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Scribble supervised salient object detection (SSSOD) constructs segmentation ability of attractive objects from surroundings under the supervision of sparse scribble labels. For the better segmentation, depth and thermal infrared modalities serve as the supplement to RGB images in the complex scenes. Existing methods specifically design various feature extraction and multi-modal fusion strategies for RGB, RGB-Depth, RGB-Thermal, and Visual-Depth-Thermal image input respectively, leading to similar model flood. As the recently proposed Segment Anything Model (SAM) possesses extraordinary segmentation and prompt interactive capability, we propose an SSSOD family based on SAM, named SSFam, for the combination input with different modalities. Firstly, different modal-aware modulators are designed to attain modal-specific knowledge which cooperates with modal-agnostic information extracted from the frozen SAM encoder for the better feature ensemble. Secondly, a siamese decoder is tailored to bridge the gap between the training with scribble prompt and the testing with no prompt for the stronger decoding ability. Our model demonstrates the remarkable performance among combinations of different modalities and refreshes the highest level of scribble supervised methods and comes close to the ones of fully supervised methods. https://github.com/liuzywen/SSFam, Comment: Accepted by TMM 2024

In recent years, radar systems have advanced significantly, offering environmental adaptation and multi-task capabilities. These developments pose new challenges for electronic intelligence (Elint) and electronic support measures (ESM), which need to identify and interpret sophisticated radar behaviors. This paper introduces a Deep Multi-Intentional Inverse Reinforcement Learning (DMIIRL) method for the identification and inverse cognition of cognitive multi-function radars (CMFR). Traditional Inverse Reinforcement Learning (IRL) methods primarily target single reward functions, but the complexity of CMFRs necessitates multiple reward functions to fully encapsulate their behavior. To this end, we develop a method that integrates IRL with Expectation-Maximization (EM) to concurrently handle multiple reward functions, offering better trajectory clustering and reward function estimation. Simulation results demonstrate the superiority of the proposed method over baseline approaches.

Escape of active agents from metastable states is of great interest in statistical and biological physics. In this study, we investigate the escape of a flexible active ring, composed of active Brownian particles, from a flat attractive surface using Brownian dynamics simulations. To systematically explore the effects of activity, persistence time, and the shape of attractive potentials, we calculate escape time and effective temperature. We observe two distinct escape mechanisms: Kramers-like thermal activation at small persistence times and the maximal force problem at large persistence time, where escape time is determined by persistence time. The escape time explicitly depends on the shape of the potential barrier at high activity and large persistence time. Moreover, when the propulsion force is biased along the ring's contour, escape becomes more difficult and is primarily driven by thermal noise. Our findings highlight that, despite its intricate configuration, the active ring can be effectively modeled as a self-propelled Brownian particle when studying its escape from a smooth surface.

It has been supposed that the interplay of elasticity and activity plays a key role in triggering the non-equilibrium behaviors in biological systems. However, the experimental model system is missing to investigate the spatiotemporally dynamical phenomena. Here, a model system of an active chain, where active eccentric-disks are linked by a spring, is designed to study the interplay of activity, elasticity, and friction. Individual active chain exhibits longitudinal and transverse motion, however, it starts to self-rotate when pinning one end, and self-beats when clamping one end. Additionally, our eccentric-disk model can qualitatively reproduce such behaviors and explain the unusual self-rotation of the first disk around its geometric center. Further, the structure and dynamics of long chains were studied via simulations without steric interactions. It was found that hairpin conformation emerges in free motion, while in the constrained motions, the rotational and beating frequencies scale with the flexure number (the ratio of self-propelling force to bending rigidity), ~4/3. Scaling analysis suggests that it results from the balance between activity and energy dissipation. Our findings show that topological constraints play a vital role in non-equilibrium synergy behavior.

Steam condensation on the surface of a solid is a widely observed mode of energy transfer in nature and various industrial applications. The condensation efficiency is closely related to the material properties and geometric morphology of the solid surface, as well as the method of liquid removal. Despite many surface modification strategies at the micro and nano levels having been employed to enhance steam condensation, understanding how to regulate steam condensation and liquid removal on complex surface morphologies remains incomplete. Here, we report a method that uses superhydrophilic and superhydrophobic coatings as well as spiked surfaces to achieve efficient steam condensation and rapid removal of the liquid. We reveal that on a copper plate with millimeter-scale spikes, hydrophobic spiked surfaces facilitate the dropwise condensation, while hydrophilic bottom grooves promote liquid spreading, and the suction in capillary gaps can promptly remove the condensate liquid. This method achieves a high condensation efficiency without relying on gravity. Additionally, we demonstrate that the condensation on spiked surfaces has a significant suction effect, as it continuously attracts the steam flow, thereby altering the flow direction. This finding provides a new approach for efficient steam condensation technology and opens up a promising pathway for providing circulation power and improving condensation efficiency in phase-change heat transfer devices such as heat pipes.

We investigate the mixing properties of solutions to the stochastic transport equation $d u= \circ d W \cdot\nabla u$, where the driving noise $W(t,x)$ is white in time, colored and divergence-free in space. Furthermore, we prove the dissipation enhancement in the presence of a small viscous term. Applying our results, we also derive the mixing properties for a regularized stochastic 2D Euler equation., Comment: 34 pages

The growing scarcity of spectrum resources, wideband spectrum sensing is required to process a prohibitive volume of data at a high sampling rate. For some applications, spectrum estimation only requires second-order statistics. In this case, a fast power spectrum sensing solution is proposed based on the generalized coprime sampling. By exploring the sensing vector inherent structure, the autocorrelation sequence of inputs can be reconstructed from sub-Nyquist samples by only utilizing the parallel Fourier transform and simple multiplication operations. Thus, it takes less time than the state-of-the-art methods while maintaining the same performance, and it achieves higher performance than the existing methods within the same execution time, without the need for pre-estimating the number of inputs. Furthermore, the influence of the model mismatch has only a minor impact on the estimation performance, which allows for more efficient use of the spectrum resource in a distributed swarm scenario. Simulation results demonstrate the low complexity in sampling and computation, making it a more practical solution for real-time and distributed wideband spectrum sensing applications.

Recently, learned video compression has achieved exciting performance. Following the traditional hybrid prediction coding framework, most learned methods generally adopt the motion estimation motion compensation (MEMC) method to remove inter-frame redundancy. However, inaccurate motion vector (MV) usually lead to the distortion of reconstructed frame. In addition, most approaches ignore the spatial and channel redundancy. To solve above problems, we propose a motion-aware and spatial-temporal-channel contextual coding based video compression network (MASTC-VC), which learns the latent representation and uses variational autoencoders (VAEs) to capture the characteristics of intra-frame pixels and inter-frame motion. Specifically, we design a multiscale motion-aware module (MS-MAM) to estimate spatial-temporal-channel consistent motion vector by utilizing the multiscale motion prediction information in a coarse-to-fine way. On the top of it, we further propose a spatial-temporal-channel contextual module (STCCM), which explores the correlation of latent representation to reduce the bit consumption from spatial, temporal and channel aspects respectively. Comprehensive experiments show that our proposed MASTC-VC is surprior to previous state-of-the-art (SOTA) methods on three public benchmark datasets. More specifically, our method brings average 10.15\% BD-rate savings against H.265/HEVC (HM-16.20) in PSNR metric and average 23.93\% BD-rate savings against H.266/VVC (VTM-13.2) in MS-SSIM metric., Comment: 12pages,12 figures

As the trend towards small, safe, smart, speedy and swarm development grows, unmanned aerial vehicles (UAVs) are becoming increasingly popular for a wide range of applications. In this letter, the challenge of wideband spectrum acquisition for the UAV swarms is studied by proposing a processing method that features lower power consumption, higher compression rates, and a lower signal-to-noise ratio. Our system is equipped with multiple UAVs, each with a different sub-sampling rate. That allows for frequency backetization and estimation based on sparse Fourier transform theory. Unlike other techniques, the collisions and iterations caused by non-sparsity environ-ments are considered. We introduce sparse coding Fourier transform to address these issues. The key is to code the entire spectrum and decode it through spectrum correlation in the code. Simulation results show that our proposed method performs well in acquiring both narrowband and wideband signals simultaneously, compared to the other methods.

Distributed unmanned aerial vehicle (UAV) swarms are formed by multiple UAVs with increased portability, higher levels of sensing capabilities, and more powerful autonomy. These features make them attractive for many recent applica-tions, potentially increasing the shortage of spectrum resources. In this paper, wideband spectrum sensing augmented technology is discussed for distributed UAV swarms to improve the utilization of spectrum. However, the sub-Nyquist sampling applied in existing schemes has high hardware complexity, power consumption, and low recovery efficiency for non-strictly sparse conditions. Thus, the Nyquist folding receiver (NYFR) is considered for the distributed UAV swarms, which can theoretically achieve full-band spectrum detection and reception using a single analog-to-digital converter (ADC) at low speed for all circuit components. There is a focus on the sensing model of two multichannel scenarios for the distributed UAV swarms, one with a complete functional receiver for the UAV swarm with RIS, and another with a decentralized UAV swarm equipped with a complete functional receiver for each UAV element. The key issue is to consider whether the application of RIS technology will bring advantages to spectrum sensing and the data fusion problem of decentralized UAV swarms based on the NYFR architecture. Therefore, the property for multiple pulse reconstruction is analyzed through the Gershgorin circle theorem, especially for very short pulses. Further, the block sparse recovery property is analyzed for wide bandwidth signals. The proposed technology can improve the processing capability for multiple signals and wide bandwidth signals while reducing interference from folded noise and subsampled harmonics. Experiment results show augmented spectrum sensing efficiency under non-strictly sparse conditions.

The limited availability of spectrum resources has been growing into a critical problem in wireless communications, remote sensing, and electronic surveillance, etc. To address the high-speed sampling bottleneck of wideband spectrum sensing, a fast and practical solution of power spectrum estimation for Nyquist folding receiver (NYFR) is proposed in this paper. The NYFR architectures is can theoretically achieve the full-band signal sensing with a hundred percent of probability of intercept. But the existing algorithm is difficult to realize in real-time due to its high complexity and complicated calculations. By exploring the sub-sampling principle inherent in NYFR, a computationally efficient method is introduced with compressive covariance sensing. That can be efficient implemented via only the non-uniform fast Fourier transform, fast Fourier transform, and some simple multiplication operations. Meanwhile, the state-of-the-art power spectrum reconstruction model for NYFR of time-domain and frequency-domain is constructed in this paper as a comparison. Furthermore, the computational complexity of the proposed method scales linearly with the Nyquist-rate sampled number of samples and the sparsity of spectrum occupancy. Simulation results and discussion demonstrate that the low complexity in sampling and computation is a more practical solution to meet the real-time wideband spectrum sensing applications.