Miracle-dz/UniFAR-FTUs huggingface dataset:
CycleGANWM: A CycleGAN watermarking method for ownership verification

Due to the proliferation and widespread use of deep neural networks (DNN), their Intellectual Property Rights (IPR) protection has become increasingly important. This paper presents a novel model watermarking method for an unsupervised image-to-image translation (I2IT) networks, named CycleGAN, which leverage the image translation visual quality and watermark embedding. In this method, a watermark decoder is trained initially. Then the decoder is frozen and used to extract the watermark bits when training the CycleGAN watermarking model. The CycleGAN watermarking (CycleGANWM) is trained with specific loss functions and optimized to get a good performance on both I2IT task and watermark embedding. For watermark verification, this work uses statistical significance test to identify the ownership of the model from the extract watermark bits. We evaluate the robustness of the model against image post-processing and improve it by fine-tuning the model with adding data augmentation on the output images before extracting the watermark bits. We also carry out surrogate model attack under black-box access of the model. The experimental results prove that the proposed method is effective and robust to some image post-processing, and it is able to resist surrogate model attack.
Privileged Prior Information Distillation for Image Matting

Performance of trimap-free image matting methods is limited when trying to decouple the deterministic and undetermined regions, especially in the scenes where foregrounds are semantically ambiguous, chromaless, or high transmittance. In this paper, we propose a novel framework named Privileged Prior Information Distillation for Image Matting (PPID-IM) that can effectively transfer privileged prior environment-aware information to improve the performance of trimap-free students in solving hard foregrounds. The prior information of trimap regulates only the teacher model during the training stage, while not being fed into the student network during actual inference. To achieve effective privileged cross-modality (i.e. trimap and RGB) information distillation, we introduce a Cross-Level Semantic Distillation (CLSD) module that reinforces the students with more knowledgeable semantic representations and environment-aware information. We also propose an Attention-Guided Local Distillation module that efficiently transfers privileged local attributes from the trimap-based teacher to trimap-free students for the guidance of local-region optimization. Extensive experiments demonstrate the effectiveness and superiority of our PPID on image matting. The code will be released soon.
Asynchronous Event-Triggered Control for Non-Linear Systems

—With the increasing ubiquity of networked control systems, various strategies for sampling constituent subsystems’ outputs have emerged. In contrast with pe- riodic sampling, event-triggered control provides a way to efﬁciently sample a subsystem and conserve network resource usage, by triggering an update only when a state-dependent error threshold is satisﬁed. Herein we describe a scheme for asynchronous event-triggered control (ETC) of a nonlinear plant using sampler subsystems with hybrid dynamics. By exploiting inherent differences in the plant and controllers time scales, the proposed scheme permits independent sampling of plant and controller states. We extend existing ETC literature by adopting a more general representation of the sampler subsystem dynamics, thus accommodating different sampling schemes for both syn- chronous and asynchronous ETC applications. We present a numerical example in order to illustrate important operational considerations for the proposed scheme.
A Multivariate Non-Gaussian Bayesian Filter Using Power Moments

In this paper, we extend our results on the univariate non-Gaussian Bayesian filter using power moments to the multivariate systems, which can be either linear or nonlinear. Doing this introduces several challenging problems, for example a positive parametrization of the density surrogate, which is not only a problem of filter design, but also one of the multiple dimensional Hamburger moment problem. We propose a parametrization of the density surrogate with the proofs to its existence, Positivstellensatz and uniqueness. Based on it, we analyze the errors of moments of the density estimates by the proposed density surrogate. A discussion on continuous and discrete treatments to the non-Gaussian Bayesian filtering problem is proposed to motivate the research on continuous parametrization of the system state. Simulation results on estimating different types of multivariate density functions are given to validate our proposed filter. To the best of our knowledge, the proposed filter is the first one implementing the multivariate Bayesian filter with the system state parameterized as a continuous function, which only requires the true states being Lebesgue integrable.
Spacetime-emergent ring toward tabletop quantum gravity experiments

We propose a way to discover, in tabletop experiments, spacetime-emergent materials, that is, materials holographically dual to higher-dimensional quantum gravity systems under the AdS/CFT correspondence. The emergence of the holographic spacetime is verified by a mathematical imaging transform of the response function on the material. We consider theories on a 1-dimensional ring-shaped material, and compute the response to a scalar source locally put at a point on the ring. When the theory on the material has a gravity dual, the imaging in the low temperature phase exhibits a distinct difference from the ordinary materials: the spacetime-emergent material can look into the holographically emergent higher-dimensional curved spacetime and provides an image as if a wave had propagated there. Therefore the image is an experimental signature of the spacetime emergence. We also estimate temperature, ring size and source frequency usable in experiments, with an example of a quantum critical material, TlCuCl$_3$.
Semantic Communication Enabling Robust Edge Intelligence for Time-Critical IoT Applications

This paper aims to design robust Edge Intelligence using semantic communication for time-critical IoT applications. We systematically analyze the effect of image DCT coefficients on inference accuracy and propose the channel-agnostic effectiveness encoding for offloading by transmitting the most meaningful task data first. This scheme can well utilize all available communication resource and strike a balance between transmission latency and inference accuracy. Then, we design an effectiveness decoding by implementing a novel image augmentation process for convolutional neural network (CNN) training, through which an original CNN model is transformed into a Robust CNN model. We use the proposed training method to generate Robust MobileNet-v2 and Robust ResNet-50. The proposed Edge Intelligence framework consists of the proposed effectiveness encoding and effectiveness decoding. The experimental results show that the effectiveness decoding using the Robust CNN models perform consistently better under various image distortions caused by channel errors or limited communication resource. The proposed Edge Intelligence framework using semantic communication significantly outperforms the conventional approach under latency and data rate constraints, in particular, under ultra stringent deadlines and low data rate.
Giant and broadband THz and IR emission in drift-biased graphene-based hyperbolic nanostructures

We demonstrate that Cherenkov radiation can be manipulated in terms of operation frequency, bandwidth, and efficiency by simultaneously controlling the properties of drifting electrons and the photonic states supported by their surrounding media. We analytically show that the radiation rate strongly depends on the momentum of the excited photonic state, in terms of magnitude, frequency dispersion, and its variation vs the properties of the drifting carriers. This approach is applied to design and realize miniaturized, broadband, tunable, and efficient terahertz and far-infrared sources by manipulating and boosting the coupling between drifting electrons and engineered hyperbolic modes in graphene-based nanostructures. The broadband, dispersive, and confined nature of hyperbolic modes relax momentum matching issues, avoid using electron beams, and drastically enhance the radiation rate—allowing that over 90% of drifting electrons emit photons. Our findings open an exciting paradigm for the development of solid-state terahertz and infrared sources.
A new geometric constraint method of moving object detection using moving camera

We propose a three-view constraint for the motion object detection using moving camera. The proposed method classifies feature points in the video sequence into background or motion object by applying the epipolar constraint and a novel geometric constraint called the “Three-view Distance Constraint”. The three-view distance constraint, being the main contribution of this paper, is derived from the relative camera poses in three different views and implemented within the detection framework. Unlike the epipolar constraint, the three-view distance constraint modifies the surface degradation to the line degradation. The three-view distance constraint is capable of detecting moving objects followed by a moving camera in the same direction . We evaluate the proposed method with several video sequences to demonstrate the effectiveness and robustness of the three-view distance constraint.
Numerical Approximation of Gaussian Random Fields on Closed Surfaces

Abstract We consider the numerical approximation of Gaussian random fields on closed surfaces defined as the solution to a fractional stochastic partial differential equation (SPDE) with additive white noise. The SPDE involves two parameters controlling the smoothness and the correlation length of the Gaussian random field. The proposed numerical method relies on the Balakrishnan integral representation of the solution and does not require the approximation of eigenpairs. Rather, it consists of a sinc quadrature coupled with a standard surface finite element method. We provide a complete error analysis of the method and illustrate its performances in several numerical experiments.
Non-monotonic behavior of timescales of passage in heterogeneous media: Dependence on the nature of barriers

Usually time of passage across a region may be expected to increase with the number of barriers along the path. Can this intuition fail depending on the special nature of the barrier? We study experimentally the transport of a robotic bug which navigates through a spatially patterned array of obstacles. Depending on the nature of the obstacles we call them either entropic or energetic barriers. For energetic barriers we ﬁnd that the timescales of ﬁrst passage vary non-monotonically with the number of barriers, while for entropic barriers ﬁrst passage times increase monotonically. We perform an exact analytic calculation to derive closed form solutions for the mean ﬁrst passage time for diﬀerent theoretical models of diﬀusion. Our analytic results capture this counter-intuitive non-monotonic behaviour for energetic barriers. We also show non-monotonic eﬀective diﬀusivity in the case of energetic barriers. Finally, using numerical simulations, we show this non-monotonic behaviour for energetic barriers continues to hold true for super-diﬀusive transport. These results may be relevant for timescales of intra-cellular biological processes.
Design of Turing Systems with Physics-Informed Neural Networks

Reaction-diffusion (Turing) systems are fundamental to the formation of spatial patterns in nature and engineering. These systems are governed by a set of non-linear partial differential equations containing parameters that determine the rate of constituent diffusion and reaction. Critically, these parameters, such as diffusion coefficient, heavily influence the mode and type of the final pattern, and quantitative characterization and knowledge of these parameters can aid in bio-mimetic design or understanding of real-world systems. However, the use of numerical methods to infer these parameters can be difficult and computationally expensive. Typically, adjoint solvers may be used, but they are frequently unstable for very non-linear systems. Alternatively, massive amounts of iterative forward simulations are used to find the best match, but this is extremely effortful. Recently, physics-informed neural networks have been proposed as a means for data-driven discovery of partial differential equations, and have seen success in various applications. Thus, we investigate the use of physics-informed neural networks as a tool to infer key parameters in reaction─diffusion systems in the steady-state for scientific discovery or design. Our proof-of-concept results show that the method is able to infer parameters for different pattern modes and types with errors of less than 10%. In addition, the stochastic nature of this method can be exploited to provide multiple parameter alternatives to the desired pattern, highlighting the versatility of this method for bio-mimetic design. This work thus demonstrates the utility of physics-informed neural networks for inverse parameter inference of reaction-diffusion systems to enhance scientific discovery and design.
Learning 3D Scene Priors with 2D Supervision

Holistic 3D scene understanding entails estimation of both layout configuration and object geometry in a 3D environment. Recent works have shown advances in 3D scene estimation from various input modalities (e.g., images, 3D scans), by leveraging 3D supervision (e.g., 3D bounding boxes or CAD models), for which collection at scale is ex-pensive and often intractable. To address this shortcoming, we propose a new method to learn 3D scene priors of layout and shape without requiring any 3D ground truth. Instead, we rely on 2D supervision from multi-view RGB images. Our method represents a 3D scene as a latent vector, from which we can progressively decode to a sequence of objects characterized by their class categories, 3D bounding boxes, and meshes. With our trained autoregressive decoder representing the scene prior, our method facilitates many downstream applications, including scene synthesis, interpolation, and single-view reconstruction. Experiments on 3D-FRONT and ScanNet show that our method outperforms state of the art in single-view reconstruction, and achieves state-of-the-art results in scene synthesis against baselines which require for 3D supervision. Project page: https://yinyunie.github. io/sceneprior-page/
Fast Sampling of Diffusion Models via Operator Learning

Diffusion models have found widespread adoption in various areas. However, their sampling process is slow because it requires hundreds to thousands of network evaluations to emulate a continuous process defined by differential equations. In this work, we use neural operators, an efficient method to solve the probability flow differential equations, to accelerate the sampling process of diffusion models. Compared to other fast sampling methods that have a sequential nature, we are the first to propose a parallel decoding method that generates images with only one model forward pass. We propose diffusion model sampling with neural operator (DSNO) that maps the initial condition, i.e., Gaussian distribution, to the continuous-time solution trajectory of the reverse diffusion process. To model the temporal correlations along the trajectory, we introduce temporal convolution layers that are parameterized in the Fourier space into the given diffusion model backbone. We show our method achieves state-of-the-art FID of 3.78 for CIFAR-10 and 7.83 for ImageNet-64 in the one-model-evaluation setting.
Enhanced Tracking and Beamforming Codebook Design for Wideband Terahertz Massive MIMO System

True-time-delay (TTD) lines are recently applied inside Terahertz (THz) hybrid-precoding transceiver to acquire high beamforming gain against beam squint effect. However, beam tracking turns into a challenging puzzle where enormous potential beam directions bring about unacceptable overhead consumption. Frequency-scanning-based beam tracking is initially explored but still imperfect in previous studies. In this paper, based on TTD-aided hybrid precoding structure, we give an enhanced frequency-scanning-based tracking scheme. Multiple beams are generated and utilized simultaneously via several subcarriers for tracking at one timeslot. The squint beams' angular coverage at all subcarriers can be flexibly controlled by two different subcarrier-angular mapping policies, named forward-pairing and backward-pairing. Then multiple physical directions can be simultaneously searched in one timeslot for lower overhead consumption. Besides, closed-form searching radius bound, parameter configuration and interferences are theoretically analyzed. Furthermore, we provide the coupled codebook design for TTDs and phase shifters (PSs), with joint consideration of both beamforming and tracking. Analytical and numerical results demonstrate the superiority of the new frequency-scanning-based tracking scheme and beamforming codebook.
Channel Optimized Visual Imagery based Robotic Arm Control under the Online Environment

An electroencephalogram is an effective approach that provides a bidirectional pathway between the user and computer in a non-invasive way. In this study, we adopted the visual imagery data for controlling the BCI-based robotic arm. Visual imagery increases the power of the alpha frequency range of the visual cortex over time as the user performs the task. We proposed a deep learning architecture to decode the visual imagery data using only two channels and also we investigated the combination of two EEG channels that has significant classification performance. When using the proposed method, the highest classification performance using two channels in the offline experiment was 0.661. Also, the highest success rate in the online experiment using two channels (AF3–Oz) was 0.78. Our results provide the possibility of controlling the BCI-based robotic arm using visual imagery data.
Normalized ground states for a biharmonic Choquard equation with exponential critical growth

In this paper, we consider the normalized ground state solution for the following biharmonic Choquard type problem where β ≥ 0, c > 0, λ ∈ R , I µ = 1 | x | µ with µ ∈ (0 , 4), F ( u ) is the primitive function of f ( u ), and f is a continuous function with exponential critical growth in the sense of the Adams inequality. By using a minimax principle based on the homotopy stable family, we obtain that the above problem admits at least one ground state normalized solution.
MOSEL Survey: Extremely weak outflows in EoR analogues at z = 3 − 4

This paper presents deep K-band spectroscopic observations of galaxies at z = 3 − 4 with composite photometric rest-frame H β+[O iii] 5007 equivalent widths (EW0) > 600 Å, comparable to the EW of galaxies observed during the epoch of reionisation (EoR, z > 6). The typical spectroscopic [O iii] 5007 EW0 and stellar mass of our targets is ∼700 Å and log (M*/M⊙)=8.98. By stacking the [O iii] 5007 emission profiles, we find evidence of a weak broad component with $\rm F_{broad}/F_{narrow} \sim 0.2$ and velocity width σbroad ∼ 170 km s−1. The strength and velocity width of the broad component does not change significantly with stellar mass and [O iii] 5007 EW0 of the stacked sample. Assuming similar broad component profiles for [O iii] 5007 and Hα emission, we estimate a mass loading factor ∼0.2, similar to low stellar mass galaxies at z > 1 even if the star formation rates of our sample is 10 times higher. We hypothesize that either the multi-phase nature of supernovae driven outflows or the suppression of winds in the extreme star-forming regime is responsible for the weak signature of outflows in the EoR analogues.
Responsible Active Learning via Human-in-the-loop Peer Study

Active learning has been proposed to reduce data annotation efforts by only manually labelling representative data samples for training. Meanwhile, recent active learning applications have benefited a lot from cloud computing services with not only sufficient computational resources but also crowdsourcing frameworks that include many humans in the active learning loop. However, previous active learning methods that always require passing large-scale unlabelled data to cloud may potentially raise significant data privacy issues. To mitigate such a risk, we propose a responsible active learning method, namely Peer Study Learning (PSL), to simultaneously preserve data privacy and improve model stability. Specifically, we first introduce a human-in-the-loop teacher-student architecture to isolate unlabelled data from the task learner (teacher) on the cloud-side by maintaining an active learner (student) on the client-side. During training, the task learner instructs the light-weight active learner which then provides feedback on the active sampling criterion. To further enhance the active learner via large-scale unlabelled data, we introduce multiple peer students into the active learner which is trained by a novel learning paradigm, including the In-Class Peer Study on labelled data and the Out-of-Class Peer Study on unlabelled data. Lastly, we devise a discrepancy-based active sampling criterion, Peer Study Feedback, that exploits the variability of peer students to select the most informative data to improve model stability. Extensive experiments demonstrate the superiority of the proposed PSL over a wide range of active learning methods in both standard and sensitive protection settings.
Exposure and emergence in usage-based grammar: computational experiments in 35 languages

Abstract This paper uses computational experiments to explore the role of exposure in the emergence of construction grammars. While usage-based grammars are hypothesized to depend on a learner’s exposure to actual language use, the mechanisms of such exposure have only been studied in a few constructions in isolation. This paper experiments with (i) the growth rate of the constructicon, (ii) the convergence rate of grammars exposed to independent registers, and (iii) the rate at which constructions are forgotten when they have not been recently observed. These experiments show that the lexicon grows more quickly than the grammar and that the growth rate of the grammar is not dependent on the growth rate of the lexicon. At the same time, register-specific grammars converge onto more similar constructions as the amount of exposure increases. This means that the influence of specific registers becomes less important as exposure increases. Finally, the rate at which constructions are forgotten when they have not been recently observed mirrors the growth rate of the constructicon. This paper thus presents a computational model of usage-based grammar that includes both the emergence and the unentrenchment of constructions.