Information Causality Research Articles

Information Causality as a Tool for Bounding the Set of Quantum Correlations.

Information causality was initially proposed as a physical principle aimed at deriving the predictions of quantum mechanics on the type of correlations observed in the Bell experiment. In the same work, information causality was famously shown to imply the Uffink inequality that approximates the set of quantum correlations and rederives Tsirelson's bound of the Clauser-Horne-Shimony-Holt inequality. This result found limited generalizations due to the difficulty of deducing implications of the information causality principle on the set of nonlocal correlations. In this Letter, we present a simple technique for obtaining polynomial inequalities from information causality bounding the set of physical correlations in any bipartite Bell scenario. This result makes information causality an efficient tool for approximating the set of quantum correlations. To demonstrate our method, we derive a family of inequalities which nontrivially constrains the set of nonlocal correlations in Bell scenarios with binary outcomes and equal number of measurement settings. Finally, we propose an improved statement of the information causality principle and obtain a tighter constraint for the simplest Bell scenario that goes beyond the Uffink inequality and recovers a part of the boundary of the quantum set.

Общий искусственный интеллект, информация и сознание: об интерпретации Д.И. Дубровского

The article is devoted to the analysis of the concept of artificial general intelligence (AGI) and its interpretation proposed by the Russian philosopher David Dubrovsky in his recent research papers. The first part of the article briefly outlines the current approaches to defining the concept of “artificial general intelligence”, including interpreting it as an artificial intelligent system capable of achieving common goals in a variety of environments. Referring to the texts of the most influential foreign researchers and developers, the author demonstrates the parallels between their proposed approaches to understanding general artificial intelligence and those interpretations proposed by David Dubrovsky and his co-authors. In particular, the commonality in the interpretations of the concept of the “world model” (Yan LeCun) and the concept of “techno-umwelt” is shown, as well as parallels between the hypothesis of “universal embodied AI” (Ben Herzel) and the arguments of the Russian philosopher about the possible implementation of AI through its involvement in various types of interactions with various worlds, virtual and physical. In the second part of the article, the potential of using the information approach developed by David Dubrovsky to solve the mind-body problem as a basis for explaining the phenomenon of general artificial intelligence is outlined. It is shown that despite the need to refine the concept of information causality proposed by the philosopher, his theory can contribute to a better understanding of the connection of possible AGI competencies with the phenomena of subjective reality. In conclusion, the key problems that currently make it difficult to find an answer to the question of the dependence of the qualities of general artificial intelligence on the presence of phenomenal consciousness are outlined. The emphasis is placed on the need to continue interdisciplinary cooperation between representatives of cognitive sciences, developers and philosophers, whose interaction is designed to help solve the characteristic difficulties associated with both the problem of conceptualizing the concept of “artificial general intelligence” and the problem of identifying consciousness in artificial intelligent systems

Bounding Quantum Correlations: The Role of the Shannon Information in the Information Causality Principle.

The Information Causality principle was proposed to re-derive the Tsirelson bound, an upper limit on the strength of quantum correlations, and has been suggested as a candidate law of nature. The principle states that the Shannon information about Alice's distant database gained by Bob after receiving an m bit message cannot exceed m bits, even when Alice and Bob share non-local resources. As originally formulated, it can be shown that the principle is violated exactly when the strength of the shared correlations exceeds the Tsirelson bound. However, we demonstrate here that when an alternative measure of information, one of the Renyi measures, is chosen, the Information Causality principle no longer arrives at the correct value for the Tsirelson bound. We argue that neither the assumption of particular 'intuitive' properties of uncertainties measures, nor pragmatic choices about how to optimise costs associated with communication, are sufficient to motivate uniquely the choice of the Shannon measure from amongst the more general Renyi measures. We conclude that the dependence of the success of Information Causality on mere convention undermines its claimed significance as a foundational principle.

A Twist for Graph Classification: Optimizing Causal Information Flow in Graph Neural Networks

Graph neural networks (GNNs) have achieved state-of-the-art results on many graph representation learning tasks by exploiting statistical correlations. However, numerous observations have shown that such correlations may not reflect the true causal mechanisms underlying the data and thus may hamper the ability of the model to generalize beyond the observed distribution. To address this problem, we propose an Information-based Causal Learning (ICL) framework that combines information theory and causality to analyze and improve graph representation learning to transform information relevance to causal dependence. Specifically, we first introduce a multi-objective mutual information optimization objective derived from information-theoretic analysis and causal learning principles to simultaneously extract invariant and interpretable causal information and reduce reliance on non-causal information in correlations. To optimize this multi-objective objective, we enable a causal disentanglement layer that effectively decouples the causal and non-causal information in the graph representations. Moreover, due to the intractability of mutual information estimation, we derive variational bounds that enable us to transform the above objective into a tractable loss function. To balance the multiple information objectives and avoid optimization conflicts, we leverage multi-objective gradient descent to achieve a stable and efficient transformation from informational correlation to causal dependency. Our approach provides important insights into modulating the information flow in GNNs to enhance their reliability and generalization. Extensive experiments demonstrate that our approach significantly improves the robustness and interpretability of GNNs across different distribution shifts. Visual analysis demonstrates how our method converts informative dependencies in representations into causal dependencies.

3D Trajectory Planning for Real-Time Image Acquisition in UAV-Assisted VR

Nowadays, unmanned aerial vehicles (UAVs), empowered with the capability of high-definition image transmission, are used to capture the rapidly changing physical environment by leveraging its high flexibility to reconstruct an immersive realistic virtual environment for metaverse users. In this paper, we consider a novel UAV-assisted image acquisition system where a UAV is dispatched to take off from an initial location to capture real-time images of multiple ground targets and then transfer the captured images back to the ground user for virtual environment reconstruction. We aim to minimize the time for the UAV to complete the image acquisition task by optimizing the three-dimensional UAV trajectory under the constraints of image quality, information causality and energy consumption. To this end, we first formulate the investigated scenario into a mixed integer optimization problem, which, however, is difficult to solve due to the infinite time-varying variables closely coupled with each other. Then, a three-stage progressive algorithm is proposed to obtain an efficient solution to the formulated mixed integer optimization problem, where the constraints of image quality, information causality and energy consumption can be sequentially satisfied. Finally, comprehensive performance evaluation is conducted to verify the effectiveness of the proposed three-stage progressive trajectory design algorithm, and the results show that the proposed algorithm significantly outperforms the benchmark schemes.

Stochastic Dynamics of Fusion Low-to-High Confinement Mode (L-H) Transition: Correlation and Causal Analyses Using Information Geometry.

We investigate the stochastic dynamics of the prey-predator model of the Low-to-High confinement mode (L-H) transition in magnetically confined fusion plasmas. By considering stochastic noise in the turbulence and zonal flows as well as constant and time-varying input power Q, we perform multiple stochastic simulations of over a million trajectories using GPU computing. Due to stochastic noise, some trajectories undergo the L-H transition while others do not, leading to a mixture of H-mode and dithering at a given time and/or input power. One of the consequences of this is that H-mode characteristics appear at a smaller input power Q<Qc (where Qc is the critical value for the L-H transition in the deterministic system) as a secondary peak of a probability density function (PDF) while dithering characteristics persists beyond the power threshold for Q>Qc as a second peak. The coexisting H-mode and dithering near Q=Qc leads to a prominent bimodal PDF with a gradual L-H transition rather than a sudden transition at Q=Qc and uncertainty in the input power. Also, a time-dependent input power leads to increased variability (dispersion) in stochastic trajectories and a more prominent bimodal PDF. We provide an interpretation of the results using information geometry to elucidate self-regulation between zonal flows, turbulence, and information causality rate to unravel causal relations involved in the L-H transition.

Energy Consumption Minimization in Unmanned Aerial Vehicle-Enabled Secure Wireless Sensor Networks

In wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs) are considered an effective data collection tool. In this paper, we investigate the energy-efficient data collection problem in a UAV-enabled secure WSN without knowing the instantaneous channel state information of the eavesdropper (Eve). Specifically, the UAV collected the information from all the wireless sensors at the scheduled time and forward it to the fusion center while Eve tries to eavesdrop on this confidential information from the UAV. To surmount this intractable and convoluted mixed-integer non-convex problem, we propose an efficient iterative optimization algorithm using the block coordinate descent (BCD) method to minimize the maximum energy consumption of the ground sensor nodes (GSNs) under the constraints of secrecy outage probability (SOP), connection outage probability (COP), minimum secure data, information causality, and UAV trajectory. Numerical results demonstrate the superiority of the algorithm we proposed in energy consumption and secrecy rate compared with other schemes.

A general theory to estimate Information transfer in nonlinear systems

A general theory for computing information transfers in nonlinear stochastic systems driven by deterministic forcings and additive and/or multiplicative noises, is presented, satisfying quite general boundary conditions in the state-space: either closed, periodic or satisfying a vanishing probability density function. It extends the Liang-Kleeman (LK) framework of causality inference to nonlinear cases based on information transfer across system variables, which is presented in detail in (Liang, 2016. Information flow and causality as rigorous notions ab initio. Phys. Rev. E, 94: 052201. DOI: 10.1103/PhysRevE.94.052201). We present an effective method of computing formulas of the rates of Shannon entropy transfer (RETs) between selected causal and consequential variables, the ‘Causal Sensitivity Method’ (CSM), relying on the estimation from data of conditional expectations of the system forcings and their derivatives. Those expectations are approximated by nonlinear differentiable regressions, leading to a much easier and more robust way of computing RETs than the ‘brute-force’ approach which calls for the computation of numerical integrals over the state-space and the knowledge of the multivariate probability density function of the system. The CSM is furthermore fully adapted to the case where no model equations are available, starting with a nonlinear model fitting from data of the consequential variables, with the subsequent application of CSM to the fitted model. RETs are decomposed into deterministic and stochastic components, being compensated by the self generation of entropy in ergodic conditions. Moreover, RETs are decomposed into sums of single one-to-one RETs plus synergetic terms (of pure nonlinear nature) accounting for the joint causal effect of groups of variables. State-dependent (or specific) RET formulas are also introduced, puting in evidence where in state-space the entropy transfers and local synergies are more relevant. A comparison of the RETs estimations is performed between: 1) the ‘brute-force’, expensive (taken as benchmark), probability-density-based approach (AN), 2) the CSM-based approach with and/or without model fitting, and 3) the multivariate linear (ML) approach, in the context of two different models: (i) a model derived from a potential function and (ii) the classical chaotic Lorenz system, both forced by additive and/or multiplicative noises. The analysis demonstrates that the CSM estimations are robust, cheaper, and less data-demanding than the AN-reference values in the different experiments, providing evidence of the possibilities and generalizations offered by the method (e.g. causality diagnostics between subspaces) and opening new perspectives on real-world applications.

Joint Slot Scheduling and Power Allocation for Throughput Maximization of Clustered UASNs

In the last decades, independent consideration of underwater acoustic medium access control (MAC) layer has received much attention for designing a reliable data transmission protocol. Although it can simplify the system design, it is often insufficient for the enhancement of overall system performance. The focus of this paper is on the cross-layer optimization to maximize the network throughput (NT) of clustered underwater acoustic sensor networks (UASNs) by jointly optimizing the sensor nodes’ slot scheduling and power allocation. The formulated problem is a mixed integer non-linear programming problem, which is NP-hard. An alternating-optimization-based centralized algorithm is proposed first to solve it, which can achieve the best NT performance but at the price of high complexity. Therefore, a multi-leader multi-follower Stackelberg game based distributed algorithm is also proposed to achieve a better tradeoff between system performance and complexity. Simulation results demonstrate that our proposed schemes considering the information causality constraint have a better NT performance than those without a “systematic” consideration over such joint optimization, like CMS-MAC algorithm.

A study of brain networks associated with Freezing of gait in Parkinson’s disease using transfer entropy analysis

BackgroundParkinson's disease (PD) is a common neurodegenerative disease in the elderly. Freezing of Gait (FOG) is one of the common motor symptoms of PD, but the potential mechanism remains unclear. This study aimed to investigate the changes of brain functional network topology in PD patients with FOG. MethodsThe resting electroencephalogram (EEG) were acquired from15 PD patients with FOG (PD-FOG), 13 PD patients without FOG (PD-nFOG), and 16 healthy control (HC). Cognitive and motor functions were assessed using subjective scales. The whole-brain functional networks were constructed based on transfer entropy. Transfer entropy was used to analyse the information flow and causality in the network and the network connectivity was analyzed by graph theory. The characteristics of PD-FOG and PD-nFOG were compared by receiver operator characteristic (ROC) curve analysis. ResultsThe θ bands brain network of PD-FOG, PD-nFOG and HC group was significantly different (P < 0.05). The average characteristic path length of the θ bands brain network was positively correlated with FOG Questionnaire (FOGQ). PD-FOG and PD-nFOG get high classification accuracy according to this feature. The information inflow in the frontal and occipital lobes and information outflow in the temporal lobe of PD-FOG patients in the θ bands increased significantly. ConclusionsThe whole-brain functional network characteristics of PD-FOG in the θ bands can serve as potential biomarkers for early diagnosis of PD-FOG. Abnormal information flow of the frontal, occipital, and temporal lobes in the θ bands may be an important factor leading to FOG.

Code-routing: a new attack on position verification

The cryptographic task of position verification attempts to verify one party's location in spacetime by exploiting constraints on quantum information and relativistic causality. A popular verification scheme known as f-routing involves requiring the prover to redirect a quantum system based on the value of a Boolean function f. Cheating strategies for the f-routing scheme require the prover use pre-shared entanglement, and security of the scheme rests on assumptions about how much entanglement a prover can manipulate. Here, we give a new cheating strategy in which the quantum system is encoded into a secret-sharing scheme, and the authorization structure of the secret-sharing scheme is exploited to direct the system appropriately. This strategy completes the f-routing task using O(SPp(f)) EPR pairs, where SPp(f) is the minimal size of a span program over the field Zp computing f. This shows we can efficiently attack f-routing schemes whenever f is in the complexity class ModpL, after allowing for local pre-processing. The best earlier construction achieved the class L, which is believed to be strictly inside of ModpL. We also show that the size of a quantum secret sharing scheme with indicator function fI upper bounds entanglement cost of f-routing on the function fI.

Power efficient UAV placement and resource allocation for adaptive video streaming in wireless networks

Unmanned aerial vehicle (UAV) enabled wireless communication that can transmit/receive data to/from ground users, especially at cell boundaries or in disaster-stricken areas where the ground-based infrastructure has been damaged, is an emerging area of research. These aerial vehicles require energy for communication and are typically energy constrained. The communication system becomes more energy hungry when it comes to video streaming of high quality. Because for higher video quality streaming, higher bitrate and more bandwidth is required. Higher transmission power and rate will result in increased streaming quality. We concentrate on UAV-aided power efficient adaptive video streaming wireless networks with multiple ground users, taking into account the available energy budget and information causality constraints. The objective of this research work is to maximize adaptive video streaming utility per unit of power in a UAV-enabled wireless communication network by optimizing the position of UAV and allocation of communication resources, such as bandwidth, and transmit power, while competing with system-designed constraints, such as energy budget, transmission rate, and allocated bandwidth. The problem that is addressed is non-convex which is approached by applying successive convex approximation, Dinkelbach method and coordinates decent techniques, and an efficient iterative algorithm is devised for its solution. Extensive simulations show that the suggested optimization strategy outperforms the results of the previously proposed works.

Resource optimization for energy‐efficient NOMA‐based multi‐UAV‐enabled relaying networks

AbstractOwing to the advantages of low cost, flexibility, and the transmission characteristics of air–ground (AG) channels, using the unmanned aerial vehicle (UAV) as mobile relay to assist wireless communication has received significant interest recently. A green non‐orthogonal multiple access (NOMA)‐based multi‐UAV‐enabled relaying system with different rate requirements is proposed here. To improve energy efficiency (EE) of the UAV relays, an optimization problem for user grouping, UAV trajectory design and resource allocation under the constraints of information causality constraint, UAVs' maximum service capacity, maximum transmit power constraint, and different communication rate requirements of the mobile users (MUs) is formulated. According to the grouping results, the UAVs' trajectory and power allocation by Dinkelbach method, successive convex approximation, and condensation algorithm are alternately optimized. To solve this highly coupled non‐linear mixed integer programming problem, a graph‐based grouping algorithm is proposed to reduce the relative distance between the UAV relay and the MU. Simulation results show that the proposed optimization algorithm can effectively improve the EE performance of this NOMA‐based multi‐UAV‐enabled relaying system.

Information causality in multipartite scenarios

The Bell nonlocality is one of the most intriguing and counterintuitive phenomena displayed by quantum systems. Interestingly, such stronger-than-classical quantum correlations are somehow constrained, and one important question to the foundations of quantum theory is whether there is a physical, operational principle responsible for those constraints. One candidate is the information causality principle, which, in some particular cases, is proven to hold for quantum systems and to be violated by stronger-than-quantum correlations. In multipartite scenarios, though, it is known that the original formulation of the information causality principle fails to detect even extremal stronger-than-quantum correlations, thus suggesting that a genuinely multipartite formulation of the principle is necessary. In this work, we advance towards this goal, reporting a different formulation of the information causality principle in multipartite scenarios. By proposing a change of perspective, we obtain multipartite informational inequalities that work as necessary criteria for the principle to hold. We prove that such inequalities hold for all quantum resources and forbid some stronger-than-quantum ones. Finally, we show that our approach can be strengthened if multiple copies of the resource are available, or, counterintuitively, if noisy communication channels are employed.

A new dialogue on Yijing -the book of changes in a world of changes, instability, disequilibrium and turbulence

ABSTRACT This paper proposes a reinterpretation of the Chinese worldview on equilibrium/nonequilibrium and yin-yang in the context of science and draws the correlative aspects with irreversible thermodynamics and quantum reality, such as instability, nonlinearity, nonequilibrium, and temporality. The paper argues that Prigogine's expressions on dissipative structures and their role in thermodynamic systems far from equilibrium, complexity, and irreversibility resonate with the principles in Yijing. Instability, far-from-equilibrium, irreversibility, probability, bifurcation, and self-organisation are intrinsic properties of nature appearing at all levels. Information is the basis of all changes. The agency of change is the human with a consciousness interpreting the information existing in the probability space between heaven and earth. The paper outlines a modelling approach with the self-organising human in the centre between heaven and earth, representing living systems as discrete dynamical systems presented with binary yin-yang choices. The concepts of yin-yang and information causality are central to Yijing’s understanding of change and are clarified in this paper.

Principle of Information Causality Rationalizes Quantum Composition.

The principle of information causality, proposed as a generalization of no signaling principle, has efficiently been applied to outcast beyond quantum correlations as unphysical. In this Letter, we show that this principle, when utilized properly, can provide physical rationale toward structural derivation of multipartite quantum systems. In accordance with the no signaling condition, the state and effect spaces of a composite system can allow different possible mathematical descriptions, even when descriptions for the individual systems are assumed to be quantum. While in one extreme, namely, the maximal tensor product composition, the state space becomes quite exotic and permits composite states that are not allowed in quantum theory, the other extreme-minimal tensor product composition-contains only separable states, and the resulting theory allows only Bell local correlation. As we show, none of these compositions is commensurate with information causality, and hence cannot be the bona-fide description of nature. Information causality therefore promises an information-theoretical derivation of self duality of the state and effect cones for composite quantum systems.

Uncertainty and information causality in opportunity-as-artefact driving entrepreneurial actions

PurposeEntrepreneurs prioritise and act on purposeful endeavours instigated to actions by the visions of profits and benefits in the perceived opportunities. In the state of maximum entropy, with disorderliness and disequilibrium, entrepreneurs select the preferred pathway, through the profit-sensing mechanism, with the best probability of success to bet on. Therefore, this paper unpacks the forces at work in the mechanism to explain how entrepreneurs respond to opportunity and interpret the signals to coalesce into organised actions.Design/methodology/approachThis research is primarily a conceptual paper on entrepreneurial action and the mechanism leading to that action. It refers to thermodynamic principles and biological cases to explain the forces at work using mostly analogical comparisons and similarities.FindingsThis paper aims to present an alternative theoretical scaffolding for entrepreneurship researchers to explore non-rational entrepreneurial behaviours and actions in uncertain, unstable and non-equilibrium environments, thereby creating new and competing hypotheses under the backdrop of adaptive evolution and thermodynamics phenomena.Research limitations/implicationsThe discussion featuring instinctively and naturally forming responses cannot fully explain the real entrepreneurial action as there is an element of free will and choices that are not discussed. While strategic choice and free-will shape decisions, they are preceded first by the attraction of the gradients and the biased motion in the direction of profit-attractant.Practical implicationsThere remain essential links and issues not addressed in this “natural science”, constituting life science and physical science, oriented entrepreneurship research and exploration. Conceptualising opportunity-as-artefact and entrepreneurship as design, significant incidences of entrepreneurial actions can be explained by the presence of gradients stimulating entrepreneurial actions.Social implicationsThis viewpoint of information causality in opportunity-as-artefact casts a new look at the venerable question of what causes entrepreneurial actions. Shane and Venkataraman brought into focus this conversation, initiating the conceptual definition of opportunity. To have entrepreneurship, entrepreneurial opportunities must come first. Figuring the signals arising from these opportunities and cueing entrepreneurs to action is the main focus of this study.Originality/valueConsidering the “mechanism” at work and the thermodynamical forces at play, the entrepreneurial design process appears to hold considerable promise for future research development.

A Location-Aware Resource Optimization for Maximizing Throughput of Emergency Outdoor–Indoor UAV Communication with FSO/RF

In emergency communication scenarios, unmanned aerial vehicles (UAVs) can be used as an air relay to provide higher-quality communication for indoor users. When bandwidth resources are scarce, the use of free space optics (FSO) technology will greatly improve the resource utilization of the communication system. Therefore, we introduce FSO technology into the backhaul link of outdoor communication, and use free space optical/radio frequency (FSO/RF) technology to realize the access link of outdoor indoor communication. The deployment location of UAVs will affect not only the through wall loss of outdoor-indoor communication but also the quality of FSO communication, and, therefore, it needs to be optimized. In addition, by optimizing the power and bandwidth allocation of UAVs, we realize the efficient utilization of resources and improve the system throughput on the premise of considering information causality constraints and user fairness. The simulation results show that, by optimizing the location and power bandwidth allocation of UAVs, the system throughput is maximized, and the throughput between each user is fair.

A novel approach of full state tendency measurement for complex systems based on information causality and PageRank: A case study of a hydropower generation system

The hydropower generation system is a typical complex nonlinear system with hybrid state responses. The interaction between the state responses of the system is affected closely by the coupling of hydraulic, mechanical, and electromagnetic factors and the frequent changes in working conditions during operation. However, the precise roles of the interaction relationship are unknown. Here, we show that this interaction depends on the causal coupling between subsystems, and use this relationship to propose a time series data mining and data prediction strategy based on the information causality and the PageRank algorithm. A nonlinear model is used to prove that the proposed prediction strategy can effectively reduce the dimension of auxiliary variables. Finally, the strategy is validated with a 250 MW hydropower unit. Our results show that the information causal coupling between variables is cross-scale with definite Markov orders of time series, and the prediction accuracy can be improved by considering the information transfer sequences between the prediction object variable and the causal variable in the absence of state auxiliary variables. Furthermore, the proposed method can be also applied to data mining of other complex systems and variable selection of prediction models and builds a bridge between mechanism- and data-driven approaches, which has a high engineering application value.

Joint Optimization of Trajectory and Resource Allocation in Secure UAV Relaying Communications for Internet of Things

As unmanned aerial vehicle (UAV) communication has been widely used in all walks of life, its secrecy issue has also received more and more attention. This article studies the physical-layer security of UAV relaying communication system in multiterminal Internet of Things (IoT) scenarios. Specifically, while receiving the information from the ground base station, the UAV safely forwards the information to one of a group of IoT terminals in the presence of an eavesdropper. Under the constraints of information causality and UAV mobility, our goal is to maximize the minimum average secrecy rate among all IoT terminals. Based on the nonconvex problem, this article proposes a high-efficiency algorithm for joint optimization of UAV trajectory and resource allocation. The simulation results show that the proposed algorithm not only effectively improves information secrecy of IoT terminals, but also enhances the fairness of communication between the IoT terminals.