Causality Constraints Research Articles

Optimal selection of reference microphone for active noise control

This paper proposes two methods for selecting a reference microphone from multiple microphones, focusing on the correlation between the reference signal and the desired signal and the causality constraint of the signal, in order to effectively reduce noise arriving from various locations. In the first method, a threshold is set for the correlation between the reference signal and the desired signal, and a microphone that satisfies the causality constraint is selected as the reference microphone from microphones with correlation values that exceed this threshold. On the other hand, the second method selects a microphone with the highest correlation between the reference signal and the desired signal as the reference microphone among the microphones that satisfy the causality constraint. We demonstrate through simulation experiments that the active noise control system using these proposed methods can provide stable and effective noise reduction for noise arriving from various locations. It is also shown that both methods can reselect an appropriate reference microphone when the noise source moves.

Deep subwavelength sound absorption by a buckling plate resonator

Conventional sound-absorbing materials face great difficulties in achieving deep subwavelength broadband sound absorption due to the causal constraint on the minimum thickness. A buckling plate resonator is proposed to alleviate the causal constraint on the thickness of sound-absorbing materials. The resonator consists of a back cavity sealed by an elastic circular thin plate. When the plate is subjected to a uniform in-plane compressive force up to its critical load, it buckles and behaves as a negative stiffness spring. The positive stiffness of the back cavity is counteracted by the negative stiffness of the buckling plate, resulting in resonance absorption at the deep subwavelength scale. The sound absorption performance of the buckling plate resonator under different in-plane loads was measured in an impedance tube. Quasi-perfect sound absorption was observed in a buckling plate resonator with a thickness less than 1% of the wavelength corresponding to the resonance frequency. This work provides a solution for the design of ultra-thin broadband sound absorbers.

Causality and quasi-normal modes in the GREFT

The General Relativity Effective Field Theory (GREFT) introduces higher-derivative interactions to parameterise the gravitational effects of massive degrees of freedom which are too heavy to be probed directly. The coefficients of these interactions have recently been constrained using causality: both from the analytic structure of 4-point graviton scattering and the time delay of gravitational waves on a black hole background. In this work, causality is used to constrain the quasi-normal mode spectrum of GREFT black holes. Demanding that quasi-normal mode perturbations decay faster in the GREFT than in General Relativity—a new kind of causality condition which stems from the analytic structure of 2-point functions on a black hole background—leads to further constraints on the GREFT coefficients. The causality constraints and compact expressions for the GREFT quasi-normal mode frequencies presented here will inform future parameterised gravitational waveforms, and the observational prospects for gravitational wave observatories are briefly discussed.

Shedding black hole light on the emergent string conjecture

Asymptotically massless towers of species are ubiquitous in the string landscape when infinite-distance limits are approached. Due to the remarkable properties of string dualities, they always comprise Kaluza-Klein states or higher-spin excitations of weakly coupled, asymptotically tensionless critical strings. The connection between towers of light species and small black holes warrants seeking a bottom-up rationale for this dichotomoy, dubbed emergent string conjecture. In this paper we move a first step in this direction, exploring bottom-up constraints on towers of light species motivated purely from the consistency of the corresponding thermodynamic picture for small black holes. These constraints shed light on the allowed towers in quantum gravity, and, upon combining them with unitarity and causality constraints from perturbative graviton scattering, they provide evidence for the emergent string scenario with no reference to a specific ultraviolet completion.

Causality criteria from stability analysis at ultrahigh boost

In this work, we have exclusively employed the linear stability analysis at ultrahigh boost on two well-known stable-causal theories—second-order Müller-Israel-Stewart and first-order Bemfica-Disconzi-Noronha-Kovtun, to identify the region of parameter space over which they are frame-invariantly stable and obey causal signal propagation. It has been shown for these two theories that at near-luminal boost, stability criteria alone can provide the causality constraints on transport coefficients, which are identical to the asymptotic causality conditions, without actually going to the asymptotic limit of the theories. The analysis indicates an alternative approach to derive the causality constraints, which is more appropriate for low-energy effective theories like relativistic hydrodynamics. Published by the American Physical Society 2024

A causal counterfactual graph neural network for arising-from-chair abnormality detection in parkinsonians

The arising-from-chair task assessment is a key aspect of the evaluation of movement disorders in Parkinson's disease (PD). However, common scale-based clinical assessment methods are highly subjective and dependent on the neurologist's expertise. Alternate automated methods for arising-from-chair assessment can be established based on quantitative susceptibility mapping (QSM) images with multiple-instance learning. However, performance stability for such methods can be typically undermined by the presence of irrelevant or spuriously-relevant features that mask the intrinsic causal features. Therefore, we propose a QSM-based arising-from-chair assessment method using a causal graph-neural-network framework, where counterfactual and debiasing strategies are developed and integrated into this framework for capturing causal features. Specifically, the counterfactual strategy is proposed to suppress irrelevant features caused by background noise, by producing incorrect predictions when dropping causal parts. The debiasing strategy is proposed to suppress spuriously relevant features caused by the sampling bias and it comprises a resampling guidance scheme for selecting stable instances and a causal invariance constraint for improving stability under various interferences. The results of extensive experiments demonstrated the superiority of the proposed method in detecting arising-from-chair abnormalities. Its clinical feasibility was further confirmed by the coincidence between the selected causal features and those reported in earlier medical studies. Additionally, the proposed method was extensible for another motion task of leg agility. Overall, this study provides a potential tool for automated arising-from-chair assessment in PD patients, and also introduces causal counterfactual thinking in medical image analysis. Our source code is publicly available at https://github.com/SJTUBME-QianLab/CFGNN-PDarising.

A time-domain integration method with equivalent complex damping model based on viscoelastic material constitutive relation

The complex damping model is only applied in frequency-domain. Based on the constitutive relation of viscoelastic materials, a vibration equation is equivalent to the constitutive relation of complex damping theory and the frequency response function satisfying the causality constraint. Compared with the existing equivalent complex damping model, the proposed equivalent damping model can consider more natural frequencies. The calculation formulas of Gauss precise integral method and improvement constant average acceleration method are derived. By the equivalent complex damping, the seismic time-history response of the typical example could carry on the numerical calculation. Compared with the Gauss precise integral calculation results of complex damping vibration equation, some conclusions can be analyzed. The proposed two methods could avoid divergent phenomenon in the time-domain numerical solution of complex damping vibration equation. The time-domain integral method of equivalent complex damping theory is stable and convergent. The Gauss precise integral method has strong equivalence and big computational complexity. The improvement constant average acceleration method has weak equivalence and small computational complexity.

Human mobility prediction with causal and spatial-constrained multi-task network

Modeling human mobility helps to understand how people are accessing resources and physically contacting with each other in cities, and thus contributes to various applications such as urban planning, epidemic control, and location-based advertisement. Next location prediction is one decisive task in individual human mobility modeling and is usually viewed as sequence modeling, solved with Markov or RNN-based methods. However, the existing models paid little attention to the logic of individual travel decisions and the reproducibility of the collective behavior of population. To this end, we propose a Causal and Spatial-constrained Long and Short-term Learner (CSLSL) for next location prediction. CSLSL utilizes a causal structure based on multi-task learning to explicitly model the “when→what→where”, a.k.a. “time→activity→location” decision logic. We next propose a spatial-constrained loss function as an auxiliary task, to ensure the consistency between the predicted and actual spatial distribution of travelers’ destinations. Moreover, CSLSL adopts modules named Long and Short-term Capturer (LSC) to learn the transition regularities across different time spans. Extensive experiments on three real-world datasets show promising performance improvements of CSLSL over baselines and confirm the effectiveness of introducing the causality and consistency constraints. The implementation is available at https://github.com/urbanmobility/CSLSL.

MCCSeg: Morphological embedding causal constraint network for medical image segmentation

Medical image multi-object segmentation aims to accurately extract each object that is great significant for the medical image analysis. Although several methods based on deep learning have been widely developed, it is still challenging due to the similarities of different objects, the significant morphological variability, data heterogeneity, and the unbalanced distribution of samples. To address the above issues, we propose a novel Morphological Embedding Causal Constraint segmentation network (MCCSeg) for medical image multi-object segmentation. We build a hybrid CNN-Transformer encoder module to obtain local and global features. Specifically, a Causal Constraint Module (CCM) is proposed to decorrelate features by sample reweighting, which utilizes weight allocation to train the weight of training samples. The Random Fourier Features (RFF) is employed to solve nonlinear dependency problems and remove correlations between features to mitigate the influence of spurious correlations. The morphological guidance module (MG) is designed to extract the boundary as prior morphological information for enhancing feature representation, which is concatenated into the deep supervision module in the decoder for further optimizing feature extraction. The experiments demonstrate that MCCSeg outperforms other state-of-the-art methods, achieving improvements of 3.76% and 5.41% in terms of DICE and HD95 scores on Synapse dataset, respectively. The ablation experiments further verify the effectiveness of MCCSeg in improving generalization.

DECA: Discrete Event inspired Cellular Automata for grain structure prediction in additive manufacturing

Microstructure largely dictates macroscopic material properties and is strongly affected by processing. Therefore, the simulation of microstructure evolution in response to thermal fields during processing is of significant interest within the computational materials science community. Additive manufacturing (AM) has emerged as a technique for producing complex geometries and unique microstructures. Yet, complex and rapid thermal cycles in AM pose computational challenges for existing microstructure models. This work proposes a discrete event inspired cellular automata (CA) approach, titled DECA, to accelerate simulation of grain structure evolution in AM. In contrast to conventional time-stepped CA models, this model directly solves the times capture events would take place allowing for stepping in events rather than time (a technique also found in the field of discrete-event simulation). In comparison to purely serial discrete-event models, DECA allows for temporary violation of the causality constraint, but detects and corrects these violations, leading to an emergent phenomenon dubbed causality rippling, in which previously calculated capture events are overwritten. The amount of repeated calculations, defined by the capture ratio, is taken as a measure of computational inefficiency, and the model parameters that affect this ratio are evaluated. The new DECA approach was found to be more computationally efficient than conventional time-stepped CA models while guaranteeing an accurate solution, which can only be achieved in the conventional models for vanishingly small time steps. Finally, opportunities for parallelization and scaling of the new approach are discussed.

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.

Spectral–Spatial Enhancement and Causal Constraint for Hyperspectral Image Cross-Scene Classification

Spectral–Spatial Enhancement and Causal Constraint for Hyperspectral Image Cross-Scene Classification

Reinforcement Learning-Based Security/Safety UAV System for Intrusion Detection Under Dynamic and Uncertain Target Movement

Autonomous security unmanned aerial vehicles (UAVs) have recently gained popularity as an effective solution for accomplishing target/intrusion detection and tracking tasks with little or no human intervention. In this context, we aim at developing an autonomous UAV system for detecting a dynamic and uncertain intrusion within an area, in which the intruder/target moves from one location to another within the area according to an unknown random distribution. The problem of finding an uncertain target while considering the energy causality constraint of the UAV&#x2019;s battery and the uncertainty of the target movement is mathematically formulated as a search benefit maximization problem, which cannot be directly optimized due to the uncertain unknown target movement. Thus, we reformulate the optimization problem as a Markov-decision process that can be solved using reinforcement learning (RL) techniques. Then, we implement an RL-based algorithm to solve the reformulated benefit maximization problem by enabling the UAV to autonomously learn the dynamics of the intruder/target. Specifically, different design variants of the RL-based algorithm are implemented that differ in the used temporal difference methods (i.e., Q-learning or state-action-reward-state-action), and in the exploration algorithms (convergence-based or <formula><tex>$\epsilon$</tex></formula>-greedy). Simulation results show RL algorithms&#x2019; superiority and effectiveness over existing random and circular target detection algorithms.

Averaged null energy and the renormalization group

We establish a connection between the averaged null energy condition (ANEC) and the monotonicity of the renormalization group, by studying the light-ray operator ∫ duTuu in quantum field theories that flow between two conformal fixed points. In four dimensions, we derive an exact sum rule relating this operator to the Euler coefficient in the trace anomaly, and show that the ANEC implies the a-theorem. The argument is based on matching anomalies in the stress tensor 3-point function, and relies on special properties of contact terms involving light-ray operators. We also illustrate the sum rule for the example of a free massive scalar field. Averaged null energy appears in a variety of other applications to quantum field theory, including causality constraints, Lorentzian inversion, and quantum information. The quantum information perspective provides a new derivation of the a-theorem from the monotonicity of relative entropy. The equation relating our sum rule to the dilaton scattering amplitude in the forward limit suggests an inversion formula for non-conformal theories.

Temporal Shift Length and Antecedent Occurrence Likelihood Modulate Counterfactual Conditional Comprehension: Evidence from Event-Related Potentials.

Counterfactual conditionals posit hypothetical scenarios in which antecedent events contradict reality. This study examined whether and how the processing difficulty of Chinese counterfactual conditionals (yaobushi, equivalent to if it had not been for in English) can be affected by the length of temporal shifts of the events across clauses and the likelihood of the antecedent occurrence. Participants read Chinese counterfactuals that contained either long (e.g., qunian-xianzai [last year-right now]) or short temporal shifts (e.g., zuotian-xianzai [yesterday-right now]) within highly likely (e.g., sign up for school activity) or less likely contexts (e.g., sign up for Arctic scientific research). ERP results revealed a significant N400 interaction between the temporal shift length and antecedent likelihood on the temporal indicators in the consequent and the sentence-ending verbs. Specifically, the less likely events elicited larger negativity than highly likely events with short temporal shifts on the temporal indicator. On the sentence-ending word, the long temporal shift elicited enlarged negativity than the short temporal shift when the antecedent was highly likely. These findings have two key implications regarding the interplay of implied causality and falsity constraints during counterfactual comprehension. First, salient falsity constraints can override effects of causal coherence on processing. Second, greater negativity for unlikely antecedents suggests that counterfactual markers concurrently activate factual and hypothetical representations.

Experimental evaluation of feedforward active noise control system with optical laser microphone and proportional-integral-differential filter

In this paper, we propose a feedforward active noise control (ANC) system with an optical laser microphone utilizing a proportional-integral-differential (PID) filter. The feedforward ANC system with the optical laser microphone has been proposed to relax the causality constraint. This system adopts a first-order differentiator to modify the velocity picked up by the optical laser microphone. This modification achieves the improvement of the coherence between the velocity as the reference signal and the unwanted noise as the sound pressure. However, the frequency response of the first-order differentiator is similar to the high-pass filter and attenuates the reference signal at low frequencies. This causes the degradation of the noise reduction performance of the conventional ANC system. To solve this problem, the proposed ANC system adopts the PID filter instead of the first-order differentiator. By adjusting the coefficients of the PID filter, the proposed system avoids the attenuation of the reference signal at low frequencies. Then, the noise reduction performance can be improved. Experimental results show that the proposed ANC system reduces the unwanted noise compared to the conventional ANC system and relaxes the causality constraint compared to the basic ANC system in the real-time operation.

Performance evaluation of an active headrest system using a filtered-x least mean square/fourth algorithm with virtual sensing.

In an active headrest system virtual sensing tends to transfer the spatial zone of quiet from the residual error microphone to the ear canal. An attempt has been made in this paper to develop an auxiliary filter based virtual sensing scheme integrated with the filtered-x least mean square/fourth algorithm for an active headrest. The performance of the proposed method has been evaluated experimentally using periodic and band limited white noise. Improved noise control performance has been observed for both periodic and broadband noise. The effect of causality constraint of the performance of the algorithm has also been tested.

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 Complete Design of Smart Pad That Reduces Snoring

This paper presents integration of active noise control (ANC) systems with a smart pad to reduce the noise made by snoring. One reference microphone, two secondary speakers and two error microphones are used with a feedforward ANC algorithm in the smart pad design. Several smart functions including snore detection, quiet zone localization, and advanced audio integration are proposed. An effective way to satisfy the causality constraint of the feedforward ANC system is presented. The smart pad is independent of the pillow and is not in contact with the user; it is portable and is not a burden to the user during sleep. Several experiments are conducted to confirm the effectiveness of the smart pad when used on a full-size bed.

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.