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Previous research indicates that background speech disrupts reading comprehension processes, but it remains unclear whether the disruption derives from semantic or phonological speech properties, and whether it affects early lexical processing or later sentence integration. Native Chinese speaking participants read sentences containing high- or low-predictability words under meaningful Chinese speech, meaningless Uyghur speech or silence conditions. Results showed that Chinese but not Uyghur speech produced increased total fixations compared to reading in silence, suggesting disruption was semantic in nature. While a standard predictability effect was comparable across background speech conditions in target word analyses, this effect disappeared in the Chinese speech condition in later measures and regions. The findings suggest that Chinese background speech may delay higher order (post-lexical) processing associated with sentence integration during reading, with implications for the Interference-by-Process hypothesis.

Di Zhang,1,&ast; Yu Guan,1,&ast; Xiuxiu Zhou,1 Mingzi Zhang,2 Yu Pu,1 Pengchen Gu,2 Yi Xia,1 Yang Lu,2 Jia Chen,2 Wenting Tu,1 Kunyao Huang,2 Jixin Hou,2 Hua Yang,2 Chicheng Fu,2 Qu Fang,2 Chuan He,2 Shiyuan Liu,1 Li Fan1 1Department of Radiology, Second Affiliated Hospital, Naval Medical University, Shanghai, People’s Republic of China; 2Scientific Research Department, Shanghai Aitrox Technology Corporation Limited, Shanghai, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Li Fan; Shiyuan Liu, Department of Radiology, Second Affiliated Hospital, Naval Medical University, 415 Fengyang Road, Shanghai, 200003, People’s Republic of China, Tel +13564684699 ; +13761304518, Fax +86 21 63587668, Email czyxfl@smmu.edu.cn; radiology_cz@163.comPurpose: To develop a novel method for calculating small airway resistance using computational fluid dynamics (CFD) based on CT data and evaluate its value to identify COPD.Patients and Methods: 24 subjects who underwent chest CT scans and pulmonary function tests between August 2020 and December 2020 were enrolled retrospectively. Subjects were divided into three groups: normal (10), high-risk (6), and COPD (8). The airway from the trachea down to the sixth generation of bronchioles was reconstructed by a 3D slicer. The small airway resistance (RSA) and RSA as a percentage of total airway resistance (RSA%) were calculated by CFD combined with airway resistance and FEV1 measured by pulmonary function test. A correlation analysis was conducted between RSA and pulmonary function parameters, including FEV1/FVC, FEV1% predicted, MEF50% predicted, MEF75% predicted and MMEF75/25% predicted.Results: The RSA and RSA% were significantly different among the three groups (p< 0.05) and related to FEV1/FVC (r = − 0.70, p < 0.001; r = − 0.67, p < 0.001), FEV1% predicted (r = − 0.60, p = 0.002; r = − 0.57, p = 0.004), MEF50% predicted (r = − 0.64, p = 0.001; r = − 0.64

Yu Pu,&ast; Xiuxiu Zhou,&ast; Di Zhang, Yu Guan, Yi Xia, Shiyuan Liu, Li Fan Department of Radiology, Second Affiliated Hospital of PLA Naval Medical University, Shanghai, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Shiyuan Liu; Li Fan, Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai, 200003, People’s Republic of China, Tel +86 21 81886012 ; +86 21 81886011, Fax +86 21 63587668, Email radiology_cz@163.com; fanli0930@163.comPurpose: To explore the morphological alterations in small pulmonary vessels in populations at high risk for chronic obstructive pulmonary disease (COPD) and smokers based on multiple computed tomography (CT) quantitative parameters.Patients and Methods: A total of 1969 Three Major Chest Diseases Screening Study participants with available demographic data and smoking history who underwent low-dose chest CT from 2018 to 2020 were included. All subjects were divided into normal, high risk for COPD, and COPD groups according to their pulmonary function test (PFT) results. Furthermore, the three groups were further subdivided into never-smokers, current smokers, and former smokers subgroups according to their smoking history. Quantitative parameters, such as the number, area at 6 mm~24 mm subpleura and volume of small pulmonary vessels, were extracted by computer software. Differences in small pulmonary vessel parameters among the groups were compared using two-way ANOVA.Results: The number, area at 6 mm~24 mm subpleura and volume of small pulmonary vessels in the group at high risk for COPD were lower than those in the normal group (P< 0.05). The number, area at 6 mm~24 mm subpleura and volume of small pulmonary vessels in the COPD group were higher than those in the normal group (P< 0.05). The number, area of small pulmonary vessels at 6 mm~12 mm subpleura in current smokers with high risk for COPD were higher than

We present a novel, deep-learning based method -- dubbed Galactic-Seismology Substructures and Streams Hunter, or GS$^{3}$ Hunter for short, to search for substructures and streams in stellar kinematics data. GS$^{3}$ Hunter relies on a combined application of Siamese Neural Networks to transform the phase space information and the K-means algorithm for the clustering. As a validation test, we apply GS$^{3}$ Hunter to a subset of the Feedback in Realistic Environments (FIRE) cosmological simulations. The stellar streams and substructures thus identified are in good agreement with corresponding results reported earlier by the FIRE team. In the same vein, we apply our method to a subset of local halo stars from the Gaia Early Data Release 3 and GALAH DR3 datasets, and recover several, previously known dynamical groups, such as Thamnos 1+2, Hot Thick Disk, ED-1, L-RL3, Helmi 1+2, and Gaia-Sausage-Enceladus, Sequoia, VRM, Cronus, Nereus. Finally, we apply our method without fine-tuning to a subset of K-giant stars located in the inner halo region, obtained from the LAMOST Data Release 5 (DR5) dataset. We recover three, previously known structures (Sagittarius, Hercules-Aquila Cloud, and the Virgo Overdensity), but we also discover a number of new substructures. We anticipate that GS$^{3}$ Hunter will become a useful tool for the community dedicated to the search of stellar streams and structures in the Milky Way (MW) and the Local group, thus helping advance our understanding of the stellar inner and outer halos, and of the assembly and tidal stripping history in and around the MW., Comment: 40 pages, 33 figures, 3 tables

In this experimental study, we use a data-driven machine learning framework based on genetic programing (GP) to discover model-free control laws (individuals) for suppressing self-excited thermoacoustic oscillations in a prototypical laminar combustor. This GP framework relies on an evolutionary algorithm to make decisions based on natural selection. Starting from an initial generation of individuals, we rank their performance based on a cost function that accounts for the trade-off between the state cost (thermoacoustic amplitude) and the input cost (actuator power). We then breed subsequent generations of individuals via a tournament in which the direct forwarding of elite individuals occurs alongside genetic operations such as mutation, replication, and crossover. We implement this GP control framework in both closed-loop and open-loop forms, followed by benchmarking against conventional open-loop control based on time-periodic forcing. We find that while all three control strategies can achieve similarly large reductions in thermoacoustic amplitude, GP closed-loop control consumes the least actuator power, making it the most efficient. It achieves this efficiency by learning an actuation mechanism that exploits the strong heat-release-rate amplification of the open flame at its preferred mode, even though the GP algorithm has never seen the open flame itself. This study demonstrates the feasibility of using GP to discover new and more efficient model-free individuals for suppressing self-excited thermoacoustic oscillations, providing a promising approach to data-driven feedback control of combustion devices.

We experimentally investigate the forced synchronization of a self-excited chaotic thermoacoustic oscillator with two natural frequencies, f(1) and f(2). On increasing the forcing amplitude, epsilon(f), at a fixed forcing frequency, f(f), we find two different types of synchronization: (i) f(f)/f(1) = 1 : 1 or 2 : 1 chaos-destroying synchronization (CDS), and (ii) phase synchronization of chaos (PSC). En route to 1 : 1 CDS, the system transitions from an unforced chaotic state (CH1,2) to a forced chaotic state (CH1,2, f), then to a two-frequency quasiperiodic state where chaos is destroyed (T-2,f(2)), and finally to a phase-locked period-1 state (P1(f)). The route to 2 : 1 CDS is similar, but the quasiperiodic state hosts a doubled torus (2T(2,f)(2)) that transforms into a phase-locked period-2 orbit (P2(f)) when CDS occurs. En route to PSC, the system transitions to a forced chaotic state (CH1,2, f) followed by a phase-locked chaotic state, where f(1), f(2) and ff still coexist but their phase difference remains bounded. We find that the maximum reduction in thermoacoustic amplitude occurs near the onset of CDS, and that the critical epsilon(f) required for the onset of CDS does not vary significantly with f(f). We then use two unidirectionally coupled Anishchenko-Astakhov oscillators to phenomenologically model the experimental synchronization dynamics, including (i) the route to 1 : 1 CDS, (ii) various phase dynamics, such as phase drifting, slipping and locking, and (iii) the thermoacoustic amplitude variations in the f(f) /f(1)-epsilon(f) plane. This study extends the applicability of open-loop control further to a chaotic thermoacoustic system, demonstrating (i) the feasibility of using an existing actuation strategy to weaken aperiodic thermoacoustic oscillations, and (ii) the possibility of developing new active suppression strategies based on both established and emerging methods of chaos control.

We explore the transition to chaos in a prototypical hydrodynamic oscillator, namely a globally unstable low-density jet subjected to external time-periodic forcing. As the forcing strengthens at an off-resonant frequency, we find that the jet exhibits a sequence of nonlinear states: period-1 limit cycle → quasiperiodicity → intermittency → low-dimensional chaos. We show that the intermittency obeys type-II Pomeau–Manneville dynamics by analysing the first return map and the scaling properties of the quasiperiodic lifetimes between successive chaotic epochs. By providing experimental evidence of the type-II intermittency route to chaos in a globally unstable jet, this study reinforces the idea that strange attractors emerge via universal mechanisms in open self-excited flows, facilitating the development of instability control strategies based on chaos theory.

We use complex network analysis to investigate the vortical interactions in a bluff-body stabilized combustion system containing a turbulent lean premixed swirling flame. Using time-resolved vorticity measurements, we construct time-varying weighted spatial networks whose node strength distribution is derived from the Biot-Savart law. We find widespread evidence of scale-free topology in the vortical networks, with the most coherent flow structures acting as the primary network hubs. Crucially, we find that even after the onset of thermoacoustic instability, the scale-free topology can persist continuously in time, contrary to some suggestions from the literature. This discovery could have important implications for the design of flow controllers that rely on destroying the primary hubs of vortical networks.

We numerically investigate the forced synchronization of the self-excited flow behind a plunging airfoil in ground effect at a Reynolds number of Re = 1000. On varying the plunging amplitude and frequency, we find a rich array of nonlinear dynamics, such as a period-1 limit cycle due to natural vortex shedding as well as two-frequency quasiperiodicity on a torus attractor (⁠T2⁠). For certain non-resonant plunging frequencies without a ground surface, we find that low-dimensional chaos emerges via the Ruelle–Takens–Newhouse route. However, we find that the chaos can be suppressed by introducing a ground surface, inducing a direct transition from T2 quasiperiodicity to 1:1 phase locking as the plunging amplitude rises over the boundaries of the Arnold tongue. Apart from suppressing chaos, the ground surface also causes the lift and drag coefficients to become less sensitive to the plunging motion itself. Knowledge of the critical plunging conditions required for forced synchronization and chaos could be useful in various engineering applications, such as the design of pico air vehicles.

Human Activity Recognition (HAR) has become increasingly popular with ubiquitous computing, driven by the popularity of wearable sensors in fields like healthcare and sports. While Convolutional Neural Networks (ConvNets) have significantly contributed to HAR, they often adopt a frame-by-frame analysis, concentrating on individual frames and potentially overlooking the broader temporal dynamics inherent in human activities. To address this, we propose the intra- and inter-frame attention model. This model captures both the nuances within individual frames and the broader contextual relationships across multiple frames, offering a comprehensive perspective on sequential data. We further enrich the temporal understanding by proposing a novel time-sequential batch learning strategy. This learning strategy preserves the chronological sequence of time-series data within each batch, ensuring the continuity and integrity of temporal patterns in sensor-based HAR.

In this article, an analogue \(h^S_{\rm top}\) of topological sequence entropy is defined for Markov hom tree-shifts. We explore various aspects of \(h^S_{\rm top}\), including the existence of the limit in the definition, its relationship to topological entropy, a full characterization of null systems (with zero \(h^S_{\rm top}\) for any \(S\)), and the upper bound as well as denseness of all possible values. The relationship between this quantity and a variant called induced entropy is also breifly discussed.

The asymmetric drift (AD) tomography in different populations is helpful for us to better understand the disk kinematics, dynamics and rotation curves. Using the common stars from the LAMOST and Gaia surveys as well as circular velocity of Gaia DR3, we qualitatively explore the asymmetric drift distributions of the Galactic disk from 8$-$16\,kpc. In the $R-Z$ plane, we find that the asymmetric drift near the mid-plane of the Galactic disk is small and gradually increases with the vertical distance, which makes the asymmetric drift appear as a ``horn'' shape. Meanwhile, we reveal that high [$\alpha$/Fe] populations have larger asymmetric drift than that in the low [$\alpha$/Fe] populations. The asymmetric drift around the solar location V$_a$ = 6 km s$^{-1}$ and the asymmetric drift median value of the whole sample is 16 km s$^{-1}$. Moreover, we also find that the asymmetric drift median value in the north (20 km s$^{-1}$) of the Galactic disk is larger than that in the south (13 km s$^{-1}$), all errors are within 2 km s$^{-1}$. Furthermore, when we investigate the asymmetric drift of the Galactic disk in the mono-age stellar populations, it shows that the older stellar populations have larger asymmetric drift and velocity dispersion which are almost consistent with predictions of previous numerical models. Finally, based on the chemistry, we unveil that the median asymmetric drift of the thick disk is much higher than that of the thin disk., Comment: 13 pages and 17 figures

rameworks and methods for generating early-warning indi­cators of impending nonlinear instabilities of a dynamical system are provided. In accordance with one aspect, the framework includes a data collector and a system identifi­cation (SI) modeler. The data collector is coupled to the dynamical system and collects pre-bifurcation data. The SI modeler predicts early-warning indicators of impending nonlinear instabilities using only the pre-bifurcation data and the SI modeler includes a means for extrapolating Fokker-Planck coefficients in response to the pre-bifurcation data to generate precursors to a Hopf bifurcation and/or to identify a type of the Hopf bifurcation and/or to forecast the limit-cycle oscillation (LCO) amplitudes of a post-bifurca­tion regime. In addition, the SI modeler predicts the loca­tions of the Hopf bifurcation points, the type of the Hopf bifurcation and the LCO amplitudes of the post-bifurcation regime.

Solving the Fokker–Planck–Kolmogorov (FPK) equation and the Backward-Kolmogorov (BK) equation is a crucial task to obtain the transient response of stochastic dynamical systems. Fractional order PID (FOPID) is a new efficient controller to change the system response to be the expected one. Therefore, in this paper, the Gaussian Radial Basis Functions Neural Network (RBFNN) is proposed to solve FPK and BK equations, to obtain the transient probability density function and the reliability function for a generalized Van der Pol system under a FOPID controller. The values of the different fractional orders are analyzed to discuss the performance of the FOPID controller. A data collection strategy is adopted to deal with the associated boundary conditions by way of a one-time Monte-Carlo simulation and uniform distribution in our Gaussian RBFNN method. The advantage of this method is that the solution process of FPK and BK equations is converted into solving algebraic equations. Numerical results with regard to the transient system response prove that the Gaussian RBFNN is efficient and accurate in getting the solutions of FPK and BK equations. The order of the fractional integration and the fractional derivative are critical parameters to control the system response. Moreover, we conclude that the fractional order parameters in a FOPID controller can indeed enhance the system’s response to a certain extent and lead to bifurcation.

Solving the Fokker–Planck–Kolmogorov (FPK) equation and the Backward-Kolmogorov (BK) equation is a crucial task to obtain the transient response of stochastic dynamical systems. Fractional order PID (FOPID) is a new efficient controller to change the system response to be the expected one. Therefore, in this paper, the Gaussian Radial Basis Functions Neural Network (RBFNN) is proposed to solve FPK and BK equations, to obtain the transient probability density function and the reliability function for a generalized Van der Pol system under a FOPID controller. The values of the different fractional orders are analyzed to discuss the performance of the FOPID controller. A data collection strategy is adopted to deal with the associated boundary conditions by way of a one-time Monte-Carlo simulation and uniform distribution in our Gaussian RBFNN method. The advantage of this method is that the solution process of FPK and BK equations is converted into solving algebraic equations. Numerical results with regard to the transient system response prove that the Gaussian RBFNN is efficient and accurate in getting the solutions of FPK and BK equations. The order of the fractional integration and the fractional derivative are critical parameters to control the system response. Moreover, we conclude that the fractional order parameters in a FOPID controller can indeed enhance the system’s response to a certain extent and lead to bifurcation.

We present experimental evidence of multifractality and scale-free network topology in a noise-perturbed laminar jet operated in a globally stable regime, prior to the critical point of a supercritical Hopf bifurcation and prior to the saddle-node point of a subcritical Hopf bifurcation. For both types of bifurcation, we find that (i) the degree of multifractality peaks at intermediate noise intensities, (ii) the conditions for peak multifractality produce a complex network whose node degree distribution obeys an inverse power-law scaling with an exponent of 2 < ? < 3, indicating scale-free topology and (iii) the Hurst exponent and the global clustering coefficient can serve as early warning indicators of global instability under specific operating and forcing conditions. By characterising the noise-induced dynamics of a canonical shear flow, we demonstrate that the multifractal and scale-free network dynamics commonly observed in turbulent flows can also be observed in laminar flows under certain stochastic forcing conditions.

Xiao-qiu Yan,1 Ying-yin Zhou,2,3 Ke Zhang,4 Guan-yu Cui2,3,5 1Department of Students’ Affairs, School of Education, Wenzhou University, Wenzhou, People’s Republic of China; 2Department of Psychology, School of Education, Wenzhou University, Wenzhou, People’s Republic of China; 3Research Center for Psychology and Behavior, Wenzhou University, Wenzhou, People’s Republic of China; 4Department of Education, School of Education, Wenzhou University, Wenzhou, People’s Republic of China; 5Faculty of Psychology, Beijing Normal University, Beijing, People’s Republic of ChinaCorrespondence: Guan-yu Cui, Email xchcgy@126.comIntroduction: Some students in current society do not pursue careers related to their majors after graduation, which may be the result of low professional commitment of college students, and the teaching enthusiasm of college teachers presented in the classroom may influence students’ professional commitment. This study considered the effect of teacher enthusiasm on students’ emotional state of boredom during class and its effect on students’ engagement in learning. This correlational study aims to explore the relationship between perceived teacher enthusiasm and professional commitment as mediated by class-related boredom and learning engagement.Methods: This study is a correlational design and adopts regression analysis. The respondents were college students (n=358; 68% female, 22% male) of different grades and majors from universities in Wenzhou, China. Questionnaires about perceived teacher enthusiasm, professional commitment, class-related boredom and learning engagement were adopted to measure the study variables.Results: The results reveal that although there is no significant direct influence between perceived teacher enthusiasm and professional commitment, perceived teacher enthusiasm affects students’ professional commitment through students’ class-related boredom and learning engagement, and there is an indirect and statistic

We experimentally study the effect of rotational asymmetries in the flame response distribution on the thermoacoustic oscillations of four turbulent lean-premixed combustors coupled in a ring network. The asymmetries are created via different combinations of high-swirl (HS) and low-swirl (LS) nozzles. By analyzing the inter-combustor acoustic interactions in terms of discrete thermoacoustic modes, we find a variety of modal patterns: (i) global alternating push–pull modes emerge for most pair-wise asymmetric nozzle combinations, (ii) 2-can push–pull modes emerge for an alternating 2-fold symmetric nozzle combination, and (iii) strong mode localization and global push–push modes emerge when the HS nozzles outnumber the LS nozzles. Using a complex systems framework, we reinterpret these modal patterns as collective states, such as a weak breathing chimera, a weak anti-phase chimera, and in-phase/anti-phase synchronization. This study shows that changing the flame response distribution of a multi-combustor system, via changes in the nozzle swirl distribution, can induce a variety of modal patterns and collective states. This sets the stage for the potential use of rotational asymmetries in the passive control of thermoacoustic modes in can-annular combustion systems.

Human activity recognition (HAR) is one of the core research themes in ubiquitous and wearable computing. With the shift to deep learning (DL) based analysis approaches, it has become possible to extract high-level features and perform classification in an end-to-end manner. Despite their promising overall capabilities, DL-based HAR may suffer from overfitting due to the notoriously small, often inadequate, amounts of labeled sample data that are available for typical HAR applications. In response to such challenges, we propose ConvBoost -- a novel, three-layer, structured model architecture and boosting framework for convolutional network based HAR. Our framework generates additional training data from three different perspectives for improved HAR, aiming to alleviate the shortness of labeled training data in the field. Specifically, with the introduction of three conceptual layers--Sampling Layer, Data Augmentation Layer, and Resilient Layer -- we develop three "boosters" -- R-Frame, Mix-up, and C-Drop -- to enrich the per-epoch training data by dense-sampling, synthesizing, and simulating, respectively. These new conceptual layers and boosters, that are universally applicable for any kind of convolutional network, have been designed based on the characteristics of the sensor data and the concept of frame-wise HAR. In our experimental evaluation on three standard benchmarks (Opportunity, PAMAP2, GOTOV) we demonstrate the effectiveness of our ConvBoost framework for HAR applications based on variants of convolutional networks: vanilla CNN, ConvLSTM, and Attention Models. We achieved substantial performance gains for all of them, which suggests that the proposed approach is generic and can serve as a practical solution for boosting the performance of existing ConvNet-based HAR models. This is an open-source project, and the code can be found at https://github.com/sshao2013/ConvBoost, Comment: 21 pages

In this study we estimate the stellar ages of LAMOST DR8 Red Giant Branch (RGB) stars based on the Gradient Boosting Decision Tree algorithm (GBDT). We used 2,643 RGB stars extracted from the APOKASC-2 astero-seismological catalog as training data-set. After selecting the parameterses ([$\alpha$/Fe], [C/Fe], T$_{eff}$, [N/Fe], [C/H], log g) highly correlated with age using GBDT, we apply the same GBDT method to the new catalog of more than 590,000 stars classified as RGB stars. The test data-set shows that the median relative error is around 11.6$\%$ for the method. We also compare the predicted ages of RGB stars with other studies (e.g., based on APOGEE), and find systematic differences. The final uncertainty is about 15 to 30$\%$ compared to open clusters' ages. Then we present the spatial distribution of the RGB sample having an age determination, which could recreate the expected result, and discuss systematic biases. All these diagnostics show that one can apply the GBDT method to other stellar samples to estimate atmospheric parameters and age., Comment: 17 pages, 17 figures, Accepted for publication in The Astrophysical Journal

The realistic rendering of woven and knitted fabrics has posed significant challenges throughout many years. Previously, fiber-based micro-appearance models have achieved considerable success in attaining high levels of realism. However, rendering such models remains complex due to the intricate internal scatterings of hundreds or thousands of fibers within a yarn, requiring vast amounts of memory and time to render. In this paper, we introduce a novel framework to capture yarn-level appearance by tracing and aggregating many light paths through the underlying fiber geometry. We then employ lightweight neural networks to accurately model the aggregated BSDF, which allows for the precise modeling of a diverse array of materials while offering substantial improvements in speed and reductions in memory. Furthermore, we introduce a novel importance sampling scheme to further speed up the rate of convergence. We validate the efficacy and versatility of our framework through comparisons with preceding fiber-based shading models and by replicating various real-world fabrics. Our proposed model's enhanced performance and adaptability make it especially beneficial for film and video game production applications., Comment: 11 pages, 10 figures, 3 tables

Attenuation correction (AC) is essential for the generation of artifact-free and quantitatively accurate positron emission tomography (PET) images. However, AC of PET faces challenges including inter-scan motion and erroneous transformation of structural voxel-intensities to PET attenuation-correction factors. Nowadays, the problem of AC for quantitative PET have been solved to a large extent after the commercial availability of devices combining PET with computed tomography (CT). Meanwhile, considering the feasibility of a deep learning approach for PET AC without anatomical imaging, this paper develops a PET AC method, which uses deep learning to generate continuously valued CT images from non-attenuation corrected PET images for AC on brain PET imaging. Specifically, an invertible network combined with the variable augmentation strategy that can achieve the bidirectional inference processes is proposed for synthetic CT generation (IVNAC). To evaluate the performance of the proposed algorithm, we conducted a comprehensive study on a total of 1440 data from 37 clinical patients using comparative algorithms (such as Cycle-GAN and Pix2pix). Perceptual analysis and quantitative evaluations illustrate that the invertible network for PET AC outperforms other existing AC models, which demonstrates the potential of the proposed method and the feasibility of achieving brain PET AC without CT.

Most existing MRI reconstruction methods perform tar-geted reconstruction of the entire MR image without tak-ing specific tissue regions into consideration. This may fail to emphasize the reconstruction accuracy on im-portant tissues for diagnosis. In this study, leveraging a combination of the properties of k-space data and the diffusion process, our novel scheme focuses on mining the multi-frequency prior with different strategies to pre-serve fine texture details in the reconstructed image. In addition, a diffusion process can converge more quickly if its target distribution closely resembles the noise distri-bution in the process. This can be accomplished through various high-frequency prior extractors. The finding further solidifies the effectiveness of the score-based gen-erative model. On top of all the advantages, our method improves the accuracy of MRI reconstruction and accel-erates sampling process. Experimental results verify that the proposed method successfully obtains more accurate reconstruction and outperforms state-of-the-art methods.

We propose new Riemannian preconditioned algorithms for low-rank tensor comple-tion via the polyadic decomposition of a tensor. These algorithms exploit a non-Euclidean metric onthe product space of the factor matrices of the low-rank tensor in the polyadic decomposition form.This new metric is designed using an approximation of the diagonal blocks of the Hessian of thetensor completion cost function and thus has a preconditioning effect on these algorithms. We provethat the proposed Riemannian gradient descent algorithm globally converges to a stationary pointof the tensor completion problem, with convergence rate estimates using the \Lojasiewicz property.Numerical results on synthetic and real-world data suggest that the proposed algorithms are moreefficient in memory and time compared to state-of-the-art algorithms. Moreover, the proposed algo-rithms display a greater tolerance for overestimated rank parameters in terms of the tensor recoveryperformance and thus enable a flexible choice of the rank parameter.

We use genetic programming (GP) to discover model-free control laws for the suppression of two-frequency quasiperiodic oscillations in a prototypical self-excited thermoacoustic system. Starting from an initial generation of control laws, we rank their performance based on a predefined cost function that quantifies the trade-off between pressure amplitude reduction and actuation cost. We then breed subsequent generations of control laws via a tournament algorithm in which direct forwarding of elitism individuals occurs alongside genetic operations such as mutation, replication and crossover. We implement this GP control strategy in both closed-loop and open-loop formats, and we benchmark these against conventional open-loop control involving harmonic forcing. We find that all three control strategies can deliver similar levels of amplitude reduction, but that GP closed-loop control requires the least actuation effort. We also find that GP closed-loop control can reveal novel actuation mechanisms, offering useful insight into the interactions between the pressure and heat-release-rate fields.

Human Activity Recognition (HAR) is one of the core research areas in mobile and wearable computing. With the application of deep learning (DL) techniques such as CNN, recognizing periodic or static activities (e.g, walking, lying, cycling, etc.) has become a well studied problem. What remains a major challenge though is the sporadic activity recognition (SAR) problem, where activities of interest tend to be non periodic, and occur less frequently when compared with the often large amount of irrelevant background activities. Recent works suggested that sequential DL models (such as LSTMs) have great potential for modeling nonperiodic behaviours, and in this paper we studied some LSTM training strategies for SAR. Specifically, we proposed two simple yet effective LSTM variants, namely delay model and inverse model, for two SAR scenarios (with and without time critical requirement). For time critical SAR, the delay model can effectively exploit predefined delay intervals (within tolerance) in form of contextual information for improved performance. For regular SAR task, the second proposed, inverse model can learn patterns from the time series in an inverse manner, which can be complementary to the forward model (i.e.,LSTM), and combining both can boost the performance. These two LSTM variants are very practical, and they can be deemed as training strategies without alteration of the LSTM fundamentals. We also studied some additional LSTM training strategies, which can further improve the accuracy. We evaluated our models on two SAR and one non-SAR datasets, and the promising results demonstrated the effectiveness of our approaches in HAR applications., Comment: 11 pages

Dynamic magnetic resonance image reconstruction from incomplete k-space data has generated great research interest due to its capability to reduce scan time. Never-theless, the reconstruction problem is still challenging due to its ill-posed nature. Recently, diffusion models espe-cially score-based generative models have exhibited great potential in algorithm robustness and usage flexi-bility. Moreover, the unified framework through the variance exploding stochastic differential equation (VE-SDE) is proposed to enable new sampling methods and further extend the capabilities of score-based gener-ative models. Therefore, by taking advantage of the uni-fied framework, we proposed a k-space and image Du-al-Domain collaborative Universal Generative Model (DD-UGM) which combines the score-based prior with low-rank regularization penalty to reconstruct highly under-sampled measurements. More precisely, we extract prior components from both image and k-space domains via a universal generative model and adaptively handle these prior components for faster processing while maintaining good generation quality. Experimental comparisons demonstrated the noise reduction and detail preservation abilities of the proposed method. Much more than that, DD-UGM can reconstruct data of differ-ent frames by only training a single frame image, which reflects the flexibility of the proposed model., Comment: 12 pages, 11 figures

In this paper, we study the problem of one-shot skeleton-based action recognition, which poses unique challenges in learning transferable representation from base classes to novel classes, particularly for fine-grained actions. Existing meta-learning frameworks typically rely on the body-level representations in spatial dimension, which limits the generalisation to capture subtle visual differences in the fine-grained label space. To overcome the above limitation, we propose a part-aware prototypical representation for one-shot skeleton-based action recognition. Our method captures skeleton motion patterns at two distinctive spatial levels, one for global contexts among all body joints, referred to as body level, and the other attends to local spatial regions of body parts, referred to as the part level. We also devise a class-agnostic attention mechanism to highlight important parts for each action class. Specifically, we develop a part-aware prototypical graph network consisting of three modules: a cascaded embedding module for our dual-level modelling, an attention-based part fusion module to fuse parts and generate part-aware prototypes, and a matching module to perform classification with the part-aware representations. We demonstrate the effectiveness of our method on two public skeleton-based action recognition datasets: NTU RGB+D 120 and NW-UCLA., Comment: one-shot, action recognition, skeleton, part-aware, graph

Action Quality Assessment(AQA) is important for action understanding and resolving the task poses unique challenges due to subtle visual differences. Existing state-of-the-art methods typically rely on the holistic video representations for score regression or ranking, which limits the generalization to capture fine-grained intra-class variation. To overcome the above limitation, we propose a temporal parsing transformer to decompose the holistic feature into temporal part-level representations. Specifically, we utilize a set of learnable queries to represent the atomic temporal patterns for a specific action. Our decoding process converts the frame representations to a fixed number of temporally ordered part representations. To obtain the quality score, we adopt the state-of-the-art contrastive regression based on the part representations. Since existing AQA datasets do not provide temporal part-level labels or partitions, we propose two novel loss functions on the cross attention responses of the decoder: a ranking loss to ensure the learnable queries to satisfy the temporal order in cross attention and a sparsity loss to encourage the part representations to be more discriminative. Extensive experiments show that our proposed method outperforms prior work on three public AQA benchmarks by a considerable margin., Comment: accepted by ECCV 2022

Bayesian optimization provides an effective method to optimize expensive-to-evaluate black box functions. It has been widely applied to problems in many fields, including notably in computer science, e.g. in machine learning to optimize hyperparameters of neural networks, and in engineering, e.g. in fluid dynamics to optimize control strategies that maximize drag reduction. This paper empirically studies and compares the performance and the robustness of common Bayesian optimization algorithms on a range of synthetic test functions to provide general guidance on the design of Bayesian optimization algorithms for specific problems. It investigates the choice of acquisition function, the effect of different numbers of training samples, the exact and Monte Carlo based calculation of acquisition functions, and both single-point and multi-point optimization. The test functions considered cover a wide selection of challenges and therefore serve as an ideal test bed to understand the performance of Bayesian optimization to specific challenges, and in general. To illustrate how these findings can be used to inform a Bayesian optimization setup tailored to a specific problem, two simulations in the area of computational fluid dynamics are optimized, giving evidence that suitable solutions can be found in a small number of evaluations of the objective function for complex, real problems. The results of our investigation can similarly be applied to other areas, such as machine learning and physical experiments, where objective functions are expensive to evaluate and their mathematical expressions are unknown.

We experimentally investigate the collective behaviour of four turbulent lean-premixed combustors coupled in a ring network. We adopt a complex systems approach, treating each combustor as an individual self-excited thermoacoustic oscillator and examining how a minimal network of such oscillators can transition into synchronous and asynchronous states. Using synchronization metrics, we identify a broad range of collective behaviour, including a weak anti-phase chimera, a breathing chimera, intermittent frequency locking, and three-frequency quasiperiodicity. The discovery of chimera states in a minimal network of coupled thermoacoustic oscillators suggests that chimera control techniques could be exploited for the suppression of thermoacoustic oscillations in can-annular combustion systems. © 2022 Internoise 2022 - 51st International Congress and Exposition on Noise Control Engineering. All rights reserved.

We use genetic programming (GP) to discover data-driven control laws for the suppression of quasiperiodic oscillations in a prototypical thermoacoustic system. We rank the control laws based on a predefined cost function that accounts for the pressure amplitude and the actuation effort. We then breed subsequent generations of control laws via a tournament process. We find that GP closed-loop control is more effective than GP open-loop control and conventional periodic forcing, producing a similarly high degree of amplitude suppression but with the lowest actuation effort. We also find that GP closed-loop control can identify unforeseen actuation mechanisms, providing new insight into the physical coupling between the heat release rate and pressure fields. © 2022 Internoise 2022 - 51st International Congress and Exposition on Noise Control Engineering. All rights reserved.

Decreasing magnetic resonance (MR) image acquisition times can potentially make MR examinations more accessible. Prior arts including the deep learning models have been devoted to solving the problem of long MRI imaging time. Recently, deep generative models have exhibited great potentials in algorithm robustness and usage flexibility. Nevertheless, none of existing schemes can be learned or employed to the k-space measurement directly. Furthermore, how do the deep generative models work well in hybrid domain is also worth being investigated. In this work, by taking advantage of the deep energy-based models, we propose a k-space and image domain collaborative generative model to comprehensively estimate the MR data from under-sampled measurement. Experimental comparisons with the state-of-the-arts demonstrated that the proposed hybrid method has less error in reconstruction accuracy and is more stable under different acceleration factors, Comment: 25 pages,11 figures. arXiv admin note: text overlap with arXiv:2109.03237

In wearable-based human activity recognition (HAR) research, one of the major challenges is the large intra-class variability problem. The collected activity signal is often, if not always, coupled with noises or bias caused by personal, environmental, or other factors, making it difficult to learn effective features for HAR tasks, especially when with inadequate data. To address this issue, in this work, we proposed a Behaviour Pattern Disentanglement (BPD) framework, which can disentangle the behavior patterns from the irrelevant noises such as personal styles or environmental noises, etc. Based on a disentanglement network, we designed several loss functions and used an adversarial training strategy for optimization, which can disentangle activity signals from the irrelevant noises with the least dependency (between them) in the feature space. Our BPD framework is flexible, and it can be used on top of existing deep learning (DL) approaches for feature refinement. Extensive experiments were conducted on four public HAR datasets, and the promising results of our proposed BPD scheme suggest its flexibility and effectiveness. This is an open-source project, and the code can be found at http://github.com/Jie-su/BPD, Comment: Accepted to ACM IMWUT 2022

Yu-Chin An,1 Chia-Lin Tsai,2 Chih-Sung Liang,3 Yu-Kai Lin,2 Guan-Yu Lin,2 Chia-Kuang Tsai,2 Yi Liu,2 Sy-Jou Chen,1 Shih-Hung Tsai,1 Kuo-Sheng Hung,4 Fu-Chi Yang2 1Department of Emergency Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, Republic of China; 2Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, Republic of China; 3Department of Psychiatry, Beitou Branch, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, Republic of China; 4Center for Precision Medicine and Genomics, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, Republic of ChinaCorrespondence: Fu-Chi Yang, Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, Republic of China, Tel +886-2-87923311 # 88078, Fax +886-2-87927174, Email fuji-yang@yahoo.com.twPurpose: Although insomnia and migraine are often comorbid, the genetic association between insomnia and migraine remains unclear. This study aimed to identify susceptibility loci associated with insomnia and migraine comorbidity.Patients and Methods: We performed a genome-wide association study (GWAS) involving 1063 clinical outpatients at a tertiary hospital in Taiwan. Migraineurs with and without insomnia were genotyped using the Affymetrix Axiom Genome-Wide TWB 2.0. We performed association analyses for the entire cohort and stratified patients into the following subgroups: episodic migraine (EM), chronic migraine (CM), migraine with aura (MA), and migraine without aura (MoA). Potential correlations between SNPs and clinical indices in migraine patients with insomnia were examined using multivariate regression analysis.Results: The SNP rs1178326 in the gene HDAC9 was significantly associated with insomnia. In the EM, CM, MA, and MoA subgroups, we identified 30 additional susceptibility loci. Multivariate regression analysis showed that SNP rs1178326

Age of information (AoI) is an effective performance metric measuring the freshness of information and is popular for applications involving status update. Most of the existing works have adopted average AoI as the metric, which cannot provide strict performance guarantees. In this work, the outage probability of the peak AoI exceeding a given threshold is analyzed in a multi-source system under round robin scheduling. Two queueing disciplines are considered, namely the first-come-first-serve (FCFS) queue and the single packet queue. For FCFS, upper and lower bounds on the outage probability are derived which coincides asymptotically, characterizing its true scaling. For the single packet queue, an upper bound is derived whose effectiveness is validated by the simulation results. The analysis concretizes the common belief that single packet queueing has a better AoI performance than FCFS. Moreover, it also reveals that the two disciplines would have similar asymptotic performance when the inter-arrival time is much larger than the total transmission time.

Yu Pu,1,&ast; Xiuxiu Zhou,1,&ast; Di Zhang,1,&ast; Yu Guan,1,&ast; Yi Xia,1 Wenting Tu,1 Yang Lu,2 Weidong Zhang,2 Chi-Cheng Fu,2 Qu Fang,2 Geertruida H de Bock,3 Shiyuan Liu,1 Li Fan1 1Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, People’s Republic of China; 2Department of Scientific Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, People’s Republic of China; 3Department of Epidemiology, University Medical Center Groningen, Groningen, the Netherlands&ast;These authors contributed equally to this workCorrespondence: Shiyuan Liu; Li Fan, Department of Radiology, ChangZheng Hospital, Naval Medical University, No. 415 Fengyang Road, Shanghai, 200003, People’s Republic of China, Tel +86 21 81886012 ; Tel +86 21 81886012, Fax +86 21 63587668, Email cjr.liushiyuan@vip.163.com; fanli0930@163.comPurpose: To explore optimal threshold of FEV1% predicted value (FEV1%pre) for high-risk chronic obstructive pulmonary disease (COPD) using the parameter response mapping (PRM) based on machine learning classification model.Patients and Methods: A total of 561 consecutive non-COPD subjects who were screened for chest diseases in our hospital between August and October 2018 and who had complete questionnaire surveys, pulmonary function tests (PFT), and paired respiratory chest CT scans were enrolled retrospectively. The CT quantitative parameter for small airway remodeling was PRM, and 72 parameters were obtained at the levels of whole lung, left and right lung, and five lobes. To identify a more reasonable thresholds of FEV1% predicted value for distinguishing high-risk COPD patients from the normal, 80 thresholds from 50% to 129% were taken with a partition of 1% to establish a random forest classification model under each threshold, such that novel PFT-parameter-based high-risk criteria would be more consistent with the PRM-based machine learning classification model.Results: Machine learning-based PRM

We experimentally study the hydrodynamic instability of a lean-premixed flame stabilized behind a circular cylinder. On reducing the equivalence ratio (ϕ) at a fixed Reynolds number (ReD), we find that the flame transitions from a steady mode to a varicose mode and then to a sinuous mode. By examining time-resolved CH* chemiluminescence images and analyzing how the Strouhal number scales with ReD, we determine that the varicose mode is convectively unstable, maintained by the amplification of disturbances in the turbulent base flow, whereas the sinuous mode is globally unstable as a result of the constructive interaction between the two diametrically opposite shear layers (Bénard–von Kármán instability). We attribute the emergence of the sinuous global mode to the flame moving sufficiently far downstream with decreasing ϕ that it is out of the wavemaker region. Finally, we investigate the lean blowoff dynamics and find that local flame pinch-off, which occurs at the end of the recirculation zone, is a reliable precursor of global flame blowoff.

We use a low-order oscillator model to investigate the mutual synchronization of a thermoacoustic system consisting of two turbulent lean-premixed combustors coupled via a cross-talk tube. The model consists of two Van der Pol (VDP) oscillators coupled via dissipative and time-delay terms. We show that, despite its simplicity, the model can reproduce many of the synchronization phenomena observed experimentally, such as amplitude death, desynchronization (quasiperiodicity), synchronization (phase locking), and nonlinear energy pumping from a limit-cycle mode to a damped mode. This study shows that the mutual synchronization dynamics of a turbulent thermoacoustic system can be reproduced with just a simple coupled VDP model. This suggests that such a model could be used to identify new strategies for quenching limit-cycle oscillations in turbulent thermoacoustic systems, such as gas turbines and rocket engines.

During a tropical cyclone, windshear can occur at Hong Kong International Airport (HKIA) owing to interactions between strong winds and the complex topography of the neighboring Lantau Island. For aviation safety, it is crucial to understand how such interactions arise under realistic but controlled meteorological conditions. In this study, we numerically simulate the turbulent airflow over Lantau Island and HKIA using OpenFOAM, an open-source platform for computational fluid dynamics. We use the platform to solve the Reynolds-averaged Navier–Stokes equations via the finite volume method of discretization. We impose a neutrally stratified atmospheric boundary layer as the upwind condition, which is initialized with a logarithmic velocity profile in three different wind directions: southerly, southeasterly and easterly. For all three directions, we find multiple high-speed ∨-shaped jets emanating from the mountain gaps of Lantau Island, giving rise to headwind and crosswind variations along the glide paths of HKIA. However, we find that it is primarily the southerly and southeasterly winds that are the most conducive to windshear. For these two wind directions, we find that windshear is most likely to occur along the glide paths of the two existing runways because these are the closest to Lantau Island itself. The third runway, which is currently under construction, is the least likely to suffer from windshear. By showing how airflow disturbances arise from the complex topography of Lantau Island, this study contributes to safer and more efficient flight operations at HKIA.

Sleep is a fundamental physiological process that is essential for sustaining a healthy body and mind. The gold standard for clinical sleep monitoring is polysomnography(PSG), based on which sleep can be categorized into five stages, including wake/rapid eye movement sleep (REM sleep)/Non-REM sleep 1 (N1)/Non-REM sleep 2 (N2)/Non-REM sleep 3 (N3). However, PSG is expensive, burdensome, and not suitable for daily use. For long-term sleep monitoring, ubiquitous sensing may be a solution. Most recently, cardiac and movement sensing has become popular in classifying three-stage sleep, since both modalities can be easily acquired from research-grade or consumer-grade devices (e.g., Apple Watch). However, how best to fuse the data for the greatest accuracy remains an open question. In this work, we comprehensively studied deep learning (DL)-based advanced fusion techniques consisting of three fusion strategies alongside three fusion methods for three-stage sleep classification based on two publicly available datasets. Experimental results demonstrate important evidence that three-stage sleep can be reliably classified by fusing cardiac/movement sensing modalities, which may potentially become a practical tool to conduct large-scale sleep stage assessment studies or long-term self-tracking on sleep. To accelerate the progression of sleep research in the ubiquitous/wearable computing community, we made this project open source, and the code can be found at: https://github.com/bzhai/Ubi-SleepNet., Comment: Accepted in IMWUT for 2021 Dec issue

Modelling various spatio-temporal dependencies is the key to recognising human actions in skeleton sequences. Most existing methods excessively relied on the design of traversal rules or graph topologies to draw the dependencies of the dynamic joints, which is inadequate to reflect the relationships of the distant yet important joints. Furthermore, due to the locally adopted operations, the important long-range temporal information is therefore not well explored in existing works. To address this issue, in this work we propose LSTA-Net: a novel Long short-term Spatio-Temporal Aggregation Network, which can effectively capture the long/short-range dependencies in a spatio-temporal manner. We devise our model into a pure factorised architecture which can alternately perform spatial feature aggregation and temporal feature aggregation. To improve the feature aggregation effect, a channel-wise attention mechanism is also designed and employed. Extensive experiments were conducted on three public benchmark datasets, and the results suggest that our approach can capture both long-and-short range dependencies in the space and time domain, yielding higher results than other state-of-the-art methods. Code available at https://github.com/tailin1009/LSTA-Net., Comment: Accepted by BMVC 2021

Purpose: Although recent deep energy-based generative models (EBMs) have shown encouraging results in many image generation tasks, how to take advantage of the self-adversarial cogitation in deep EBMs to boost the performance of Magnetic Resonance Imaging (MRI) reconstruction is still desired. Methods: With the successful application of deep learning in a wide range of MRI reconstruction, a line of emerging research involves formulating an optimization-based reconstruction method in the space of a generative model. Leveraging this, a novel regularization strategy is introduced in this article which takes advantage of self-adversarial cogitation of the deep energy-based model. More precisely, we advocate for alternative learning a more powerful energy-based model with maximum likelihood estimation to obtain the deep energy-based information, represented as image prior. Simultaneously, implicit inference with Langevin dynamics is a unique property of re-construction. In contrast to other generative models for reconstruction, the proposed method utilizes deep energy-based information as the image prior in reconstruction to improve the quality of image. Results: Experiment results that imply the proposed technique can obtain remarkable performance in terms of high reconstruction accuracy that is competitive with state-of-the-art methods, and does not suffer from mode collapse. Conclusion: Algorithmically, an iterative approach was presented to strengthen EBM training with the gradient of energy network. The robustness and the reproducibility of the algorithm were also experimentally validated. More importantly, the proposed reconstruction framework can be generalized for most MRI reconstruction scenarios., Comment: 36 pages, 9 figures