Review
Ecology
Kaitlin Kimmel, Laura E. Dee, Meghan L. Avolio, Paul J. Ferraro
Summary: Ecologists face challenges in inferring causal relationships and lack a general framework to address them. By reviewing causal assumptions and providing solutions, they can design better experiments and bridge the gap between experimental and observational research in ecology.
TRENDS IN ECOLOGY & EVOLUTION
(2021)
Article
Public, Environmental & Occupational Health
Noah A. Haber, Mollie E. Wood, Sarah Wieten, Alexander Breskin
Summary: The DAGWOOD framework is a method for encoding and analyzing causal inference assumptions, which includes a root DAG and a set of branch DAGs. The branch DAGs provide alternative assumptions by changing the root DAG and meeting specific conditions. This framework helps organize causal model assumptions, reinforce best practices, evaluate causal models, and generate new causal models.
ANNALS OF EPIDEMIOLOGY
(2022)
Article
Physics, Multidisciplinary
Bhabani Prasad Mandal, Sumit Kumar Rai, Ronaldo Thibes
Summary: In this paper, a general framework for studying BRST-related transformations is proposed. Different forms of BRST and BRST-related symmetries, including ordinary BRST, anti-BRST, dual-BRST, anti-dual-BRST, and additional sets of new BRST-related symmetries, are investigated within a prototypical first-class system. A Z4 x Z2 discrete group of symmetries of the ghost sector is identified, which connects the various forms of BRST-related transformations. Their distinct roles in different Hamiltonian and Lagrangian approaches are clarified. As a unifying framework, a gauge invariant prototypical first-class system encompassing an extensive class of physical models is used.
Article
Automation & Control Systems
Kayvan Sadeghi, Terry Soo
Summary: This paper formalizes the constraint-based structure learning of the true causal graph from observed data with the existence of unobserved variables. It presents a natural family of constraint-based structure-learning algorithms that output graphs that are Markov equivalent to the causal graph. Under the faithfulness assumption, this natural family encompasses all exact structure-learning algorithms. A set of assumptions is also provided, under which any natural structure-learning algorithm can output Markov equivalent graphs to the causal graph. These assumptions can be considered as a relaxation of faithfulness and can be directly tested from the underlying distribution of the data, especially in the context of structural causal models. The definitions and results in this paper are specialized for structural causal models.
JOURNAL OF MACHINE LEARNING RESEARCH
(2022)
Article
Operations Research & Management Science
Jodi Dianetti, Giorgio Ferrari, Markus Fischer, Max Nendel
Summary: This study provides an abstract framework for investigating submodular mean field games. It establishes verifiable sufficient conditions for the existence and approximation of strong mean field equilibria in models with discontinuous data and common noise. The framework is general enough to encompass a variety of problems, such as mean field games for finite space Markov chains in discrete time, singularly controlled and reflected diffusions, and mean field games of optimal timing. The analysis relies on Tarski's fixed point theorem and technical results on lattices of flows of probability and subprobability measures.
MATHEMATICS OF OPERATIONS RESEARCH
(2022)
Article
Quantum Science & Technology
Rafael Chaves, George Moreno, Emanuele Polino, Davide Poderini, Iris Agresti, Alessia Suprano, Mariana R. Barros, Gonzalo Carvacho, Elie Wolfe, Askery Canabarro, Robert W. Spekkens, Fabio Sciarrino
Summary: Bell's theorem proves the incompatibility between quantum theory and local hidden-variable models, showing that classical causal models cannot explain quantum correlations, but measurement dependence can be quantitatively upper bounded within a network.
Article
Automation & Control Systems
Jiajia Jia, Henk J. van Waarde, Harry L. Trentelman, M. Kanat Camlibel
Summary: This article discusses strong structural controllability of linear systems, establishing necessary and sufficient algebraic conditions as well as a graph-theoretic condition for the full rank property of pattern matrices. Introducing a new color change rule, the study provides a comprehensive understanding of strong structural controllability and how it builds upon existing literature.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Computer Science, Artificial Intelligence
Maarten C. Vonk, Ninoslav Malekovic, Thomas Back, Anna V. Kononova
Summary: This paper aims to disambiguate the different causal concepts that have emerged in causal inference and causal discovery from observational data by attributing them to different levels of Pearl's Causal Hierarchy. We will provide the reader with a comprehensive arrangement of assumptions necessary to engage in causal reasoning at the desired level of the hierarchy. Finally, this paper points to further research areas related to the strong assumptions that researchers have glibly adopted to take part in causal discovery, causal identification and causal inference.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Review
Cell Biology
Adam Turnbull, Aaron Seitz, Duje Tadin, Feng Vankee Lin
Summary: This article proposes a framework to enhance the effect of cognitive training interventions in brain aging. The focus is on developing cognitive training task paradigms informed by population-level cognitive characteristics and pathophysiology, and personalizing how these tasks are presented to optimize mismatch between participant capacity and training demands using feedback loops.
AGEING RESEARCH REVIEWS
(2022)
Article
Mathematics
Ana Arnal, Fernando Casas, Cristina Chiralt, Jose Angel Oteo
Summary: This paper introduces a framework for deriving Fer and Wilcox expansions for the solution of differential equations from specific choices for the initial transformation of the product expansion. It develops recurrence formulas and provides a new lower bound for the convergence of the Wilcox expansion, along with applications of the results. Two examples are worked out up to a high order of approximation to illustrate the behavior of the Wilcox expansion.
Review
Ecology
Kathryn Wilsterman, Mallory A. Ballinger, Caroline M. Williams
Summary: Animals across different taxonomic groups exhibit similar phenotypes during programmed dormancy in highly seasonal environments, but research on dormancy has been historically limited by phylogenetic barriers. A broad comparative approach could provide new insights into the evolution of programmed dormancy.
FUNCTIONAL ECOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Rune Christiansen, Niklas Pfister, Martin Emil Jakobsen, Nicola Gnecco, Jonas Peters
Summary: This paper investigates the problem of predicting a response variable from a set of covariates in the presence of different distributions between the test and training sets. The authors consider interventions in a structural causal model to create test distributions and focus on minimizing the worst-case risk. They introduce the framework of distribution generalization to analyze the problem in partially observed nonlinear models and propose a practical method called NILE for achieving distribution generalization. Consistency is proven and empirical results are presented.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Xiuwen Gong, Dong Yuan, Wei Bao, Fulin Luo
Summary: Partially labeled data learning is widely used in data science, but the challenge lies in handling ambiguities caused by false-positive labels. The current strategy is to identify the ground-truth labels from the candidate set, but it lacks theoretical interpretation. Instead, we propose a novel unifying probabilistic framework that provides a clear formulation and theoretical interpretation for PLL and PML. Our framework also integrates the identifying and embedding methods, considering feature and label correlations. Experimental results show the superiority of our derived framework in both PLL and PML scenarios.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Review
Ecology
Suchinta Arif, M. Aaron MacNeil
Summary: Ecologists often lack training in inferring causation from observational data. Structural causal modeling (SCM), which utilizes directed acyclic graphs (DAGs), offers a framework to determine cause-and-effect relationships. This framework can assist ecologists in quantifying causal relationships and investigating ecological questions using observational data.
ECOLOGICAL MONOGRAPHS
(2023)
Article
Biodiversity Conservation
Carlo Ricotta, Laszlo Szeidl, Sandrine Pavoine
Summary: The concept of generalized means has been identified as an effective tool in unifying the parameterized diversity and dissimilarity measures, including various diversity coefficients and dissimilarity coefficients.
ECOLOGICAL INDICATORS
(2021)
Article
Physics, Multidisciplinary
Jacob Hastrup, Kimin Park, Jonatan Bohr Brask, Radim Filip, Ulrik Lund Andersen
Summary: We propose a protocol for transferring arbitrary continuous-variable quantum states into a few discrete variable qubits and back. The protocol is deterministic and only relies on two-mode Rabi-type interactions that are readily available in trapped-ion and superconducting circuit platforms. The errors caused by transferring an infinite-dimensional state into a finite-dimensional register are exponentially suppressed with the number of qubits. Furthermore, the encoded states show robustness against noise acting on the qubits. Our protocol provides a powerful and flexible tool for discrete-continuous hybrid quantum systems.
PHYSICAL REVIEW LETTERS
(2022)
Article
Optics
Gonzalo Carvacho, Emanuele Roccia, Mauro Valeri, Francesco Basso Basset, Davide Poderini, Claudio Pardo, Emanuele Polino, Lorenzo Carosini, Michele B. Rota, Julia Neuwirth, Saimon F. Covre da Silva, Armando Rastelli, Nicolo Spagnolo, Rafael Chaves, Rinaldo Trotta, Fabio Sciarrino
Summary: Quantum networks are important for distributed quantum information processing, and researchers have implemented a simple bilocality scenario in an urban network to establish quantum communication and shared entanglement. By violating a nonlinear Bell inequality, the nonlocal behavior of the network nodes is demonstrated.
Article
Multidisciplinary Sciences
Iris Agresti, Davide Poderini, Beatrice Polacchi, Nikolai Miklin, Mariami Gachechiladze, Alessia Suprano, Emanuele Polino, Giorgio Milani, Gonzalo Carvacho, Rafael Chaves, Fabio Sciarrino
Summary: Since Bell's theorem, it has been known that local realism fails to explain quantum phenomena. However, recent research has found that in the instrumental scenario, nonclassicality can be observed beyond the classical notions of cause and effect. Through interventions, quantum violations of classical bounds on causal influence can be demonstrated, even without Bell-like violations.
Article
Physics, Multidisciplinary
Carles Roch i Carceller, Kieran Flatt, Hanwool Lee, Joonwoo Bae, Jonatan Bohr Brask
Summary: In this study, we compare the abilities of quantum and classical physics in terms of randomness certification from partially characterized devices, and propose quantum and noncontextual semi-device independent protocols for random-number generation based on maximum-confidence discrimination. The results show that, without unambiguously identifying any input states, quantum devices can certify more randomness than noncontextual ones.
PHYSICAL REVIEW LETTERS
(2022)
Article
Quantum Science & Technology
Kimin Park, Jacob Hastrup, Jonas Schou Neergaard-Nielsen, Jonatan Bohr Brask, Radim Filip, Ulrik L. Andersen
Summary: This study proposes a hybrid protection scheme using two-level ancillas to protect quantum information, which can effectively slow down the decoherence process caused by energy loss and enhance the robustness of complex coherent states.
NPJ QUANTUM INFORMATION
(2022)
Article
Multidisciplinary Sciences
Emanuele Polino, Davide Poderini, Giovanni Rodari, Iris Agresti, Alessia Suprano, Gonzalo Carvacho, Elie Wolfe, Askery Canabarro, George Moreno, Giorgio Milani, Robert W. Spekkens, Rafael Chaves, Fabio Sciarrino
Summary: The authors realize a photonic experiment to demonstrate a triangle causal structure, which violates classical predictions without external inputs. The violation of Bell inequalities can only be explained by modeling causal dependencies as intrinsically quantum. There are also other causal structures beyond Bell that can witness nonclassicality, some of which do not require external inputs.
NATURE COMMUNICATIONS
(2023)
Review
Physics, Applied
Eric G. Cavalcanti, Rafael Chaves, Flaminia Giacomini, Yeong-Cherng Liang
Summary: As we enter the second century of quantum physics, four researchers provide their insights on new research directions aimed at addressing unresolved questions in the foundations of quantum theory.
NATURE REVIEWS PHYSICS
(2023)
Article
Quantum Science & Technology
Pedro Lauand, Davide Poderini, Ranieri Nery, George Moreno, Lucas Pollyceno, Rafael Rabelo, Rafael Chaves
Summary: Seen from the modern lens of causal inference, Bell's theorem proves that a specific classical causal model cannot explain quantum correlations. Therefore, it is necessary to consider different causal structures. For the case of three observable variables, there are three nontrivial causal networks, and we analyze the third one, named the Evans scenario, which is similar to the causal structure underlying the entanglement-swapping experiment. We introduce new computational tools and prove that postquantum correlations violate the constraints imposed by a classical description of the Evans causal structure.
Article
Optics
Anders J. E. Bjerrum, Jonatan B. Brask, Jonas S. Neergaard-Nielsen, Ulrik L. Andersen
Summary: In this paper, an all-optical setup is analyzed, which achieves Bell-inequality violation over long distances by using probabilistic entanglement swapping. The setup consists of two-mode squeezers, displacements, beamsplitters, and on-off detectors. The arrangement of events to close both the detection and locality loopholes is described. A scenario with dichotomic inputs and outputs is analyzed, and the robustness of Bell inequality violation is checked for up to six parties, considering phase, amplitude, dark count noise, and loss.
Article
Optics
Anders J. E. Bjerrum, Jonatan B. Brask, Jonas S. Neergaard-Nielsen, Ulrik L. Andersen
Summary: This study investigates the storage and purification of a photon-loss-affected two-mode squeezed vacuum state using noiseless amplification with solid-state qubits. The proposed method increases entanglement between the parties sharing the state probabilistically. The amplification step involves transferring the state from an optical mode to a set of solid-state qubits acting as a quantum memory, similar to a set of quantum scissors.
Article
Optics
Lucas Pollyceno, Rafael Chaves, Rafael Rabelo
Summary: Bell nonlocality is a fascinating and counterintuitive phenomenon exhibited by quantum systems. The information causality principle is a potential explanation for the constraints on stronger-than-quantum correlations. However, the original formulation of this principle fails to detect even extreme stronger-than-quantum correlations in multipartite scenarios, indicating the need for a genuinely multipartite formulation. In this study, we propose a new formulation of the information causality principle in multipartite scenarios, introducing multipartite informational inequalities as necessary criteria for the principle to hold. We prove that these inequalities hold for all quantum resources and forbid some stronger-than-quantum ones. Furthermore, our approach can be strengthened with multiple copies of the resource or the use of noisy communication channels.
Article
Physics, Multidisciplinary
Nicola D'Alessandro, Beatrice Polacchi, George Moreno, Emanuele Polino, Rafael Chaves, Iris Agresti, Fabio Sciarrino
Summary: In this research, an alternative strategy is proposed to optimize correlations compatible with arbitrary quantum networks using a feedforward artificial neural network. Compared to existing methods, this method handles problems with nonlinear optimization constraints and objective functions and is more efficient than other approaches, allowing exploration of previously inaccessible areas. In addition, the neural network is extended to the experimental field to obtain device-independent uncertainty estimates on Bell-like violations obtained with independent sources of entangled photon states. This research paves the way for certifying quantum resources in networks of growing size and complexity.
PHYSICAL REVIEW RESEARCH
(2023)
Article
Quantum Science & Technology
Bradley Longstaff, Jonatan Bohr Brask
Summary: We investigate nonlocal quantum correlations arising between multiple two-level impurity atoms coupled to an ultracold bosonic gas. Genuine multipartite nonlocality is observed in a system of three impurities, and non-Markovian effects and the persistence of coherences in the impurity subsystem are crucial for preventing complete loss of nonlocality and allowing for nonlocal correlations to be generated and maintained for extended periods of time.
Review
Physics, Applied
Andreas Elben, Steven T. Flammia, Hsin-Yuan Huang, Richard Kueng, John Preskill, Benoit Vermersch, Peter Zoller
Summary: Programmable quantum simulators and computers provide unprecedented opportunities for exploring complex quantum systems. Measurement protocols that involve randomization have advantages such as reusing data sets for multiple applications and mitigating imperfections. These protocols have been used to realize a range of tasks in quantum devices, including simulation, chaos probing, order parameter measurement, and state comparison. The randomized measurement toolbox strengthens our ability to understand and control the quantum world by translating complex quantum states into simpler classical representations.
NATURE REVIEWS PHYSICS
(2023)
Article
Computer Science, Theory & Methods
Stefan Hillmich, Alwin Zulehner, Richard Kueng, Igor L. Markov, Robert Wille
Summary: Quantum computers have the potential to solve problems faster than conventional computers, but the complexity of simulating quantum circuits needs to be addressed. This paper proposes using decision diagrams and approximation methods to improve the efficiency and accuracy of quantum circuit simulation.
ACM TRANSACTIONS ON QUANTUM COMPUTING
(2022)