Miracle-dz/UniFAR-FTUs huggingface dataset:
A fast Fourier convolutional deep neural network for accurate and explainable discrimination of wheat yellow rust and nitrogen deficiency from Sentinel-2 time series data

Introduction Accurate and timely detection of plant stress is essential for yield protection, allowing better-targeted intervention strategies. Recent advances in remote sensing and deep learning have shown great potential for rapid non-invasive detection of plant stress in a fully automated and reproducible manner. However, the existing models always face several challenges: 1) computational inefficiency and the misclassifications between the different stresses with similar symptoms; and 2) the poor interpretability of the host-stress interaction. Methods In this work, we propose a novel fast Fourier Convolutional Neural Network (FFDNN) for accurate and explainable detection of two plant stresses with similar symptoms (i.e. Wheat Yellow Rust And Nitrogen Deficiency). Specifically, unlike the existing CNN models, the main components of the proposed model include: 1) a fast Fourier convolutional block, a newly fast Fourier transformation kernel as the basic perception unit, to substitute the traditional convolutional kernel to capture both local and global responses to plant stress in various time-scale and improve computing efficiency with reduced learning parameters in Fourier domain; 2) Capsule Feature Encoder to encapsulate the extracted features into a series of vector features to represent part-to-whole relationship with the hierarchical structure of the host-stress interactions of the specific stress. In addition, in order to alleviate over-fitting, a photochemical vegetation indices-based filter is placed as pre-processing operator to remove the non-photochemical noises from the input Sentinel-2 time series. Results and discussion The proposed model has been evaluated with ground truth data under both controlled and natural conditions. The results demonstrate that the high-level vector features interpret the influence of the host-stress interaction/response and the proposed model achieves competitive advantages in the detection and discrimination of yellow rust and nitrogen deficiency on Sentinel-2 time series in terms of classification accuracy, robustness, and generalization.
LIO-GVM: An Accurate, Tightly-Coupled Lidar-Inertial Odometry With Gaussian Voxel Map

This letter presents a probabilistic voxel-based LiDAR Inertial Odometry framework for accurate and robust pose estimation. The framework addresses the correspondence mismatching issue by representing the LiDAR points as a set of Gaussian distributions and evaluating the divergence in variance for outlier rejection. Based on the fitted distributions, a new residual metric is proposed for the filter-based Lidar inertial odometry by incorporating both the distance and variance disparities, further enriching the comprehensiveness and accuracy of the residual metric. With the strategic design of the residual, we propose a simple yet effective voxel-solely mapping scheme, which only requires the maintenance of one centroid and one covariance matrix for each voxel. Experiments on different datasets demonstrate the robustness and high accuracy of our framework for various data inputs and environments.
Locking On: Leveraging Dynamic Vehicle-Imposed Motion Constraints to Improve Visual Localization

Most 6-DoF localization and SLAM systems use static landmarks but ignore dynamic objects because they cannot be usefully incorporated into a typical pipeline. Where dynamic objects have been incorporated, typical approaches have attempted relatively sophisticated identification and localization of these objects, limiting their robustness or general utility. In this research, we propose a middle ground, demonstrated in the context of autonomous vehicles, using dynamic vehicles to provide limited pose constraint information in a 6-DoF frame-by-frame PnP-RANSAC localization pipeline. We refine initial pose estimates with a motion model and propose a method for calculating the predicted quality of future pose estimates, triggered by whether or not the autonomous vehicle's motion is constrained by the relative frame-to-frame location of dynamic vehicles in the environment. Our approach detects and identifies suitable dynamic vehicles to define these pose constraints to modify a pose filter, resulting in improved recall across a range of localization tolerances from 0.25m to 5m, compared to a state-of-the-art baseline single image PnP method and its vanilla pose filtering. Our constraint detection system is active for approximately 35% of the time on the Ford AV dataset and localization is particularly improved when the constraint detection is active.
Modeling and parametric optimization of 3D tendon-sheath actuator system for upper limb soft exosuit

This paper presents an analysis of parametric characterization of a motor driven tendon-sheath actuator system for use in upper limb augmentation for applications such as rehabilitation, therapy, and industrial automation. The double tendon sheath system, which uses two sets of cables (agonist and antagonist side) guided through a sheath, is considered to produce smooth and natural-looking movements of the arm. The exoskeleton is equipped with a single motor capable of controlling both the flexion and extension motions. One of the key challenges in the implementation of a double tendon sheath system is the possibility of slack in the tendon, which can impact the overall performance of the system. To address this issue, a robust mathematical model is developed and a comprehensive parametric study is carried out to determine the most effective strategies for overcoming the problem of slack and improving the transmission. The study suggests that incorporating a series spring into the system's tendon leads to a universally applicable design, eliminating the need for individual customization. The results also show that the slack in the tendon can be effectively controlled by changing the pretension, spring constant, and size and geometry of spool mounted on the axle of motor.
Why Deep Models Often cannot Beat Non-deep Counterparts on Molecular Property Prediction?

Molecular property prediction (MPP) is a crucial task in the drug discovery pipeline, which has recently gained considerable attention thanks to advances in deep neural networks. However, recent research has revealed that deep models struggle to beat traditional non-deep ones on MPP. In this study, we benchmark 12 representative models (3 non-deep models and 9 deep models) on 14 molecule datasets. Through the most comprehensive study to date, we make the following key observations: \textbf{(\romannumeral 1)} Deep models are generally unable to outperform non-deep ones; \textbf{(\romannumeral 2)} The failure of deep models on MPP cannot be solely attributed to the small size of molecular datasets. What matters is the irregular molecule data pattern; \textbf{(\romannumeral 3)} In particular, tree models using molecular fingerprints as inputs tend to perform better than other competitors. Furthermore, we conduct extensive empirical investigations into the unique patterns of molecule data and inductive biases of various models underlying these phenomena.
First-order ANIL learns linear representations despite misspecified latent dimension

Due to its empirical success in few-shot classification and reinforcement learning, meta-learning has recently received significant interest. Meta-learning methods leverage data from previous tasks to learn a new task in a sample-efficient manner. In particular, model-agnostic methods look for initialisation points from which gradient descent quickly adapts to any new task. Although it has been empirically suggested that such methods perform well by learning shared representations during pretraining, there is limited theoretical evidence of such behavior. More importantly, it has not been rigorously shown that these methods still learn a shared structure, despite architectural misspecifications. In this direction, this work shows, in the limit of an infinite number of tasks, that first-order ANIL with a linear two-layer network architecture successfully learns linear shared representations. This result even holds with a misspecified network parameterisation; having a width larger than the dimension of the shared representations results in an asymptotically low-rank solution. The learnt solution then yields a good adaptation performance on any new task after a single gradient step. Overall this illustrates how well model-agnostic methods such as first-order ANIL can learn shared representations.
I / O Bottleneck Investigation in Deep Learning Systems

As deep learning systems continue to grow in importance, several researchers have been analyzing approaches to make such systems efficient and scalable on high-performance computing platforms. As computational parallelism increases, however, data I/O becomes the major bottleneck limiting the overall system scalability. In this paper, we present a detailed analysis of the performance bottlenecks of Caffe on large supercomputing systems and propose an optimized I/O plugin, namely LMDBIO. Throughout the paper, we present six sophisticated data I/O techniques that address five major problems in state of the art I/O subsystem. Our experimental results show that Caffe/LMDBIO can improve the overall execution time of Caffe/LMDB by 77-fold compared with LMDB.
QuAVF: Quality-aware Audio-Visual Fusion for Ego4D Talking to Me Challenge

This technical report describes our QuAVF@NTU-NVIDIA submission to the Ego4D Talking to Me (TTM) Challenge 2023. Based on the observation from the TTM task and the provided dataset, we propose to use two separate models to process the input videos and audio. By doing so, we can utilize all the labeled training data, including those without bounding box labels. Furthermore, we leverage the face quality score from a facial landmark prediction model for filtering noisy face input data. The face quality score is also employed in our proposed quality-aware fusion for integrating the results from two branches. With the simple architecture design, our model achieves 67.4% mean average precision (mAP) on the test set, which ranks first on the leaderboard and outperforms the baseline method by a large margin. Code is available at: https://github.com/hsi-che-lin/Ego4D-QuAVF-TTM-CVPR23
ORIGINAL Lower Extremities' Joint Stability during the Elderly Woman's Walking

Objective: The aim of this study was to investigate the periodicity of the lower extremity joint flexion/ extension angle to compare the local stability between young and elderly women during walking on a treadmill. Method: Eighteen young women (mean age=21.2 ± 1.6 y; mean mass=57.1 ± 6.1 kg; mean height=1.61 ± 0.04 m) and 18 elderly women (mean age=66.4 ± 1.2 y; mean mass=55.4 ± 8.3 kg; mean height=1.56 ± 0.04 m) participated in this study. Approximate entropy (ApEn) was used to determine the periodicity in the lower limb joint angles. Results: The ApEn values of the two groups were statistically greater in the surrogate data test than in the original time series data ( p <.05). The periodicity of the hip and ankle flexion/extension angles decreased in the elderly women group compared with the young women group ( p <.05). The periodicity of the lower extremity joint flexion/extension angle showed that the ankle joint increased dominatingly in both groups ( p <.05); the hip joint decreased compared with the knee joint in the young women group; and the knee joint decreased compared with the hip joint in the elderly women group ( p <.05). Conclusion: These results suggest that the lower extremity joint flexion/extension angles of the young and elderly women during walking contained random noises as well as biological signals. In addition, the differences in the periodicity in the lower extremity joint between the young and elderly women may provide some insight in predicting potential falls and be used as a characteristic indicator for determining local stability in elderly women during walking.
Comparing spin supplementary conditions for particle motion around traversable wormholes

The Mathisson-Papapetrou-Dixon (MPD) equations describe the motion of spinning test particles. It is well-known that these equations, which couple the Riemann curvature tensor with the antisymmetric spin tensor S, together with the normalization condition for the four-velocity, is a system of eleven equations relating fourteen unknowns. To ``close'' the system, it is necessary to introduce a constraint of the form V_\mu S^{\mu \nu} = 0, usually known as the spin supplementary condition (SSC), where V_\mu is a future-oriented reference vector satisfying the normalization condition V_\alpha V^\alpha = -1. There are several SSCs in the literature. In particular, the Tulzcyjew-Dixon, Mathisson-Pirani, and Ohashi-Kyrian-Semer\'ak are the most used by the community. From the physical point of view, choosing a different SSC (a different reference vector $V^\mu$) is equivalent to fixing the centroid of the test particle. In this manuscript, we compare different SSCs for spinning test particles moving around a Morris-Thorne traversable wormhole. To do so, we first obtain the orbital frequency and expand it up to third-order in the particle's spin; as expected, the zero-order coincides with the Keplerian frequency, the same in all SSCs; nevertheless, we found that differences appear in the second order of the expansion, similar to the Schwarzschild and Kerr black holes. We also compare the behavior of the innermost stable circular orbit (ISCO). Since each SSC is associated with a different centroid of the test particle, we analyze (separately) the radial and spin corrections for each SSC. We found that the radial corrections improve the convergence, especially between Tulzcyjew-Dixon and Mathisson-Pirani SSCs. In the case of Ohashi-Kyrian-Semer\'ak, we found that the spin corrections remove the divergence for the ISCO and extend its existence for higher values of the particle's spin.
Polarimetric iToF: Measuring High-Fidelity Depth Through Scattering Media

Indirect time-of-flight (iToF) imaging allows us to capture dense depth information at a low cost. However, iToF imaging often suffers from multipath interference (MPI) artifacts in the presence of scattering media, resulting in severe depth-accuracy degradation. For instance, iToF cameras cannot measure depth accurately through fog because ToF active illumination scatters back to the sensor before reaching the farther target surface. In this work, we propose a polarimetric iToF imaging method that can capture depth information robustly through scattering media. Our observations on the principle of indirect ToF imaging and polarization of light allow us to formulate a novel computational model of scattering-aware polarimetric phase measurements that enables us to correct MPI errors. We first devise a scattering-aware polarimetric iToF model that can estimate the phase of unpolarized backscattered light. We then combine the optical filtering of polarization and our computational modeling of unpolarized backscattered light via scattering analysis of phase and amplitude. This allows us to tackle the MPI problem by estimating the scattering energy through the participating media. We validate our method on an experimental setup using a customized off-the-shelf iToF camera. Our method outperforms baseline methods by a significant margin by means of our scattering model and polarimetric phase measurements.
Scattering spectra models for physics

Abstract Physicists routinely need probabilistic models for a number of tasks such as parameter inference or the generation of new realizations of a field. Establishing such models for highly non-Gaussian fields is a challenge, especially when the number of samples is limited. In this paper, we introduce scattering spectra models for stationary fields and we show that they provide accurate and robust statistical descriptions of a wide range of fields encountered in physics. These models are based on covariances of scattering coefficients, i.e. wavelet decomposition of a field coupled with a pointwise modulus. After introducing useful dimension reductions taking advantage of the regularity of a field under rotation and scaling, we validate these models on various multiscale physical fields and demonstrate that they reproduce standard statistics, including spatial moments up to fourth order. The scattering spectra provide us with a low-dimensional structured representation that captures key properties encountered in a wide range of physical fields. These generic models can be used for data exploration, classification, parameter inference, symmetry detection, and component separation.
Incorporating Auxiliary Variables to Improve the Efficiency of Time-Varying Treatment Effect Estimation

The use of smart devices (e.g., smartphones, smartwatches) and other wearables for context sensing and delivery of digital interventions to improve health outcomes has grown significantly in behavioral and psychiatric studies. Micro-randomized trials (MRTs) are a common experimental design for obtaining data-driven evidence on mobile health (mHealth) intervention effectiveness where each individual is repeatedly randomized to receive treatments over numerous time points. Individual characteristics and the contexts around randomizations are also collected throughout the study, some may be pre-specified as moderators when assessing time-varying causal effect moderation. Moreover, we have access to abundant measurements beyond just the moderators. Our study aims to leverage this auxiliary information to improve causal estimation and better understand the intervention effect. Similar problems have been raised in randomized control trials (RCTs), where extensive literature demonstrates that baseline covariate information can be incorporated to alleviate chance imbalances and increase asymptotic efficiency. However, covariate adjustment in the context of time-varying treatments and repeated measurements, as seen in MRTs, has not been studied. Recognizing the connection to Neyman Orthogonality, we address this gap by introducing an intuitive approach to incorporate auxiliary variables to improve the efficiency of moderated causal excursion effect estimation. The efficiency gain of our approach is proved theoretically and demonstrated through simulation studies and an analysis of data from the Intern Health Study (NeCamp et al., 2020).
Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors

We present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture. In both stages, the 3D content is learned through reference view supervision and novel views guided by a combination of 2D and 3D diffusion priors. We introduce a single trade-off parameter between the 2D and 3D priors to control exploration (more imaginative) and exploitation (more precise) of the generated geometry. Additionally, we employ textual inversion and monocular depth regularization to encourage consistent appearances across views and to prevent degenerate solutions, respectively. Magic123 demonstrates a significant improvement over previous image-to-3D techniques, as validated through extensive experiments on synthetic benchmarks and diverse real-world images. Our code, models, and generated 3D assets are available at https://github.com/guochengqian/Magic123.
Stretching Behavior of Knotted and Unknotted Flow Fields

Vortex stretching is a common feature of many complex flows, including turbulence. Experiments and simulations of isolated vortex knots demonstrate that this behavior can also be seen in relatively simple systems, and appears to be dependent on vortex topology. Here we simulate the advection of material lines in the frozen flow fields of vortices on the surface of a torus. We find that knotted configurations lead to exponential stretching behavior which is qualitatively different than that observed by collections of unknots. This stretching can be explained by the formation of bights, sharp bends in the material lines which can be used to predict the stretching rate. This behavior is confirmed by computing the finite time Lyapunov exponents of the flow fields, which demonstrate the exponential stretching is mediated by bight forming regions between the vortex lines. This work both establishes a clear connection between topology and stretching behavior, as well as providing an intuitive mechanism for exponential growth of material lines in knotted flows.
Adaptive sieving: A dimension reduction technique for sparse optimization problems

In this paper, we propose an adaptive sieving (AS) strategy for solving general sparse machine learning models by effectively exploring the intrinsic sparsity of the solutions, wherein only a sequence of reduced problems with much smaller sizes need to be solved. We further apply the proposed AS strategy to generate solution paths for large-scale sparse optimization problems efficiently. We establish the theoretical guarantees for the proposed AS strategy including its finite termination property. Extensive numerical experiments are presented in this paper to demonstrate the effectiveness and flexibility of the AS strategy to solve large-scale machine learning models.
Hierarchical Bayesian Regression for Multi-Location Sales Transaction Forecasting

The features in many prediction models naturally take the form of a hierarchy. The lower levels represent individuals or events. These units group naturally into locations and intervals or other aggregates, often at multiple levels. Levels of groupings may intersect and join, much as relational database tables do. Besides representing the structure of the data, predictive features in hierarchical models can be assigned to their proper levels. Such models lend themselves to hierarchical Bayes solution methods that ``share'' results of inference between groups by generalizing over the case of individual models for each group versus one model that aggregates all groups into one. In this paper we show our work-in-progress applying a hierarchical Bayesian model to forecast purchases throughout the day at store franchises, with groupings over locations and days of the week. We demonstrate using the \textsf{stan} package on individual sales transaction data collected over the course of a year. We show how this solves the dilemma of having limited data and hence modest accuracy for each day and location, while being able to scale to a large number of locations with improved accuracy.