The Fréchet derivatives or sensitivity kernels of the observed seismograms are fundamental to seismic full-waveform inversion (FWI). They quantitatively measure the seismogram variations caused by any physical parameter perturbation of the Earth’s subsurface. The 3-D viscoelastic tilted transversely isotropic (TTI) media are often encountered in practices due to the presence of dip thin layers, joints, fractures or cracks, orientated grains or crystallization, and water or gas saturation. To image such subsurface, we have derived explicit 3-D frequency-domain Fréchet derivatives of the seismogram spectrum with respect to 13 independent physical parameters of TTI rock, which include density, five elastic moduli, five Q-factors, and inclination and declination angles of the symmetric axis of rock structure. We have demonstrated a fully parallel implementation to compute the Fréchet derivatives and conduct synthetic subsurface imaging experiments, in which the 13 independent parameters of the subsurface targets are successfully reconstructed. The experimental results have verified the correctness and validity of the derived 3-D Fréchet derivatives for imaging viscoelastic TTI media.

In this paper, we investigate the selection of time headway to ensure robust string stability in connected and autonomous vehicle platoons in the presence of signal noise in Vehicle-to-Vehicle (V2V) communication. In particular, we consider the effect of noise in communicated vehicle acceleration from the predecessor vehicle to the follower vehicle on the selection of the time headway in predecessor-follower type vehicle platooning with a Constant Time Headway Policy (CTHP). Employing a CTHP based control law for each vehicle that utilizes onboard sensors for measurement of position and velocity of the predecessor vehicle and wireless communication network for obtaining the acceleration of the predecessor vehicle, we investigate how the implementable time headway is affected by communicated signal noise. We derive constraints on the CTHP controller gains for predecessor acceleration, velocity error and spacing error and a lower bound on the time headway which will ensure robust string stability of the platoon against signal noise. We perform comparative numerical simulations on an example to illustrate the main results.

In this paper, we provide a systematic procedure for designing cooperative adaptive cruise control (CACC) systems that are robust to parasitic actuation lag in braking and propulsion and delay in the communicated acceleration from the predecessor vehicle. In particular, we derive a tight lower bound on the employable time headway in CACC systems for guaranteeing robust string stability. The lower bound on the time headway is dependent on the upper bound on the parasitic actuation lag (τ0) and the communication delay (ℓ). The main result of the paper is that if τ0exceeds ℓ, then the employable time headway is lower bounded by τ0+ℓ. Otherwise, it is better to use adaptive cruise control (ACC) where the employable time headway is lower bounded by 2τ0. The above results on CACC systems are then extended to next-generation CACC (CACC+) systems that employ information from multiple predecessor vehicles. Several comparative numerical simulations for a representative maneuver corroborate the main results.

Keyphrase generation (KPG) aims to automatically generate a collection of phrases representing the core concepts of a given document.The dominant paradigms in KPG include ONE2SEQ and ONE2SET.Recently, there has been increasing interest in applying large language models (LLMs) to KPG.Our preliminary experiments reveal that it is challenging for a single model to excel in both recall and precision.Further analysis shows that: 1) the ONE2SET paradigm owns the advantage of high recall, but suffers from improper assignments of supervision signals during training; 2) LLMs are powerful in keyphrase selection, but existing selection methods often make redundant selections.Given these observations, we introduce a generate-then-select framework decomposing KPG into two steps, where we adopt a ONE2SET-based model as generator to produce candidates and then use an LLM as selector to select keyphrases from these candidates.Particularly, we make two important improvements on our generator and selector: 1) we design an Optimal Transport-based assignment strategy to address the above improper assignments; 2) we model the keyphrase selection as a sequence labeling task to alleviate redundant selections.Experimental results on multiple benchmark datasets show that our framework significantly surpasses state-of-theart models, especially in absent keyphrase prediction.We release our code at https:// github.

Ensuring safety is the most important factor in connected and autonomous vehicles, especially in emergency braking situations. As such, assessing the safety benefits of one information topology over other is a necessary step towards evaluating and ensuring safety. In this paper, we compare the safety benefits of a cooperative adaptive cruise control which utilizes information from one predecessor vehicle (CACC) with the one that utilizes information from multiple predecessors (CACC+) for the maintenance of spacing under an emergency braking scenario. A constant time headway policy is employed for maintenance of spacing (that includes a desired standstill spacing distance and a velocity dependent spacing distance) between the vehicles in the platoon. The considered emergency braking scenario consists of braking of the leader vehicle of the platoon at its maximum deceleration and that of the following vehicles to maintain the spacing as per CACC or CACC+. By focusing on the standstill spacing distance and utilizing Monte Carlo simulations, we assess the safety benefits of CACC+ over CACC by utilizing the following safety metrics: (1) probability of collision, (2) expected number of collisions, and (3) severity of collision (defined as the relative velocity of the two vehicles at impact). We present and provide discussion of these results.

With the rich sensing ability and extensive usage of various sensors, mobile crowdsensing (MCS) has become a new paradigm to collect sensing data for various sensing applications. In the modern urban environment, the multisource sensing information and the difference of mobile users make the sensing scenario more and more complex. To improve the applicability of different sensing scenarios, it is necessary to design a heterogeneous user recruitment mechanism for multiple heterogeneous tasks. However, most of the prior works focus on the recruitment of single-type users for homogeneous tasks without considering the heterogeneity of tasks (e.g., spatiotemporal characteristics, sensor requirements, etc.) and users (e.g., personal preferences, carrying sensors, etc). In this article, we propose the problem of heterogeneous user recruitment of multiple heterogeneous tasks (HURoTs) in MCS, with the goal of minimizing the total platform payment and maximizing the task coverage ratio. The HURoT problem is proved to be NP-hard, which is divided into multiple subproblems in different sensing cycles. Moreover, by introducing the user’s utility function, we propose three greedy-based user recruitment algorithms to obtain near-optimal solutions. Extensive experiments are conducted to validate the effectiveness of the proposed schemes.

Mobile crowdsensing (MCS) is an emerging paradigm that leverages pervasive smart terminals equipped with various embedded sensors to collect sensory data for wide applications. As the sensing scale increases in MCS, the design of efficient task allocation becomes crucial. However, many prior task allocation schemes, which ignore the time for task-performing, are not applicable to the scenario where mobile users with limited time budgets are able to undertake multiple sensing tasks. In this article, we focus on the task allocation in time dependent crowdsensing systems and formulate the time dependent task allocation problem, in which both the sensing duration and the user's sensing capacity are considered. We prove that the task allocation problem is NP-hard and propose an efficient task allocation algorithm called optimized allocation scheme of time-dependent tasks (OPAT), which can maximize the sensing capacity of each mobile user. The extensive simulations are conducted to demonstrate the effectiveness of the proposed OPAT scheme.

The Exemplar Memory (EM) design has shown its effectiveness in facilitating the unsupervised person re-identification (RE-ID). However, there are obvious defects in the update strategies with most existing results, such as the inability to eliminate static errors and ensure convergence stability of learning. To address these issues, in this paper, we propose a novel center feature learning scheme to improve the update strategies of the traditional EM design for unsupervised RE-ID problems. First, the EM module is regarded as a center feature of a cluster of images, then the goal is transformed into pulling the similar images close to while pushing the dissimilar images away from the center feature space. Second, in order to provide effective guidelines on reducing static errors, we propose an error-memory module to improve the central feature learning performances. In addition, an error-prediction module is designed as well to ensure the stability of convergence. Besides, a camera-invariance learning strategy is also introduced to further improve the proposed algorithm. Finally, extensive comparative experiments are conducted on Market-1501 and DukeMTMC-reID datasets to demonstrate the effectiveness and improvements of the proposed method over existing results. The code of this work is available at https://github.com/binquanwang/CFL_master.

Person re-identification (re-ID) aims to identify the same persons' images across different cameras by deploying a trained re-ID model. Nevertheless, the domain bias between different datasets can lead to considerably low identification accuracy when directly applying a trained re-ID model from one dataset to another. Therefore, in order to develop a kind of more transferable re-ID methods with strong robustness, this paper proposes a fast momentum contrast learning framework, which consists of a teacher encoder and a fast momentum encoder, from a reinforcing visual representation learning point of view. The fast momentum encoder produced by the teacher encoder is employed to build a dynamic dictionary and then design the contrast learning loss function together with the teacher encoder. In addition, both the individual loss and the clustering loss are adopted for obtaining the optimal performances in unsupervised person re-ID models. The proposed framework achieves one stage of end-to-end training and experimental results demonstrate that our identification accuracy outperforms existing advanced methods.

This paper considers the problem of asynchronous H∞control for networked Markovian jump systems subject to probabilistic packet dropouts and communication delays in the measurement channel. A new dynamic output-feedback-based asynchronous controller is proposed wherein the dynamic output-feedback controller modes need not synchronize with the system modes. By utilizing results from stochastic Lyapunov-Krasovskii stability theory, sufficient conditions in terms of matrix inequalities are derived such that the closed-loop networked Markovian jump system is stochastically stable and achieves the prescribed H∞performance. Using the Schur complement technique and under the assumption that the input matrix is full rank, the sufficient condition is reduced to a linear matrix inequality and the dynamic output-feedback-based asynchronous controller is synthesized. A detailed numerical example with simulation results are presented to evaluate the proposed controller design scheme.

This paper proposes a two-channel periodic event-triggered observed-based repetitive control strategy to track a periodic reference signal subject to limited communication capacity. Under the designed two-channel periodic event-triggering framework, within any two consecutive event-triggering instants in each event-triggering channel, not only the output measurements to the state observer but also the observer states to the repetitive controller structure are kept unchanged via a zero-order hold (ZOH) which can substantially alleviate the communication burden when the output measurements and the observer states do not change significantly. By employing the input delay approach, the overall system consisting of the physical plant, the state observer, the repetitive controller, and the two-channel periodic event-triggering mechanisms is modeled as a closed-loop time-varying delay system. Sufficient conditions in terms of linear matrix inequalities are derived to ensure the closed-loop system to be asymptotically stable with a prescribed H∞attenuation performance level for an exogenous disturbance input. The controller gains, observer gains, and the event-triggering parameters are synthesized by using a matrix decomposition technique. A numerical example is provided to evaluate the proposed design approach.