Search

The Deffuant-Weisbuch (DW) model is a well-known bounded-confidence opinion dynamics that has attracted wide interest. Although the heterogeneous DW model has been studied by simulations over $20$ years, its convergence proof is open. Our previous paper \cite{GC-WS-WM-FB:20} solves the problem for the case of uniform weighting factors greater than or equal to $1/2$, but the general case remains unresolved. This paper considers the DW model with heterogeneous confidence bounds and heterogeneous (unconstrained) weighting factors and shows that, with probability one, the opinion of each agent converges to a fixed vector. In other words, this paper resolves the convergence conjecture for the heterogeneous DW model. Our analysis also clarifies how the convergence speed may be arbitrarily slow under certain parameter conditions.

This paper exploits the multi-antenna technique to enhance the covert communication performance in a relay system, where a source S conducts covert communication with a destination D via a relay R, subjecting to the detections of transmissions in the two hops from a single-antenna warden W. To demonstrate the performance gain from adopting the multi-antenna technique, we first consider the scenario when S, R and D all adopt single antenna, and apply hypothesis testing and statistics theories to develop a theoretical framework for the covert performance modeling in terms of detection error probability (DEP) and covert throughput. We then consider the scenario when S, R and D all adopt multiple antennas, and apply the hypothesis testing, statistics and matrix theories to develop corresponding theoretical framework for performance modeling. We further explore the optimal designs of the target rate and transmit power for covert throughput maximization under above both scenarios, subjecting to the constraints of covertness, reliability and transmit power. To solve the optimization problems, we employ Karushi-Kuhn-Tucker (KKT) conditions method in the single antenna scenario and a search algorithm in the multi-antenna scenario. Finally, we provide extensive numerical results to illustrate how the multi-antenna technique can enhance the covert performance in two-hop relay systems.

The complexity of clouds, particularly in terms of texture detail at high resolutions, has not been well explored by most existing cloud detection networks. This paper introduces the High-Resolution Cloud Detection Network (HR-cloud-Net), which utilizes a hierarchical high-resolution integration approach. HR-cloud-Net integrates a high-resolution representation module, layer-wise cascaded feature fusion module, and multi-resolution pyramid pooling module to effectively capture complex cloud features. This architecture preserves detailed cloud texture information while facilitating feature exchange across different resolutions, thereby enhancing overall performance in cloud detection. Additionally, a novel approach is introduced wherein a student view, trained on noisy augmented images, is supervised by a teacher view processing normal images. This setup enables the student to learn from cleaner supervisions provided by the teacher, leading to improved performance. Extensive evaluations on three optical satellite image cloud detection datasets validate the superior performance of HR-cloud-Net compared to existing methods.The source code is available at \url{https://github.com/kunzhan/HR-cloud-Net}., Comment: Journal of Electronic Imaging

In text-to-video (T2V) generation, significant attention has been directed toward its development, yet unifying discrete and continuous grounding conditions in T2V generation remains under-explored. This paper proposes a Grounded text-to-Video generation framework, termed GVDIFF. First, we inject the grounding condition into the self-attention through an uncertainty-based representation to explicitly guide the focus of the network. Second, we introduce a spatial-temporal grounding layer that connects the grounding condition with target objects and enables the model with the grounded generation capacity in the spatial-temporal domain. Third, our dynamic gate network adaptively skips the redundant grounding process to selectively extract grounding information and semantics while improving efficiency. We extensively evaluate the grounded generation capacity of GVDIFF and demonstrate its versatility in applications, including long-range video generation, sequential prompts, and object-specific editing.

Referring Expression Comprehension (REC) aims to localize the target objects specified by free-form natural language descriptions in images. While state-of-the-art methods achieve impressive performance, they perform a dense perception of images, which incorporates redundant visual regions unrelated to linguistic queries, leading to additional computational overhead. This inspires us to explore a question: can we eliminate linguistic-irrelevant redundant visual regions to improve the efficiency of the model? Existing relevant methods primarily focus on fundamental visual tasks, with limited exploration in vision-language fields. To address this, we propose a coarse-to-fine iterative perception framework, called ScanFormer. It can iteratively exploit the image scale pyramid to extract linguistic-relevant visual patches from top to bottom. In each iteration, irrelevant patches are discarded by our designed informativeness prediction. Furthermore, we propose a patch selection strategy for discarded patches to accelerate inference. Experiments on widely used datasets, namely RefCOCO, RefCOCO+, RefCOCOg, and ReferItGame, verify the effectiveness of our method, which can strike a balance between accuracy and efficiency., Comment: Accepted by CVPR2024

In this paper, we prove that an ancient smooth curve shortening flow with finite-entropy embedded in $\mathbb{R}^2$ has a unique tangent flow at infinity. To this end, we show that its rescaled flows backwardly converge to a line with multiplity $m\geq 3$ exponentially fast in any compact region, unless the flow is a shrinking circle, a static line, a paper clip, or a translating grim reaper. In addition, we figure out the exact numbers of tips, vertices, and inflection points of the curves at negative enough time. Moreover, the exponential growth rate of graphical radius and the convergence of vertex regions to grim reaper curves will be shown.

This study introduces a two-degree-of-freedom piezoelectric energy harvester designed to harness rotational motion as an energy source. The harvester is built using a cut-out beam, which enables the first two resonant frequencies to be closely located in the low-frequency range. A distributed continuous model is developed and validated with experimental results. As the beam undergoes significant displacement due to rotational excitations, the geometric nonlinearity arising from longitudinal displacement is considered in the model to enhance its accuracy. It is observed that as the rotating speed increases, the increased centrifugal force causes the first resonant frequency to rise while the second resonant frequency decreases. The rotation-specific mode veering and the interchange of the first two modes are discussed. This study explores the potential to expand the bandwidth of the harvester using two types of nonlinear external force, namely mechanical stoppers and magnetic force. The results indicate that the proposed harvester can broaden the bandwidth of the first and second resonant frequencies. This research addresses the gap of combining multimodal and nonlinear force methods in rotation-al piezoelectric energy harvesting.

In this paper, we construct solutions of Lagrangian mean curvature flow which exist and are embedded for all time, but form an infinite-time singularity and converge to an immersed special Lagrangian as $t\to\infty$. In particular, the flow decomposes the initial data into a union of special Lagrangians intersecting at one point. This result shows that infinite-time singularities can form in the Thomas--Yau `semi-stable' situation. A precise polynomial blow-up rate of the second fundamental form is also shown. The infinite-time singularity formation is obtained by a perturbation of an approximate family $N^{\varepsilon(t)}$ constructed by gluing in special Lagrangian `Lawlor necks' of size $\varepsilon(t)$, where the dynamics of the neck size $\varepsilon(t)$ are driven by the obstruction for the existence of nearby special Lagrangians to $N^{\varepsilon(t)}$. This is inspired by the work of Brendle and Kapouleas regarding ancient solutions of the Ricci flow., Comment: minor modifications; add a precise polynomial blow-up rate of the second fundamental form

We perform the first convergent Bayesian global fits of 4D Composite Higgs Models with partially-composite third generation quarks and leptons based on the minimal $SO(5) \rightarrow SO(4)$ symmetry breaking pattern. We consider two models with the $\tau$ lepton and its associated neutrino in different representations of $SO(5)$. Fitting each model with a wide array of experimental constraints allows us to analyse the Bayesian evidence and currently-observed fine-tuning of each model by calculating the Kullback-Leibler divergence between their respective priors and posteriors. Notably both models are found to be capable of satisfying all constraints simultaneously at the $3\sigma$ level at scales of $< 5$ TeV. From a Bayesian viewpoint of naturalness the model with leptons in the $\mathbf{14}$ and $\mathbf{10}$ representations is preferred over those in the $\mathbf{5}$ representation due to its lower fine-tuning. Finally, we consider the experimental signatures for the preferred parameters in these models, including lepton partner decay signatures and gluon-fusion produced Higgs signal strengths, and discuss their potential phenomenology at future high-luminosity LHC runs., Comment: 32+12 pages, 13+8 figures; version 2 with improved content following comments from referee; ver 3 improved clarity

For a multi-attribute decision making (MADM) problem, the information of alternatives under different attributes is given in the form of intuitionistic fuzzy number(IFN). Intuitionistic fuzzy set (IFS) plays an important role in dealing with un-certain and incomplete information. The similarity measure of intuitionistic fuzzy sets (IFSs) has always been a research hotspot. A new similarity measure of IFSs based on the projection technology and cosine similarity measure, which con-siders the direction and length of IFSs at the same time, is first proposed in this paper. The objective of the presented pa-per is to develop a MADM method and medical diagnosis method under IFS using the projection technology and cosine similarity measure. Some examples are used to illustrate the comparison results of the proposed algorithm and some exist-ing methods. The comparison result shows that the proposed algorithm is effective and can identify the optimal scheme accurately. In medical diagnosis area, it can be used to quickly diagnose disease. The proposed method enriches the exist-ing similarity measure methods and it can be applied to not only IFSs, but also other interval-valued intuitionistic fuzzy sets(IVIFSs) as well.

TianQin (TQ) project plans to deploy three satellites in space around the Earth to measure the displacement change of test masses caused by gravitational waves via laser interferometry. The requirement of the acceleration noise of the test mass is on the order of $10^{-15}~\,{\rm m}\,{\rm s}^{-2}\,{\rm Hz}^{-1/2}$ in the sensitive frequency range of TQ, %the extremely precise acceleration measurement requirements make it necessary to investigate acceleration noise due to space magnetic fields. which is so stringent that the acceleration noise caused by the interaction of the space magnetic field with the test mass needs to be investigated. In this work, by using the Tsyganenko model, a data-based empirical space magnetic field model, we obtain the magnetic field distribution around TQ's orbit spanning two solar cycles in 23 years from 1998 to 2020. With the obtained space magnetic field, we derive the distribution and amplitude spectral densities (ASDs) of the acceleration noise of TQ in 23 years. Our results reveal that the average values of the ratio of the acceleration noise cauesd by the space magnetic field to the requirements of TQ at 1 mHz ($R_{\rm 1mHz}$) and 6 mHz ($R_{\rm 6mHz}$) are 0.123$\pm$0.052 and 0.027$\pm$0.013, respectively. The occurence probabilities of $R_{\rm 1mHz}>0.2$ and $>0.3$ are only 7.9\% and 1.2\%, respectively, and $R_{\rm 6mHz}$ never exceeds 0.2., Comment: 16 pages, 10 figures, Accepted by PRD

Non-Hermitian boundaries commonly take place in many open quantum systems locally coupled to a surrounding environment. Here, we reveal a type of non-Hermitian effect induced by non-Hermitian boundaries, the non-Hermitian proximity effect (NHPE), which describes the penetration of non-Hermiticity from the boundary into the bulk. For gapped quantum systems, the NHPE generates in-gap states with imaginary eigenenergies, termed ``imaginary in-gap states". The imaginary in-gap states are localized at the system boundary and decay into the bulk, analogous to the behaviors of the conventional impurity states. However, in contrast to impurity states, the imaginary in-gap states exhibit distinct dynamical behaviors under time-evolution. Moreover, they are physically manifested as corner modes under open boundaries, as a combined result of the non-Hermitian skin effect (NHSE) and NHPE. These results not only uncover implicit similarities between quantum systems with non-Hermitian boundaries and impurity physics, but also point to intriguing non-Hermitian phenomena broadly relevant to open quantum systems., Comment: 10 pages, 5 figures

Type II radio bursts are often associated with coronal shocks that are typically driven by coronal mass ejections (CMEs) from the Sun. Here, we conduct a case study of a type II radio burst that is associated with a C4.5 class flare and a blowout jet, but without the presence of a CME. The blowout jet is observed near the solar disk center in the extreme-ultraviolet (EUV) passbands with different characteristic temperatures. Its evolution involves an initial phase and an ejection phase with a velocity of 560 km/s. Ahead of the jet front, an EUV wave propagates at a projected velocity of 403 km/s in the initial stage. The moving velocity of the source region of the type II radio burst is estimated to be 641 km/s, which corresponds to the shock velocity against the coronal density gradient. The EUV wave and the type II radio burst are closely related to the ejection of the blowout jet, suggesting that both are likely the manifestation of a coronal shock driven by the ejection of the blowout jet. The type II radio burst likely starts lower than those associated with CMEs. The combination of the velocities of the radio burst and the EUV wave yields a modified shock velocity at 757 km/s. The Alfven Mach number is in the range of 1.09-1.18, implying that the shock velocity is 10%-20% larger than the local Alfven velocity., Comment: Accepted by ApJ, 17 pages, and 6 figures

In this study, we discovered that the energy gap above the vacuum level in the projected bulk band structure of Ag(100) prevents electrons in the first-order field emission resonance (FER) from inducing the surface plasmons. This mechanism allows light emission from FER to reveal characteristics of triplet states and Auger-type excitation resulting from two-electron tunneling in FER. According to optical spectra, surface plasmons can be induced by electrons in the zeroth-order FER. However, corresponding radiative decay can also trigger Auger-type excitation, whose energy state is influenced by the sharpness-dependent image potential acting on the scanning tunneling microscope tip., Comment: 19 pages, 8 figures

Although the impressive performance in visual grounding, the prevailing approaches usually exploit the visual backbone in a passive way, i.e., the visual backbone extracts features with fixed weights without expression-related hints. The passive perception may lead to mismatches (e.g., redundant and missing), limiting further performance improvement. Ideally, the visual backbone should actively extract visual features since the expressions already provide the blueprint of desired visual features. The active perception can take expressions as priors to extract relevant visual features, which can effectively alleviate the mismatches. Inspired by this, we propose an active perception Visual Grounding framework based on Language Adaptive Weights, called VG-LAW. The visual backbone serves as an expression-specific feature extractor through dynamic weights generated for various expressions. Benefiting from the specific and relevant visual features extracted from the language-aware visual backbone, VG-LAW does not require additional modules for cross-modal interaction. Along with a neat multi-task head, VG-LAW can be competent in referring expression comprehension and segmentation jointly. Extensive experiments on four representative datasets, i.e., RefCOCO, RefCOCO+, RefCOCOg, and ReferItGame, validate the effectiveness of the proposed framework and demonstrate state-of-the-art performance., Comment: Accepted by CVPR2023

Different from universal object detection, referring expression comprehension (REC) aims to locate specific objects referred to by natural language expressions. The expression provides high-level concepts of relevant visual and contextual patterns, which vary significantly with different expressions and account for only a few of those encoded in the REC model. This leads us to a question: do we really need the entire network with a fixed structure for various referring expressions? Ideally, given an expression, only expression-relevant components of the REC model are required. These components should be small in number as each expression only contains very few visual and contextual clues. This paper explores the adaptation between expressions and REC models for dynamic inference. Concretely, we propose a neat yet efficient framework named Language Adaptive Dynamic Subnets (LADS), which can extract language-adaptive subnets from the REC model conditioned on the referring expressions. By using the compact subnet, the inference can be more economical and efficient. Extensive experiments on RefCOCO, RefCOCO+, RefCOCOg, and Referit show that the proposed method achieves faster inference speed and higher accuracy against state-of-the-art approaches., Comment: Accepted by AAAI2023

Gait recognition, which aims at identifying individuals by their walking patterns, has recently drawn increasing research attention. However, gait recognition still suffers from the conflicts between the limited binary visual clues of the silhouette and numerous covariates with diverse scales, which brings challenges to the model's adaptiveness. In this paper, we address this conflict by developing a novel MetaGait that learns to learn an omni sample adaptive representation. Towards this goal, MetaGait injects meta-knowledge, which could guide the model to perceive sample-specific properties, into the calibration network of the attention mechanism to improve the adaptiveness from the omni-scale, omni-dimension, and omni-process perspectives. Specifically, we leverage the meta-knowledge across the entire process, where Meta Triple Attention and Meta Temporal Pooling are presented respectively to adaptively capture omni-scale dependency from spatial/channel/temporal dimensions simultaneously and to adaptively aggregate temporal information through integrating the merits of three complementary temporal aggregation methods. Extensive experiments demonstrate the state-of-the-art performance of the proposed MetaGait. On CASIA-B, we achieve rank-1 accuracy of 98.7%, 96.0%, and 89.3% under three conditions, respectively. On OU-MVLP, we achieve rank-1 accuracy of 92.4%., Comment: Accepted by ECCV2022

Gait is one of the most promising biometrics that aims to identify pedestrians from their walking patterns. However, prevailing methods are susceptible to confounders, resulting in the networks hardly focusing on the regions that reflect effective walking patterns. To address this fundamental problem in gait recognition, we propose a Generative Counterfactual Intervention framework, dubbed GaitGCI, consisting of Counterfactual Intervention Learning (CIL) and Diversity-Constrained Dynamic Convolution (DCDC). CIL eliminates the impacts of confounders by maximizing the likelihood difference between factual/counterfactual attention while DCDC adaptively generates sample-wise factual/counterfactual attention to efficiently perceive the sample-wise properties. With matrix decomposition and diversity constraint, DCDC guarantees the model to be efficient and effective. Extensive experiments indicate that proposed GaitGCI: 1) could effectively focus on the discriminative and interpretable regions that reflect gait pattern; 2) is model-agnostic and could be plugged into existing models to improve performance with nearly no extra cost; 3) efficiently achieves state-of-the-art performance on arbitrary scenarios (in-the-lab and in-the-wild)., Comment: Accepted by CVPR2023

The stable cooperation ratio of spatial evolutionary games has been widely studied using simulations or approximate analysis methods. However, sometimes such ``stable'' cooperation ratios obtained via approximate methods might not be actually stable, but correspond to quasi-equilibriums instead. We find that various classic game models, like the evolutionary snowdrift game, evolutionary prisoner's dilemma, and spatial public goods game on square lattices and scale-free networks, exhibit the phase transition in convergence time to the equilibrium state. Moreover, mathematical principles are provided to explain the phase transition of convergence time and quasi-equilibrium of cooperation ratio. The findings explain why and when cooperation and defection have a long-term coexistence., Comment: 11 pages, 7 figures

Revealing the energy and spatial characteristics of impurity induced states in superconductors is essential for understanding their mechanism and fabricating new quantum state by manipulating impurities. Here by using high-resolution scanning tunneling microscopy/spectroscopy, we investigated the spatial distribution and magnetic field response of the impurity states in (Li1-xFex)OHFeSe. We detected two pairs of strong in-gap states on the "dumbbell" shaped defects. They display clear damped oscillations with different phase shifts and a direct phase-energy correlation. These features have long been predicted for classical Yu-Shiba-Rusinov (YSR) state, which are demonstrated here with unprecedented resolution for the first time. Moreover, upon applying magnetic field, all the in-gap state peaks remarkably split into two rather than shift, and the splitting strength is field orientation dependent. Via detailed numerical model calculations, we found such anisotropic splitting behavior can be naturally induced by a high-spin impurity coupled to anisotropic environment, highlighting how magnetic anisotropy affects the behavior of YSR states., Comment: Main text with supplementary (accepted by Phys. Rev. Lett.)

As the rapid development of Intelligent Tutoring Systems (ITS) in the past decade, tracing the students' knowledge state has become more and more important in order to provide individualized learning guidance. This is the main idea of Knowledge Tracing (KT), which models students' mastery of knowledge concepts (KCs, skills needed to solve a question) based on their past interactions on platforms. Plenty of KT models have been proposed and have shown remarkable performance recently. However, the majority of these models use concepts to index questions, which means the predefined skill tags for each question are required in advance to indicate the KCs needed to answer that question correctly. This makes it pretty hard to apply on large-scale online education platforms where questions are often not well-organized by skill tags. In this paper, we propose Q-matrix-based Attentive Knowledge Tracing (QAKT), an end-to-end style model that is able to apply the attentive method to scenes where no predefined skill tags are available without sacrificing its performance. With a novel hybrid embedding method based on the q-matrix and Rasch model, QAKT is capable of modeling problems hierarchically and learning the q-matrix efficiently based on students' sequences. Meanwhile, the architecture of QAKT ensures that it is friendly to questions associated with multiple skills and has outstanding interpretability. After conducting experiments on a variety of open datasets, we empirically validated that our model shows similar or even better performance than state-of-the-art KT methods. Results of further experiments suggest that the q-matrix learned by QAKT is highly model-agnostic and more information-sufficient than the one labeled by human experts, which could help with the data mining tasks in existing ITSs., Comment: 11 pages, 4 figures, 5 tables