-world and simulated data. AU - Beneš, Nikola AU - Brim, Luboš AU - Huvar, Ondřej AU - Pastva, Samuel AU - Šafránek, David ID - 12876 IS - 4 JF - Bioinformatics TI - Boolean network sketches: A unifying framework for logical model inference VL - 39 ER -