Miracle-dz/UniFAR-FTUs huggingface dataset:
Advancing Early Detection of Virus Yellows: Developing a Hybrid Convolutional Neural Network for Automatic Aphid Counting in Sugar Beet Fields

Aphids are efficient vectors to transmit virus yellows in sugar beet fields. Timely monitoring and control of their populations are thus critical to prevent the large-scale outbreak of virus yellows. However, the manual counting of aphids, which is the most common practice, is labor-intensive and time-consuming. Additionally, two of the biggest challenges in aphid counting are that aphids are small objects and their density distributions are varied in different areas of the field. To address these challenges, we proposed a hybrid automatic aphid counting network architecture which integrates the detection network and the density map estimation network. When the distribution density of aphids is low, it utilizes an improved Yolov5 to count aphids. Conversely, when the distribution density of aphids is high, its witches to CSRNet to count aphids. To the best of our knowledge, this is the first framework integrating the detection network and the density map estimation network for counting tasks. Through comparison experiments of counting aphids, it verified that our proposed approach outperforms all other methods in counting aphids. It achieved the lowest MAE and RMSE values for both the standard and high-density aphid datasets: 2.93 and 4.01 (standard), and 34.19 and 38.66 (high-density), respectively. Moreover, the AP of the improved Yolov5 is 5% higher than that of the original Yolov5. Especially for extremely small aphids and densely distributed aphids, the detection performance of the improved Yolov5 is significantly better than the original Yolov5. This work provides an effective early warning for the virus yellows risk caused by aphids in sugar beet fields, offering protection for sugar beet growth and ensuring sugar beet yield. The datasets and project code are released at: https://github.com/JunfengGaolab/Counting-Aphids.
Cluster States and $\pi$-Transition in the Kuramoto Model with Higher Order Interactions

We have examined the synchronization and de-synchronization transitions observable in the Kuramoto model with a standard pair-wise first harmonic interaction plus a higher order (triadic) symmetric interaction for unimodal and bimodal Gaussian distributions of the natural frequencies $\{ \omega_i \}$. These transitions have been accurately characterized thanks to a self-consistent mean-field approach joined with extensive numerical simulations. The higher-order interactions favour the formation of two cluster states, which emerge from the incoherent regime via continuous (discontinouos) transitions for unimodal (bimodal) distributions. Fully synchronized initial states give rise to two symmetric equally populated bimodal clusters, each characterized by either positive or negative natural frequencies. These bimodal clusters are formed at an angular distance $\gamma$, which increases for decreasing pair-wise couplings until it reaches $\gamma=\pi$ (corresponding to an anti-phase configuration), where the cluster state destabilizes via an abrupt transition: the $\pi$-transition. The uniform clusters that reform immediately after (with a smaller angle $\gamma$) are composed by oscillators with positive and negative $\{ \omega_i \}$. For bimodal distributions we have obtained detailed phase diagrams involving all the possible dynamical states in terms of standard and novel order parameters. In particular, the clustering order parameter, here introduced, appears quite suitable to characterize the two cluster regime. As a general aspect, hysteretic (non hysteretic) synchronization transitions, mostly mediated by the emergence of standing waves, are observable for attractive (repulsive) higher-order interactions.
TriDo-Former: A Triple-Domain Transformer for Direct PET Reconstruction from Low-Dose Sinograms

To obtain high-quality positron emission tomography (PET) images while minimizing radiation exposure, various methods have been proposed for reconstructing standard-dose PET (SPET) images from low-dose PET (LPET) sinograms directly. However, current methods often neglect boundaries during sinogram-to-image reconstruction, resulting in high-frequency distortion in the frequency domain and diminished or fuzzy edges in the reconstructed images. Furthermore, the convolutional architectures, which are commonly used, lack the ability to model long-range non-local interactions, potentially leading to inaccurate representations of global structures. To alleviate these problems, we propose a transformer-based model that unites triple domains of sinogram, image, and frequency for direct PET reconstruction, namely TriDo-Former. Specifically, the TriDo-Former consists of two cascaded networks, i.e., a sinogram enhancement transformer (SE-Former) for denoising the input LPET sinograms and a spatial-spectral reconstruction transformer (SSR-Former) for reconstructing SPET images from the denoised sinograms. Different from the vanilla transformer that splits an image into 2D patches, based specifically on the PET imaging mechanism, our SE-Former divides the sinogram into 1D projection view angles to maintain its inner-structure while denoising, preventing the noise in the sinogram from prorogating into the image domain. Moreover, to mitigate high-frequency distortion and improve reconstruction details, we integrate global frequency parsers (GFPs) into SSR-Former. The GFP serves as a learnable frequency filter that globally adjusts the frequency components in the frequency domain, enforcing the network to restore high-frequency details resembling real SPET images. Validations on a clinical dataset demonstrate that our TriDo-Former outperforms the state-of-the-art methods qualitatively and quantitatively.
UQSA -- An R-Package for Uncertainty Quantification and Sensitivity Analysis for Biochemical Reaction Network Models

We present an R-package developed for modeling of biochemical reaction networks, uncertainty quantification (UQ) and sensitivity analysis (SA). Estimating parameters and quantifying their uncertainty (and resulting prediction uncertainty), is required for data-driven systems biology modeling. Sampling methods need to be efficient when confronted with high-dimensional, correlated parameter distributions. We have developed the UQSA package to be fast for this problem class and work well with other tools for modelling. We aim for simplicity, and part of that is our use of the SBtab format for the unified storage of model and data. Our tool-set is modular enough, that parts can be replaced. We use intermediate formats that are not hidden from the user to make this feasible. UQ is performed through Markov chain Monte Carlo (MCMC) sampling in an Approximate Bayesian Computation (ABC) setting. This can be followed by a variance-decomposition based global sensitivity analysis. If needed, complex parameter distributions can be described, evaluated, and sampled from, with the help of Vine-copulas that are available in R. This approach is especially useful when new experimental data become available, and a previously calibrated model needs to be updated. Implementation: R is a high-level language and allows the use of sophisticated statistical methods. The ode solver we used is written in C (gsl_odeiv2, interface to R is ours). We use the SBtab tabular format for the model description, as well as the data and an event system to be able to model inputs frequently encountered in systems biology and neuroscience. The code has been tested on one node with 256 cores of a computing cluster, but smaller examples are included in the repository that can be run on a laptop. Source code: https://github.com/icpm-kth/uqsa
Universal conductivity at a two-dimensional superconductor-insulator transition: The effects of quenched disorder and Coulomb interaction

We calculate the zero-temperature universal electrical conductivity at a superconductor-insulator transition in two spatial dimensions. We focus on transitions in the universality class of the dirty 3d XY model. We use a dual model consisting of a single Dirac fermion at zero density coupled to a Chern-Simons gauge field in the presence of a quenched random mass, with or without an unscreened Coulomb interaction. Our calculation is performed in a $1/N_f$ expansion, where $N_f$ is the number of Dirac fermions. At zeroth order, the model exhibits particle-vortex self-dual electrical transport with $\sigma_{xx} \lesssim (2e)^2/h$ and small, but finite $\sigma_{xy}$. Corrections of $\mathcal{O}(1/N_f)$ due to fluctuations in the Chern-Simons gauge field and disorder produce violations of self-duality. We find these violations to be milder when the Coulomb interaction is present.
A Homomorphic Encryption Framework for Privacy-Preserving Spiking Neural Networks

Machine learning (ML) is widely used today, especially through deep neural networks (DNNs); however, increasing computational load and resource requirements have led to cloud-based solutions. To address this problem, a new generation of networks has emerged called spiking neural networks (SNNs), which mimic the behavior of the human brain to improve efficiency and reduce energy consumption. These networks often process large amounts of sensitive information, such as confidential data, and thus privacy issues arise. Homomorphic encryption (HE) offers a solution, allowing calculations to be performed on encrypted data without decrypting them. This research compares traditional DNNs and SNNs using the Brakerski/Fan-Vercauteren (BFV) encryption scheme. The LeNet-5 and AlexNet models, widely-used convolutional architectures, are used for both DNN and SNN models based on their respective architectures, and the networks are trained and compared using the FashionMNIST dataset. The results show that SNNs using HE achieve up to 40% higher accuracy than DNNs for low values of the plaintext modulus t, although their execution time is longer due to their time-coding nature with multiple time steps.
Surface recombination and out of plane diffusivity of free excitons in hexagonal boron nitride

We present a novel experimental protocol using Cathodoluminescence measurements as a function of the electron incident energy to study both exciton diffusion in a directional way and surface exciton recombination. Our approach overcomes the challenges of anisotropic diffusion and the limited applicability of existing methods to the bulk counterparts of 2D materials. The protocol is then applied at room and at cryogenic temperatures to four bulk hexagonal boron nitride crystals grown by different synthesis routes. The exciton diffusivity depends on the sample quality but not on the temperature, indicating it is limited by defect scattering even in the best quality crystals. The lower limit for the diffusivity by phonon scattering is 0.2 cm$^{2}$. s$^{-1}$. Diffusion lengths were as much as 570 nm. Finally, the surface recombination velocity exceeds 10$^{5}$ cm$^{2}$. s$^{-1}$, at a level similar to silicon or diamond. This result reveals that surface recombination could strongly limit light-emitting devices based on 2D materials.
AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining

Although audio generation shares commonalities across different types of audio, such as speech, music, and sound effects, designing models for each type requires careful consideration of specific objectives and biases that can significantly differ from those of other types. To bring us closer to a unified perspective of audio generation, this paper proposes a framework that utilizes the same learning method for speech, music, and sound effect generation. Our framework introduces a general representation of audio, called"language of audio"(LOA). Any audio can be translated into LOA based on AudioMAE, a self-supervised pre-trained representation learning model. In the generation process, we translate any modalities into LOA by using a GPT-2 model, and we perform self-supervised audio generation learning with a latent diffusion model conditioned on LOA. The proposed framework naturally brings advantages such as in-context learning abilities and reusable self-supervised pretrained AudioMAE and latent diffusion models. Experiments on the major benchmarks of text-to-audio, text-to-music, and text-to-speech demonstrate state-of-the-art or competitive performance against previous approaches. Our code, pretrained model, and demo are available at https://audioldm.github. io/audioldm2.
Data-driven Intra-Autonomous Systems Graph Generator

Accurate modeling of realistic network topologies is essential for evaluating novel Internet solutions. Current topology generators, notably scale-free-based models, fail to capture multiple properties of intra-AS topologies. While scale-free networks encode node-degree distribution, they overlook crucial graph properties like betweenness, clustering, and assortativity. The limitations of existing generators pose challenges for training and evaluating deep learning models in communication networks, emphasizing the need for advanced topology generators encompassing diverse Internet topology characteristics. This paper introduces a novel deep-learning-based generator of synthetic graphs representing intra-autonomous in the Internet, named Deep-Generative Graphs for the Internet (DGGI). It also presents a novel massive dataset of real intra-AS graphs extracted from the project ITDK, called IGraphs. It is shown that DGGI creates synthetic graphs that accurately reproduce the properties of centrality, clustering, assortativity, and node degree. The DGGI generator overperforms existing Internet topology generators. On average, DGGI improves the MMD metric $84.4\%$, $95.1\%$, $97.9\%$, and $94.7\%$ for assortativity, betweenness, clustering, and node degree, respectively.
Doppler-Enhanced Quantum Magnetometry with thermal Rydberg atoms

We report experimental measurements showing how one can combine quantum interference and thermal Doppler shifts at room temperature to detect weak magnetic fields. We pump ${}^{87}$Rb atoms to a highly-excited, Rydberg level using a probe and a coupling laser, leading to narrow transmission peaks of the probe due to destructive interference of transition amplitudes, known as Electromagnetically Induced Transparency (EIT). While it is customary in such setups to use counterpropagating lasers to minimize the effect of Doppler shifts, here we show, on the contrary, that one can harness Doppler shifts in a copropagating arrangement to produce an enhanced response to a magnetic field. In particular, we demonstrate an order-of-magnitude bigger splitting in the transmission spectrum as compared to the counterpropagating case. We explain and generalize our findings with theoretical modelling and simulations based on a Lindblad master equation. Our results pave the way to using quantum effects for magnetometry in readily deployable room-temperature platforms.
A Generalized Model for Predicting the Drag Coefficient of Arbitrary Bluff Shaped Bodies at High Reynolds Numbers

We propose an accurate model for the drag coefficient of arbitrary bluff bodies that is valid for high Reynolds numbers ($Re$). The model is based on the drag coefficient model derived for the case of a sphere:, $C_D = a_1 +{\frac{K a_2}{Re}} +{a_3\log(Re)+ a_4\log^2(Re) + a_5\log^4(Re)}$ (El Hasadi and Padding, Chemical Engineering Science, Vol. 265, 2023). The coefficients $a_2$, $a_3$, $a_4$, and $a_5$ do not depend on the object's shape or its orientation with respect to the flow, and $K$ is the Stokes drag correction factor, which for the case of the sphere, is equal to 1.0. The shape and orientation effects are included in the value of $a_1$ for the high Reynolds number flow regime. Interestingly, we found a strong correlation between the value of the $a_1$ coefficient and the frictional drag derived from boundary layer theory. One of the main findings of this investigation is that the rate of change of the drag coefficient with respect to the Reynolds number in the inertial flow regime is independent of the shape of the body or its orientation. Our model successfully predicts, with acceptable accuracy, the historical data of Wieselsberger (Technical Report, 1922) for the case of an infinite cylinder. Additionally, the model predicts the drag coefficient of other bluff body geometries such as oblate and prolate spheroids, spherocylinders, cubes, normal flat plates and irregular non-spherical particles.
Topological classification of tumour-immune interactions and dynamics

The complex and dynamic crosstalk between tumour and immune cells results in tumours that can exhibit distinct qualitative behaviours - elimination, equilibrium, and escape - and intricate spatial patterns, yet share similar cell configurations in the early stages. We offer a topological approach to analyse time series of spatial data of cell locations (including tumour cells and macrophages) in order to predict malignant behaviour. We propose four topological vectorisations specialised to such cell data: persistence images of Vietoris-Rips and radial filtrations at static time points, and persistence images for zigzag filtrations and persistence vineyards varying in time. To demonstrate the approach, synthetic data are generated from an agent-based model with varying parameters. We compare the performance of topological summaries in predicting - with logistic regression at various time steps - whether tumour niches surrounding blood vessels are present at the end of the simulation, as a proxy for metastasis (i.e., tumour escape). We find that both static and time-dependent methods accurately identify perivascular niche formation, significantly earlier than simpler markers such as the number of tumour cells and the macrophage phenotype ratio. We find additionally that dimension 0 persistence applied to macrophage data, representing multi-scale clusters of the spatial arrangement of macrophages, performs best at this classification task at early time steps, prior to full tumour development, and performs even better when time-dependent data are included; in contrast, topological measures capturing the shape of the tumour, such as tortuosity and punctures in the cell arrangement, perform best at intermediate and later stages. The logistic regression coefficients reveal detailed shape differences between the classes.
Conditional Independence Testing for Discrete Distributions: Beyond $\chi^2$- and $G$-tests

This paper is concerned with the problem of conditional independence testing for discrete data. In recent years, researchers have shed new light on this fundamental problem, emphasizing finite-sample optimality. The non-asymptotic viewpoint adapted in these works has led to novel conditional independence tests that enjoy certain optimality under various regimes. Despite their attractive theoretical properties, the considered tests are not necessarily practical, relying on a Poissonization trick and unspecified constants in their critical values. In this work, we attempt to bridge the gap between theory and practice by reproving optimality without Poissonization and calibrating tests using Monte Carlo permutations. Along the way, we also prove that classical asymptotic $\chi^2$- and $G$-tests are notably sub-optimal in a high-dimensional regime, which justifies the demand for new tools. Our theoretical results are complemented by experiments on both simulated and real-world datasets. Accompanying this paper is an R package UCI that implements the proposed tests.
Density-Based Topology Optimization of High-Fidelity Fluid-Structure Interaction Problems with Large Deformations

The application of modern topology optimization techniques to single physics systems has seen great advances in the last three decades. However, the application of these tools to sophisticated multiphysics systems such as fluid-structure interactions is still lagging behind, mainly due to the multidisciplinary and complex nature of such systems. In this work, we implement topology optimization of high-fidelity, fully-coupled fluid-structure interaction problems with large deformations. We use the arbitrary Lagrangian-Eulerian approach to deform the fluid mesh as a pseudo-structural system such that structural deformations are completely reflected in the fluid flow mesh. The fluid-structure interaction problem is formulated using the three-field formulation and the sensitivity analysis is derived using the discrete adjoint approach. We show through numerical examples the effect of the projection and interpolation parameters on the convergence and topology of the optimized designs and demonstrate the effect of considering the structural deformations in the fluid mesh.
Bayesian Meta-Learning on Control Barrier Functions with Data from On-Board Sensors

In this paper, we consider a way to safely navigate the robots in unknown environments using measurement data from sensory devices. The control barrier function (CBF) is one of the promising approaches to encode safety requirements of the system and the recent progress on learning-based approaches for CBF realizes online synthesis of CBF-based safe controllers with sensor measurements. However, the existing methods are inefficient in the sense that the trained CBF cannot be generalized to different environments and the re-synthesis of the controller is necessary when changes in the environment occur. Thus, this paper considers a way to learn CBF that can quickly adapt to a new environment with few amount of data by utilizing the currently developed Bayesian meta-learning framework. The proposed scheme realizes efficient online synthesis of the controller as shown in the simulation study and provides probabilistic safety guarantees on the resulting controller.
Mono-hydra: Real-time 3D scene graph construction from monocular camera input with IMU

Abstract. The ability of robots to autonomously navigate through 3D environments depends on their comprehension of spatial concepts, ranging from low-level geometry to high-level semantics, such as objects, places, and buildings. To enable such comprehension, 3D scene graphs have emerged as a robust tool for representing the environment as a layered graph of concepts and their relationships. However, building these representations using monocular vision systems in real-time remains a difficult task that has not been explored in depth.This paper puts forth a real-time spatial perception system Mono-Hydra, combining a monocular camera and an IMU sensor setup, focusing on indoor scenarios. However, the proposed approach is adaptable to outdoor applications, offering flexibility in its potential uses. The system employs a suite of deep learning algorithms to derive depth and semantics. It uses a robocentric visual-inertial odometry (VIO) algorithm based on square-root information, thereby ensuring consistent visual odometry with an IMU and a monocular camera. This system achieves sub-20 cm error in real-time processing at 15 fps, enabling real-time 3D scene graph construction using a laptop GPU (NVIDIA 3080). This enhances decision-making efficiency and effectiveness in simple camera setups, augmenting robotic system agility. We make Mono-Hydra publicly available at: https://github.com/UAV-Centre-ITC/Mono_Hydra.